From d29711bf216af6e5cc68cdc712d67c45b35a290c Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Wed, 14 Sep 2022 14:03:13 +0200 Subject: [PATCH 01/20] working on nn basics --- my_code/1_value_gradient_backprop.ipynb | 686 ++++++++++++++++++++++++ 1 file changed, 686 insertions(+) create mode 100644 my_code/1_value_gradient_backprop.ipynb diff --git a/my_code/1_value_gradient_backprop.ipynb b/my_code/1_value_gradient_backprop.ipynb new file mode 100644 index 00000000..ff15df4a --- /dev/null +++ b/my_code/1_value_gradient_backprop.ipynb @@ -0,0 +1,686 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " return 3*x**2 - 4*x + 5" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f(3.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.arange(-5, 5, 0.25)\n", + "ys = f(xs)\n", + "plt.plot(xs, ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.00000009255109" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now the derivative at a particular point\n", + "# from the definition of derivate\n", + "h = 0.00000001 # lim h->0\n", + "x = 3\n", + "\n", + "f(x+h) # it should be greater than f(x)\n", + "\n", + "# Now Slope\n", + "\n", + "slope = (f(x+h) - f(x)) / h\n", + "slope\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-22.00000039920269" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.00000001 # lim h->0\n", + "x = -3\n", + "\n", + "f(x+h) # it should be greater than f(x)\n", + "\n", + "# Now Slope\n", + "\n", + "slope = (f(x+h) - f(x)) / h\n", + "slope # here it will be negative" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.00000001 # lim h->0\n", + "x = 2/3\n", + "\n", + "f(x+h) # it should be greater than f(x)\n", + "\n", + "# Now Slope\n", + "\n", + "slope = (f(x+h) - f(x)) / h\n", + "slope # here it will be zero" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.0\n" + ] + } + ], + "source": [ + "# Now more complex function\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d = a*b + c\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d1 4.0\n", + "d2 3.999699999999999\n", + "slope -3.000000000010772\n" + ] + } + ], + "source": [ + "h = 0.0001\n", + "\n", + "# inputs\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d1 = a*b +c\n", + "\n", + "a += h # increment a by h\n", + "d2 = a*b + c\n", + "\n", + "print(\"d1\", d1)\n", + "print(\"d2\", d2)\n", + "print(\"slope\", (d2-d1)/h)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d1 4.0\n", + "d2 4.0002\n", + "slope 2.0000000000042206\n" + ] + } + ], + "source": [ + "h = 0.0001\n", + "\n", + "# inputs\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d1 = a*b +c\n", + "\n", + "b += h # increment b by h\n", + "d2 = a*b + c\n", + "\n", + "print(\"d1\", d1)\n", + "print(\"d2\", d2)\n", + "print(\"slope\", (d2-d1)/h)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d1 4.0\n", + "d2 4.0001\n", + "slope 0.9999999999976694\n" + ] + } + ], + "source": [ + "h = 0.0001\n", + "\n", + "# inputs\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d1 = a*b +c\n", + "\n", + "c += h # increment c by h\n", + "d2 = a*b + c\n", + "\n", + "print(\"d1\", d1)\n", + "print(\"d2\", d2)\n", + "print(\"slope\", (d2-d1)/h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Value() Object Building" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data):\n", + " self.data = data\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=2.0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for +: 'Value' and 'Value'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/ibad/Desktop/nn-zero-to-hero/my_code/1_value_gradient_backprop.ipynb Cell 15\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m a \u001b[39m=\u001b[39m Value(\u001b[39m2.0\u001b[39m)\n\u001b[1;32m 3\u001b[0m b \u001b[39m=\u001b[39m Value(\u001b[39m-\u001b[39m\u001b[39m3.0\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m c \u001b[39m=\u001b[39m a \u001b[39m+\u001b[39;49m b \u001b[39m# This operating is not defined yet\u001b[39;00m\n\u001b[1;32m 5\u001b[0m c\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Value' and 'Value'" + ] + } + ], + "source": [ + "# Now we want to add 2 value objects\n", + "a = Value(2.0)\n", + "b = Value(-3.0)\n", + "c = a + b # This operating is not defined yet\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\"):\n", + " self.data = data\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " return Value(self.data + other.data, (self, other), \"+\")\n", + "\n", + " def __mul__(self, other):\n", + " return Value(self.data * other.data, (self, other), \"*\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=-1.0)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now we want to add 2 value objects\n", + "a = Value(2.0)\n", + "b = Value(-3.0)\n", + "c = a + b # This operating is not defined yet\n", + "c\n", + "\n", + "# a +b \n", + "# python will do\n", + "# a.__add__(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=4.0)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0)\n", + "b = Value(-3.0)\n", + "c = Value(10.0)\n", + "\n", + "d = a*b +c # (a.__mul__(b)).__add__(c)\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{Value(data=-6.0), Value(data=10.0)}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We want to keep a track of relationship between a, b, c, d\n", + "d._prev # we know the children of the value" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'+'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now we also want to know what operations created them\n", + "d._op" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### These expressions will get very large, we would like to visualise them with this code" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install graphviz\n", + "from graphviz import Digraph\n", + "\n", + "def trace(root):\n", + " # builds a set of all nodes and edges in a graph\n", + " nodes, edges = set(), set()\n", + " def build(v):\n", + " if v not in nodes:\n", + " nodes.add(v)\n", + " for child in v._prev:\n", + " edges.add((child, v))\n", + " build(child)\n", + " build(root)\n", + " return nodes, edges\n", + "\n", + "def draw_dot(root):\n", + " dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n", + " \n", + " nodes, edges = trace(root)\n", + " for n in nodes:\n", + " uid = str(id(n))\n", + " # for any value in the graph, create a rectangular ('record') node for it\n", + " dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n", + " if n._op:\n", + " # if this value is a result of some operation, create an op node for it\n", + " dot.node(name = uid + n._op, label = n._op)\n", + " # and connect this node to it\n", + " dot.edge(uid + n._op, uid)\n", + "\n", + " for n1, n2 in edges:\n", + " # connect n1 to the op node of n2\n", + " dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n", + "\n", + " return dot" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " return Value(self.data + other.data, (self, other), \"+\")\n", + "\n", + " def __mul__(self, other):\n", + " return Value(self.data * other.data, (self, other), \"*\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=-8.0)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0, label=\"a\")\n", + "b = Value(-3.0, label=\"b\")\n", + "c = Value(10.0, label=\"c\")\n", + "e = a*b; e.label=\"e\"\n", + "d = e+c; d.label=\"d\"\n", + "f = Value(-2.0, label=\"f\")\n", + "L = d*f; L.label=\"L\"\n", + "L" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140284111602688\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140284111617808*\n\n*\n\n\n\n140284111602688->140284111617808*\n\n\n\n\n\n140284111615024\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140284111614064*\n\n*\n\n\n\n140284111615024->140284111614064*\n\n\n\n\n\n140284111615024+\n\n+\n\n\n\n140284111615024+->140284111615024\n\n\n\n\n\n140284111607392\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140284111607392->140284111614064*\n\n\n\n\n\n140284111614064\n\nL\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140284111614064*->140284111614064\n\n\n\n\n\n140284111617184\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140284111617184->140284111615024+\n\n\n\n\n\n140284111617808\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140284111617808->140284111615024+\n\n\n\n\n\n140284111617808*->140284111617808\n\n\n\n\n\n140284111613920\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140284111613920->140284111617808*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using this we can visulize the operations\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Now let's calculate gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.000000000021544\n" + ] + } + ], + "source": [ + "def lol():\n", + " h = 0.0001\n", + " a = Value(2.0, label=\"a\")\n", + " b = Value(-3.0, label=\"b\")\n", + " c = Value(10.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-2.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + "\n", + " L1 = L.data\n", + "\n", + " a = Value(2.0 + h, label=\"a\")\n", + " b = Value(-3.0, label=\"b\")\n", + " c = Value(10.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-2.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + "\n", + " L2 = L.data\n", + "\n", + " print((L2-L1)/h)\n", + "\n", + "lol()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "L.grad = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a749d5604b01dc93a2fdc0deac3d2511bae86647 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 17 Sep 2022 10:32:44 +0200 Subject: [PATCH 02/20] upto manual backprop --- my_code/1_value_gradient_backprop.ipynb | 723 ++++++++++++++++++++++-- my_code/neuron_model.jpeg | Bin 0 -> 37814 bytes 2 files changed, 678 insertions(+), 45 deletions(-) create mode 100644 my_code/neuron_model.jpeg diff --git a/my_code/1_value_gradient_backprop.ipynb b/my_code/1_value_gradient_backprop.ipynb index ff15df4a..7f80ea75 100644 --- a/my_code/1_value_gradient_backprop.ipynb +++ b/my_code/1_value_gradient_backprop.ipynb @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 4, @@ -327,32 +327,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for +: 'Value' and 'Value'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/ibad/Desktop/nn-zero-to-hero/my_code/1_value_gradient_backprop.ipynb Cell 15\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m a \u001b[39m=\u001b[39m Value(\u001b[39m2.0\u001b[39m)\n\u001b[1;32m 3\u001b[0m b \u001b[39m=\u001b[39m Value(\u001b[39m-\u001b[39m\u001b[39m3.0\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m c \u001b[39m=\u001b[39m a \u001b[39m+\u001b[39;49m b \u001b[39m# This operating is not defined yet\u001b[39;00m\n\u001b[1;32m 5\u001b[0m c\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Value' and 'Value'" - ] - } - ], + "outputs": [], "source": [ - "# Now we want to add 2 value objects\n", - "a = Value(2.0)\n", - "b = Value(-3.0)\n", - "c = a + b # This operating is not defined yet\n", - "c" + "# # Now we want to add 2 value objects\n", + "# a = Value(2.0)\n", + "# b = Value(-3.0)\n", + "# c = a + b # This operating is not defined yet\n", + "# c" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -384,7 +372,7 @@ "Value(data=-1.0)" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -403,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -412,7 +400,7 @@ "Value(data=4.0)" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -428,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -437,7 +425,7 @@ "{Value(data=-6.0), Value(data=10.0)}" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -458,7 +446,7 @@ "'+'" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -477,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -519,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -544,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -553,7 +541,7 @@ "Value(data=-8.0)" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -569,19 +557,26 @@ "L" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backpropagation" + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140284111602688\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140284111617808*\n\n*\n\n\n\n140284111602688->140284111617808*\n\n\n\n\n\n140284111615024\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140284111614064*\n\n*\n\n\n\n140284111615024->140284111614064*\n\n\n\n\n\n140284111615024+\n\n+\n\n\n\n140284111615024+->140284111615024\n\n\n\n\n\n140284111607392\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140284111607392->140284111614064*\n\n\n\n\n\n140284111614064\n\nL\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140284111614064*->140284111614064\n\n\n\n\n\n140284111617184\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140284111617184->140284111615024+\n\n\n\n\n\n140284111617808\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140284111617808->140284111615024+\n\n\n\n\n\n140284111617808*->140284111617808\n\n\n\n\n\n140284111613920\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140284111613920->140284111617808*\n\n\n\n\n\n", + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058518461488\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140058518463696+\n\n+\n\n\n\n140058518461488->140058518463696+\n\n\n\n\n\n140058518464128\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140058518461776*\n\n*\n\n\n\n140058518464128->140058518461776*\n\n\n\n\n\n140058518463696\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140058518467056*\n\n*\n\n\n\n140058518463696->140058518467056*\n\n\n\n\n\n140058518463696+->140058518463696\n\n\n\n\n\n140058518464224\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140058518464224->140058518467056*\n\n\n\n\n\n140058518461776\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140058518461776->140058518463696+\n\n\n\n\n\n140058518461776*->140058518461776\n\n\n\n\n\n140058518466432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140058518466432->140058518461776*\n\n\n\n\n\n140058518467056\n\nL\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140058518467056*->140058518467056\n\n\n\n\n\n", "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -600,20 +595,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "6.000000000021544\n" + "-2.000000000000668\n" ] } ], "source": [ "def lol():\n", - " h = 0.0001\n", + " h = 0.001\n", " a = Value(2.0, label=\"a\")\n", " b = Value(-3.0, label=\"b\")\n", " c = Value(10.0, label=\"c\")\n", @@ -621,33 +616,671 @@ " d = e+c; d.label=\"d\"\n", " f = Value(-2.0, label=\"f\")\n", " L = d*f; L.label=\"L\"\n", - "\n", " L1 = L.data\n", "\n", - " a = Value(2.0 + h, label=\"a\")\n", + " a = Value(2.0, label=\"a\")\n", " b = Value(-3.0, label=\"b\")\n", " c = Value(10.0, label=\"c\")\n", " e = a*b; e.label=\"e\"\n", " d = e+c; d.label=\"d\"\n", + " d.data += h\n", + "\n", " f = Value(-2.0, label=\"f\")\n", " L = d*f; L.label=\"L\"\n", "\n", - " L2 = L.data\n", + " L2 = L.data #+ h\n", "\n", " print((L2-L1)/h)\n", - "\n", + " \n", "lol()" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "L.grad = 1.0" ] }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058518461488\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140058518463696+\n\n+\n\n\n\n140058518461488->140058518463696+\n\n\n\n\n\n140058518464128\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140058518461776*\n\n*\n\n\n\n140058518464128->140058518461776*\n\n\n\n\n\n140058518463696\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140058518467056*\n\n*\n\n\n\n140058518463696->140058518467056*\n\n\n\n\n\n140058518463696+->140058518463696\n\n\n\n\n\n140058518464224\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140058518464224->140058518467056*\n\n\n\n\n\n140058518461776\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140058518461776->140058518463696+\n\n\n\n\n\n140058518461776*->140058518461776\n\n\n\n\n\n140058518466432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140058518466432->140058518461776*\n\n\n\n\n\n140058518467056\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140058518467056*->140058518467056\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using this we can visulize the operations\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L = d*f\n", + "\n", + "We want to know\n", + "\n", + "dL/dd = ? => f\n", + "\n", + "(f(x+h) - f(x)) / h\n", + "\n", + "proof:\n", + "(f(x+h) - f(x)) / h\n", + "\n", + "((d+h)*f - d*f) / h\n", + "\n", + "(d*f + h*f - d*f) / h\n", + "\n", + "h*f / h\n", + "\n", + "= f\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "f.grad = 4.0 # value of d\n", + "d.grad = -2.0 # value of f" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058518461488\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140058518463696+\n\n+\n\n\n\n140058518461488->140058518463696+\n\n\n\n\n\n140058518464128\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140058518461776*\n\n*\n\n\n\n140058518464128->140058518461776*\n\n\n\n\n\n140058518463696\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140058518467056*\n\n*\n\n\n\n140058518463696->140058518467056*\n\n\n\n\n\n140058518463696+->140058518463696\n\n\n\n\n\n140058518464224\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140058518464224->140058518467056*\n\n\n\n\n\n140058518461776\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140058518461776->140058518463696+\n\n\n\n\n\n140058518461776*->140058518461776\n\n\n\n\n\n140058518466432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140058518466432->140058518461776*\n\n\n\n\n\n140058518467056\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140058518467056*->140058518467056\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-3.9999999999995595\n" + ] + } + ], + "source": [ + "def lol():\n", + " \n", + " h = 0.001\n", + " \n", + " a = Value(2.0, label='a')\n", + " b = Value(-3.0, label='b')\n", + " c = Value(10.0, label='c')\n", + " e = a*b; e.label = 'e'\n", + " d = e + c; d.label = 'd'\n", + " f = Value(-2.0, label='f')\n", + " L = d * f; L.label = 'L'\n", + " \n", + " L1 = L.data\n", + " \n", + " a = Value(2.0, label='a')\n", + " b = Value(-3.0+h, label='b')\n", + " c = Value(10.0, label='c')\n", + " \n", + " e = a*b; e.label = 'e'\n", + " d = e + c; d.label = 'd'\n", + " f = Value(-2.0, label='f')\n", + " L = d * f; L.label = 'L'\n", + " \n", + " L2 = L.data\n", + " \n", + " print((L2 - L1)/h)\n", + " \n", + "lol()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we would want to find dL/dc\n", + "\n", + "We know d = c + e which when differenciated gives dd/dc = 1.0\n", + "\n", + "also, dd/de = 1.0 too\n", + "\n", + "now, dL/dc can calculated with chain rule\n", + "\n", + "The chain rule may also be expressed in Leibniz's notation. If a variable z depends on the variable y, which itself depends on the variable x (that is, y and z are dependent variables), then z depends on x as well, via the intermediate variable y. In this case, the chain rule is expressed as:\n", + "\n", + "\n", + "dz/dx = (dz/dy) * (dy/dx) # relative rate of change \n", + "\n", + "Now, we want:\n", + "dL/dc =\n", + "\n", + "We know:\n", + "dL/dd\n", + "dd/dc\n", + "\n", + "So, \n", + "dL/dc = (dL/dd) * (dd/dc)\n", + "\n", + "The + node just directly routes the rate of change." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058518461488\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140058518463696+\n\n+\n\n\n\n140058518461488->140058518463696+\n\n\n\n\n\n140058518464128\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140058518461776*\n\n*\n\n\n\n140058518464128->140058518461776*\n\n\n\n\n\n140058518463696\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140058518467056*\n\n*\n\n\n\n140058518463696->140058518467056*\n\n\n\n\n\n140058518463696+->140058518463696\n\n\n\n\n\n140058518464224\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140058518464224->140058518467056*\n\n\n\n\n\n140058518461776\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140058518461776->140058518463696+\n\n\n\n\n\n140058518461776*->140058518461776\n\n\n\n\n\n140058518466432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140058518466432->140058518461776*\n\n\n\n\n\n140058518467056\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140058518467056*->140058518467056\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Therefore:\n", + "c.grad = -2.0\n", + "e.grad = -2.0\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we want:\n", + "\n", + "dL/da = (dL/de) * (de/da) \n", + "\n", + "and\n", + "\n", + "dL/db = dL/de) * (de/db)\n", + "\n", + "e = a*b\n", + "\n", + "de/da = b\n", + "\n", + "de/db = a" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058518461488\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140058518463696+\n\n+\n\n\n\n140058518461488->140058518463696+\n\n\n\n\n\n140058518464128\n\na\n\ndata 2.0000\n\ngrad 6.0000\n\n\n\n140058518461776*\n\n*\n\n\n\n140058518464128->140058518461776*\n\n\n\n\n\n140058518463696\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140058518467056*\n\n*\n\n\n\n140058518463696->140058518467056*\n\n\n\n\n\n140058518463696+->140058518463696\n\n\n\n\n\n140058518464224\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140058518464224->140058518467056*\n\n\n\n\n\n140058518461776\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140058518461776->140058518463696+\n\n\n\n\n\n140058518461776*->140058518461776\n\n\n\n\n\n140058518466432\n\nb\n\ndata -3.0000\n\ngrad -4.0000\n\n\n\n140058518466432->140058518461776*\n\n\n\n\n\n140058518467056\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140058518467056*->140058518467056\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.grad = (-2.0 * -3.0)\n", + "b.grad = (-2.0 * 2.0)\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is exactly what backpropagation is:\n", + "Backproagation is recursive application of chain rule throught the computation graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### My own little exercise to see if i really understood everything correctly" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=145.0)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(4.0, label=\"a\")\n", + "b = Value(-8.0, label=\"b\")\n", + "c = Value(3.0, label=\"c\")\n", + "e = a*b; e.label=\"e\"\n", + "d = e+c; d.label=\"d\"\n", + "f = Value(-5.0, label=\"f\")\n", + "L = d*f; L.label=\"L\"\n", + "L" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058518464032\n\na\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140058518470032*\n\n*\n\n\n\n140058518464032->140058518470032*\n\n\n\n\n\n140058518461056\n\nL\n\ndata 145.0000\n\ngrad 0.0000\n\n\n\n140058518461056*\n\n*\n\n\n\n140058518461056*->140058518461056\n\n\n\n\n\n140058518461152\n\nf\n\ndata -5.0000\n\ngrad 0.0000\n\n\n\n140058518461152->140058518461056*\n\n\n\n\n\n140058518461200\n\nb\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140058518461200->140058518470032*\n\n\n\n\n\n140058518462784\n\nc\n\ndata 3.0000\n\ngrad 0.0000\n\n\n\n140058518462880+\n\n+\n\n\n\n140058518462784->140058518462880+\n\n\n\n\n\n140058518470032\n\ne\n\ndata -32.0000\n\ngrad 0.0000\n\n\n\n140058518470032->140058518462880+\n\n\n\n\n\n140058518470032*->140058518470032\n\n\n\n\n\n140058518462880\n\nd\n\ndata -29.0000\n\ngrad 0.0000\n\n\n\n140058518462880->140058518461056*\n\n\n\n\n\n140058518462880+->140058518462880\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "L.grad = 1.0\n", + "\n", + "f.grad = -29.0\n", + "d.grad = -5.0\n", + "\n", + "e.grad = -5 * 1\n", + "c.grad = -5 * 1\n", + "\n", + "b.grad = -5 * 4.0\n", + "a.grad = -5 * -8.0\n", + "\n", + "# All correct and verified\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.000000000020464\n" + ] + } + ], + "source": [ + "# verfiying the gradient\n", + "def lol():\n", + "\n", + " h = 0.001\n", + "\n", + " a = Value(4.0, label=\"a\")\n", + " b = Value(-8.0, label=\"b\")\n", + " c = Value(3.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-5.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + "\n", + " L1 = L.data\n", + "\n", + " a = Value(4.0 + h, label=\"a\")\n", + " b = Value(-8.0, label=\"b\")\n", + " c = Value(3.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " \n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-5.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + " L2 = L.data\n", + "\n", + " print((L2 - L1)/h)\n", + " \n", + "lol()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single Optimisation Step\n", + "\n", + "We will nudge our inputs to make L go up" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "a = Value(2.0, label=\"a\")\n", + "b = Value(-3.0, label=\"b\")\n", + "c = Value(10.0, label=\"c\")\n", + "e = a*b; e.label=\"e\"\n", + "d = e+c; d.label=\"d\"\n", + "f = Value(-2.0, label=\"f\")\n", + "L = d*f; L.label=\"L\"\n", + "\n", + "L.grad = 1.0\n", + "f.grad = 4.0 # value of d\n", + "d.grad = -2.0 # value of f\n", + "\n", + "c.grad = -2.0\n", + "e.grad = -2.0\n", + "\n", + "a.grad = (-2.0 * -3.0)\n", + "b.grad = (-2.0 * 2.0)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058483255824\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140058483254912*\n\n*\n\n\n\n140058483255824->140058483254912*\n\n\n\n\n\n140058483255392\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140058483255392->140058483254912*\n\n\n\n\n\n140058483255392+\n\n+\n\n\n\n140058483255392+->140058483255392\n\n\n\n\n\n140058483254912\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140058483254912*->140058483254912\n\n\n\n\n\n140058483258032\n\nb\n\ndata -3.0000\n\ngrad -4.0000\n\n\n\n140058483254672*\n\n*\n\n\n\n140058483258032->140058483254672*\n\n\n\n\n\n140058483254960\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140058483254960->140058483255392+\n\n\n\n\n\n140058483252608\n\na\n\ndata 2.0000\n\ngrad 6.0000\n\n\n\n140058483252608->140058483254672*\n\n\n\n\n\n140058483254672\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140058483254672->140058483255392+\n\n\n\n\n\n140058483254672*->140058483254672\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-7.286496\n" + ] + } + ], + "source": [ + "step_size = 0.01\n", + "\n", + "# We are chaning the leaf nodes\n", + "a.data += step_size*a.grad\n", + "b.data += step_size*b.grad\n", + "c.data += step_size*c.grad\n", + "f.data += step_size*f.grad\n", + "\n", + "# Let's do the forward pass again\n", + "e = a * b\n", + "d = e + c\n", + "L = d * f\n", + "\n", + "# We expect a greater value of L here\n", + "print(L.data)\n", + "\n", + "# This can be used to optimise as we can control the value of L" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058483255824\n\nf\n\ndata -1.9600\n\ngrad 4.0000\n\n\n\n140058483257888*\n\n*\n\n\n\n140058483255824->140058483257888*\n\n\n\n\n\n140058483257888\n\n \n\ndata -7.2865\n\ngrad 0.0000\n\n\n\n140058483257888*->140058483257888\n\n\n\n\n\n140058483254960\n\nc\n\ndata 9.9800\n\ngrad -2.0000\n\n\n\n140058483255536+\n\n+\n\n\n\n140058483254960->140058483255536+\n\n\n\n\n\n140058483252416\n\n \n\ndata -6.2624\n\ngrad 0.0000\n\n\n\n140058483252416->140058483255536+\n\n\n\n\n\n140058483252416*\n\n*\n\n\n\n140058483252416*->140058483252416\n\n\n\n\n\n140058483258032\n\nb\n\ndata -3.0400\n\ngrad -4.0000\n\n\n\n140058483258032->140058483252416*\n\n\n\n\n\n140058483255536\n\n \n\ndata 3.7176\n\ngrad 0.0000\n\n\n\n140058483255536->140058483257888*\n\n\n\n\n\n140058483255536+->140058483255536\n\n\n\n\n\n140058483252608\n\na\n\ndata 2.0600\n\ngrad 6.0000\n\n\n\n140058483252608->140058483252416*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual Backproagation through a Neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "tanh fucntion, activatiom" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.arange(-5, 5, 0.2)\n", + "y = np.tanh(np.arange(-5, 5, 0.2))\n", + "plt.plot(x, y)\n", + "plt.grid()\n", + "\n", + "# It squashes the input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "o = tanh(n)\n", + "\n", + "dervate of tanh = 1 - tanh²\n", + "\n", + "do/dn = 1 - o²\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " return Value(self.data + other.data, (self, other), \"+\")\n", + "\n", + " def __mul__(self, other):\n", + " return Value(self.data * other.data, (self, other), \"*\")\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# see neuron_model.jpeg\n", + "\n", + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058483810384\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140058483812784*\n\n*\n\n\n\n140058483810384->140058483812784*\n\n\n\n\n\n140058483818064\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140058483812592+\n\n+\n\n\n\n140058483818064->140058483812592+\n\n\n\n\n\n140058483818064*\n\n*\n\n\n\n140058483818064*->140058483818064\n\n\n\n\n\n140058483812496\n\noutput\n\ndata 0.7071\n\ngrad 0.0000\n\n\n\n140058483812496tanh\n\ntanh\n\n\n\n140058483812496tanh->140058483812496\n\n\n\n\n\n140058483811008\n\nb\n\ndata 6.8814\n\ngrad 0.0000\n\n\n\n140058483812688+\n\n+\n\n\n\n140058483811008->140058483812688+\n\n\n\n\n\n140058483805920\n\nw1\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140058483805920->140058483818064*\n\n\n\n\n\n140058483812592\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140058483812592->140058483812688+\n\n\n\n\n\n140058483812592+->140058483812592\n\n\n\n\n\n140058483812688\n\nn\n\ndata 0.8814\n\ngrad 0.0000\n\n\n\n140058483812688->140058483812496tanh\n\n\n\n\n\n140058483812688+->140058483812688\n\n\n\n\n\n140058483812208\n\nx1\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140058483812208->140058483818064*\n\n\n\n\n\n140058483812784\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140058483812784->140058483812592+\n\n\n\n\n\n140058483812784*->140058483812784\n\n\n\n\n\n140058483810240\n\nx2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140058483810240->140058483812784*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we would care more about gradients wrt weights w1 and w2.\n", + "\n", + "\n", + "Now we calculate gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140058483810384\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140058483812784*\n\n*\n\n\n\n140058483810384->140058483812784*\n\n\n\n\n\n140058483818064\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140058483812592+\n\n+\n\n\n\n140058483818064->140058483812592+\n\n\n\n\n\n140058483818064*\n\n*\n\n\n\n140058483818064*->140058483818064\n\n\n\n\n\n140058483812496\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140058483812496tanh\n\ntanh\n\n\n\n140058483812496tanh->140058483812496\n\n\n\n\n\n140058483811008\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140058483812688+\n\n+\n\n\n\n140058483811008->140058483812688+\n\n\n\n\n\n140058483805920\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140058483805920->140058483818064*\n\n\n\n\n\n140058483812592\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140058483812592->140058483812688+\n\n\n\n\n\n140058483812592+->140058483812592\n\n\n\n\n\n140058483812688\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140058483812688->140058483812496tanh\n\n\n\n\n\n140058483812688+->140058483812688\n\n\n\n\n\n140058483812208\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140058483812208->140058483818064*\n\n\n\n\n\n140058483812784\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140058483812784->140058483812592+\n\n\n\n\n\n140058483812784*->140058483812784\n\n\n\n\n\n140058483810240\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140058483810240->140058483812784*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o.grad = 1.0\n", + "n.grad = 1 - o.data**2\n", + "\n", + "b.grad = 0.5\n", + "x1w1x2w2.grad = 0.5\n", + "\n", + "x1w1.grad = 0.5\n", + "x2w2.grad = 0.5\n", + "\n", + "w1.grad = x1w1.grad * x1.data\n", + "x1.grad = x1w1.grad * w1.data\n", + "\n", + "w2.grad = x2w2.grad * x2.data\n", + "x2.grad = x2w2.grad * w2.data\n", + "\n", + "draw_dot(o)\n" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/my_code/neuron_model.jpeg b/my_code/neuron_model.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..7943119c938906584835148b350061354fdd7636 GIT binary patch literal 37814 zcmd422Ut^Gw=Nn4L_tJ)CsaYI2uKei0s;a80#c(OU3!ZUh=PFhCh$p%Gy!QMy-SUP zfbjOjq0Khc>B^d*NoYeZqc`^VrFK(LC4C*!ofMaalWzoDSKPDy#4iI#?z>3{mT z`~jf9Mh3Z(N>0WPxI#}xPEU5(0|1i7N=f#Q;~#_nd+b+8W4uar?K(9LsYBf@z!frb z@+%bNe~+4UHiUE@KtWH*z$dMKmC@)W6~8yry?4oZ*90C`cQPA~U#!WnfV2UMPG_bO22-usjaJTXl!ck>h9_7L;U>JKRPx(F*!9oGmAnm zudJ@EZ)|Si_74t^@W+Ic)4y?%0m%Oa7U}(8ko^-bdJ?WH6cpqXRDa_lyAt>}aC!<# zKIy9r>PA#Ay&3uMy}QQrFgdTf^SXeHF_ziRXM~zXP!=VG`y1Lnko~^{7WTh{?B9U> zJFZ#44RSKl3Sb5I$&1#LwYdy zeP%$eq=z_0>WM^^>&+bsnUmMR9c+H&og~3GSAELDaqw=nhd(A63O92L*` zXtdQ0((Dki7F(+4GwXklmm=ntC+K_=OC2qsaPzL#HCL+*I*pB&gA#I}jbf93!2MU) 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_op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad = 1.0 * out.grad\n", + " other.grad = 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad = other.data * out.grad\n", + " other.grad = self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad = (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970775568\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934156128*\n\n*\n\n\n\n140596970775568->140596934156128*\n\n\n\n\n\n140596970780752\n\nx1\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596934160736*\n\n*\n\n\n\n140596970780752->140596934160736*\n\n\n\n\n\n140596970777248\n\nb\n\ndata 6.8814\n\ngrad 0.0000\n\n\n\n140596934162752+\n\n+\n\n\n\n140596970777248->140596934162752+\n\n\n\n\n\n140596970782384\n\nw1\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970782384->140596934160736*\n\n\n\n\n\n140596934162752\n\nn\n\ndata 0.8814\n\ngrad 0.0000\n\n\n\n140596934158672tanh\n\ntanh\n\n\n\n140596934162752->140596934158672tanh\n\n\n\n\n\n140596934162752+->140596934162752\n\n\n\n\n\n140596934158672\n\noutput\n\ndata 0.7071\n\ngrad 0.0000\n\n\n\n140596934158672tanh->140596934158672\n\n\n\n\n\n140596934156128\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596934156224+\n\n+\n\n\n\n140596934156128->140596934156224+\n\n\n\n\n\n140596934156128*->140596934156128\n\n\n\n\n\n140596970775376\n\nx2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596970775376->140596934156128*\n\n\n\n\n\n140596934160736\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596934160736->140596934156224+\n\n\n\n\n\n140596934160736*->140596934160736\n\n\n\n\n\n140596934156224\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596934156224->140596934162752+\n\n\n\n\n\n140596934156224+->140596934156224\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We don't have to do it manually now. We just have to call backward in the right order" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# but we will have initialise o.grad first\n", + "o.grad = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "o._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "n._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "b._backward() # nothing will happen. None" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "x1w1x2w2._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "x1w1._backward()\n", + "x2w2._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970775568\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934156128*\n\n*\n\n\n\n140596970775568->140596934156128*\n\n\n\n\n\n140596970780752\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596934160736*\n\n*\n\n\n\n140596970780752->140596934160736*\n\n\n\n\n\n140596970777248\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596934162752+\n\n+\n\n\n\n140596970777248->140596934162752+\n\n\n\n\n\n140596970782384\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596970782384->140596934160736*\n\n\n\n\n\n140596934162752\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596934158672tanh\n\ntanh\n\n\n\n140596934162752->140596934158672tanh\n\n\n\n\n\n140596934162752+->140596934162752\n\n\n\n\n\n140596934158672\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596934158672tanh->140596934158672\n\n\n\n\n\n140596934156128\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934156224+\n\n+\n\n\n\n140596934156128->140596934156224+\n\n\n\n\n\n140596934156128*->140596934156128\n\n\n\n\n\n140596970775376\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596970775376->140596934156128*\n\n\n\n\n\n140596934160736\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934160736->140596934156224+\n\n\n\n\n\n140596934160736*->140596934160736\n\n\n\n\n\n140596934156224\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934156224->140596934162752+\n\n\n\n\n\n140596934156224+->140596934156224\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementing the backward function for a whole expression graph\n", + "Now we want to eliminate calling backward pass manually\n", + "\n", + "We want to calculate backward for a node only when backward for all the forward nodes have been calculated.\n", + "\n", + "We calculate backward when backward for all dependencies has been calculated.\n", + "\n", + "This ordering of graphs can be achieved by something called **Toplogical Sort**" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "topo = []\n", + "visited = set()\n", + "\n", + "def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "o.grad = 1.0\n", + "\n", + "build_topo(o)\n", + "\n", + "for node in reversed(topo):\n", + " node._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140598171427376\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934161888*\n\n*\n\n\n\n140598171427376->140596934161888*\n\n\n\n\n\n140596934158432\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934162080+\n\n+\n\n\n\n140596934158432->140596934162080+\n\n\n\n\n\n140596934158432*\n\n*\n\n\n\n140596934158432*->140596934158432\n\n\n\n\n\n140596934149216\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596934155696+\n\n+\n\n\n\n140596934149216->140596934155696+\n\n\n\n\n\n140596934162080\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934162080->140596934155696+\n\n\n\n\n\n140596934162080+->140596934162080\n\n\n\n\n\n140596970774272\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596970774272->140596934158432*\n\n\n\n\n\n140596934163424\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596934163424tanh\n\ntanh\n\n\n\n140596934163424tanh->140596934163424\n\n\n\n\n\n140596970779984\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596970779984->140596934158432*\n\n\n\n\n\n140596934155696\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596934155696->140596934163424tanh\n\n\n\n\n\n140596934155696+->140596934155696\n\n\n\n\n\n140596934149072\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934149072->140596934161888*\n\n\n\n\n\n140596934161888\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934161888->140596934162080+\n\n\n\n\n\n140596934161888*->140596934161888\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here we have it correctly\n", + "draw_dot(o)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's add this to value class\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad = 1.0 * out.grad\n", + " other.grad = 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad = other.data * out.grad\n", + " other.grad = self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad = (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's see if everything works\n", + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936162848\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596936160880+\n\n+\n\n\n\n140596936162848->140596936160880+\n\n\n\n\n\n140596936157232\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936158288+\n\n+\n\n\n\n140596936157232->140596936158288+\n\n\n\n\n\n140596936157232*\n\n*\n\n\n\n140596936157232*->140596936157232\n\n\n\n\n\n140596936158288\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936158288->140596936160880+\n\n\n\n\n\n140596936158288+->140596936158288\n\n\n\n\n\n140596936160880\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596936156032tanh\n\ntanh\n\n\n\n140596936160880->140596936156032tanh\n\n\n\n\n\n140596936160880+->140596936160880\n\n\n\n\n\n140596936162944\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936162944->140596936157232*\n\n\n\n\n\n140596936162464\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936162464->140596936158288+\n\n\n\n\n\n140596936162464*\n\n*\n\n\n\n140596936162464*->140596936162464\n\n\n\n\n\n140596936162992\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596936162992->140596936162464*\n\n\n\n\n\n140596936159968\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596936159968->140596936162464*\n\n\n\n\n\n140596936156992\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936156992->140596936157232*\n\n\n\n\n\n140596936156032\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596936156032tanh->140596936156032\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o.backward()\n", + "\n", + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now that's backprop for 1 Neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is still a bug, we haven't surfaced yet" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936162704\n\na\n\ndata 3.0000\n\ngrad 1.0000\n\n\n\n140596936159344+\n\n+\n\n\n\n140596936162704->140596936159344+\n\n\n\n\n\n140596936159344\n\nb\n\ndata 6.0000\n\ngrad 1.0000\n\n\n\n140596936159344+->140596936159344\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(3.0, label=\"a\")\n", + "b = a + a; b.label=\"b\"\n", + "\n", + "b.backward()\n", + "\n", + "draw_dot(b)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596934151184\n\nf\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934151184*\n\n*\n\n\n\n140596934151184*->140596934151184\n\n\n\n\n\n140596936160784\n\na\n\ndata -2.0000\n\ngrad -6.0000\n\n\n\n140596934151424+\n\n+\n\n\n\n140596936160784->140596934151424+\n\n\n\n\n\n140596934159776*\n\n*\n\n\n\n140596936160784->140596934159776*\n\n\n\n\n\n140596936162416\n\nb\n\ndata 3.0000\n\ngrad -6.0000\n\n\n\n140596936162416->140596934151424+\n\n\n\n\n\n140596936162416->140596934159776*\n\n\n\n\n\n140596934151424\n\ne\n\ndata 1.0000\n\ngrad -6.0000\n\n\n\n140596934151424->140596934151184*\n\n\n\n\n\n140596934151424+->140596934151424\n\n\n\n\n\n140596934159776\n\nd\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934159776->140596934151184*\n\n\n\n\n\n140596934159776*->140596934159776\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Another example for this bug\n", + "a = Value(-2.0, label='a')\n", + "b = Value(3.0, label='b')\n", + "d = a * b ; d.label = 'd'\n", + "e = a + b ; e.label = 'e'\n", + "f = d * e ; f.label = 'f'\n", + "\n", + "f.backward()\n", + "\n", + "draw_dot(f)\n", + "\n", + "# There will be an issue when we are using same variable twice\n", + "# before we were using one variable once\n", + "\n", + "# If you see the multivariate chain rule you will see we need to accumulate the gradients\n", + "# That's what we will do now\n", + "# Any contrubution that flows backward will simply add" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's add this to value class\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970778256\n\na\n\ndata -2.0000\n\ngrad -3.0000\n\n\n\n140596934149792+\n\n+\n\n\n\n140596970778256->140596934149792+\n\n\n\n\n\n140596934148880*\n\n*\n\n\n\n140596970778256->140596934148880*\n\n\n\n\n\n140596934149792\n\ne\n\ndata 1.0000\n\ngrad -6.0000\n\n\n\n140596934162368*\n\n*\n\n\n\n140596934149792->140596934162368*\n\n\n\n\n\n140596934149792+->140596934149792\n\n\n\n\n\n140596934148880\n\nd\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934148880->140596934162368*\n\n\n\n\n\n140596934148880*->140596934148880\n\n\n\n\n\n140596970780560\n\nb\n\ndata 3.0000\n\ngrad -8.0000\n\n\n\n140596970780560->140596934149792+\n\n\n\n\n\n140596970780560->140596934148880*\n\n\n\n\n\n140596934162368\n\nf\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934162368*->140596934162368\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Another example for this bug\n", + "a = Value(-2.0, label='a')\n", + "b = Value(3.0, label='b')\n", + "d = a * b ; d.label = 'd'\n", + "e = a + b ; e.label = 'e'\n", + "f = d * e ; f.label = 'f'\n", + "\n", + "f.backward()\n", + "\n", + "draw_dot(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596934162848\n\na\n\ndata 3.0000\n\ngrad 2.0000\n\n\n\n140596934163808+\n\n+\n\n\n\n140596934162848->140596934163808+\n\n\n\n\n\n140596934163808\n\nb\n\ndata 6.0000\n\ngrad 1.0000\n\n\n\n140596934163808+->140596934163808\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(3.0, label=\"a\")\n", + "b = a + a; b.label=\"b\"\n", + "\n", + "b.backward()\n", + "\n", + "draw_dot(b)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now everything is correct" + ] + }, { "cell_type": "code", "execution_count": null, From dc11c53518d12d88fa35fc3d0bcd5409c8b4dc26 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 17 Sep 2022 16:21:42 +0200 Subject: [PATCH 04/20] value class complete --- ... => 1_gradients_and_backpropagation.ipynb} | 0 ..._activation_optmization_grad_descent.ipynb | 585 ++++++++++++++++++ 2 files changed, 585 insertions(+) rename my_code/{1_value_gradient_backprop.ipynb => 1_gradients_and_backpropagation.ipynb} (100%) create mode 100644 my_code/2_activation_optmization_grad_descent.ipynb diff --git a/my_code/1_value_gradient_backprop.ipynb b/my_code/1_gradients_and_backpropagation.ipynb similarity index 100% rename from my_code/1_value_gradient_backprop.ipynb rename to my_code/1_gradients_and_backpropagation.ipynb diff --git a/my_code/2_activation_optmization_grad_descent.ipynb b/my_code/2_activation_optmization_grad_descent.ipynb new file mode 100644 index 00000000..e49ce77f --- /dev/null +++ b/my_code/2_activation_optmization_grad_descent.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def f(x):\n", + " return 3*x**2 - 4*x + 5\n", + "\n", + "f(3.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.arange(-5, 5, 0.25)\n", + "ys = f(xs)\n", + "plt.plot(xs, ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install graphviz\n", + "from graphviz import Digraph\n", + "\n", + "def trace(root):\n", + " # builds a set of all nodes and edges in a graph\n", + " nodes, edges = set(), set()\n", + " def build(v):\n", + " if v not in nodes:\n", + " nodes.add(v)\n", + " for child in v._prev:\n", + " edges.add((child, v))\n", + " build(child)\n", + " build(root)\n", + " return nodes, edges\n", + "\n", + "def draw_dot(root):\n", + " dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n", + " \n", + " nodes, edges = trace(root)\n", + " for n in nodes:\n", + " uid = str(id(n))\n", + " # for any value in the graph, create a rectangular ('record') node for it\n", + " dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n", + " if n._op:\n", + " # if this value is a result of some operation, create an op node for it\n", + " dot.node(name = uid + n._op, label = n._op)\n", + " # and connect this node to it\n", + " dot.edge(uid + n._op, uid)\n", + "\n", + " for n1, n2 in edges:\n", + " # connect n1 to the op node of n2\n", + " dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n", + "\n", + " return dot" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def trace(root):\n", + " # builds a set of all nodes and edges in a graph\n", + " nodes, edges = set(), set()\n", + " def build(v):\n", + " if v not in nodes:\n", + " nodes.add(v)\n", + " for child in v._prev:\n", + " edges.add((child, v))\n", + " build(child)\n", + " build(root)\n", + " return nodes, edges" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's add this to value class\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " self.grad += other * (self.data ** (other - 1)) * out.grad\n", + "\n", + " self.grad = 1.0 \n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Currently we cannot do integer addition directly, so, we will make change now\n", + "# We will do this for multiply\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " \n", + " def __add__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other) # now, we can add integer directly\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + " def __radd__(self, other):\n", + " return self + other\n", + "\n", + " \n", + " def __mul__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + "\n", + " def __pow__(self, other):\n", + " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", + " out = Value(self.data**other, (self,), f'**{other}')\n", + "\n", + " def _backward():\n", + " self.grad += (other*self.data**(other - 1)) * out.grad\n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + "\n", + " def __rmul__(self, other): #other * self\n", + " return self * other\n", + "\n", + " \n", + " def __truediv__(self, other):\n", + " return self * (other**-1)\n", + "\n", + " \n", + " def __sub__(self, other):\n", + " return self + (-other) \n", + " \n", + " \n", + " def exp(self):\n", + " x = self.data\n", + " out = Value(math.exp(x), (self, ), \"exp\")\n", + "\n", + " def _backward():\n", + " self.grad += out.data * out.grad # exp(x) * out.grad\n", + " \n", + " out._backward = _backward\n", + " \n", + " return out\n", + "\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value(data=3.0)\n", + "Value(data=3.0)\n", + "Value(data=4.0)\n", + "Value(data=1.0)\n", + "Value(data=0.5)\n", + "Value(data=16.0)\n", + "Value(data=54.598150033144236)\n" + ] + } + ], + "source": [ + "a = Value(2.0)\n", + "b = Value(4.0)\n", + "\n", + "print(a+1)\n", + "print(1+a)\n", + "\n", + "print(a*2)\n", + "print(a/2)\n", + "print(a/b)\n", + "\n", + "print(b**2)\n", + "\n", + "print(b.exp())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Division**\n", + "\n", + "a / b\n", + "\n", + "a * (1/b)\n", + "\n", + "a * (b**-1)\n", + "\n", + "Let's make:\n", + "x**k\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=0.5)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0)\n", + "b = Value(4.0)\n", + "\n", + "a/b\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Breaking down tanh" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's see if everything works\n", + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "#o = n.tanh(); o.label = \"output\"\n", + "# ---------- tanh break-up ----------\n", + "e = (2*n).exp()\n", + "o = (e - 1) / (e + 1); o.label = \"output\"\n", + "\n", + "# ---------- tanh break-up ----------\n", + "\n", + "\n", + "o.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140471696892416\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140471696895440*\n\n*\n\n\n\n140471696892416->140471696895440*\n\n\n\n\n\n140471696889872\n\n \n\ndata 1.7627\n\ngrad 0.2500\n\n\n\n140471696890112exp\n\nexp\n\n\n\n140471696889872->140471696890112exp\n\n\n\n\n\n140471696889872*\n\n*\n\n\n\n140471696889872*->140471696889872\n\n\n\n\n\n140471696896544\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140471696899856+\n\n+\n\n\n\n140471696896544->140471696899856+\n\n\n\n\n\n140471696896544+\n\n+\n\n\n\n140471696896544+->140471696896544\n\n\n\n\n\n140471696887376\n\n \n\ndata 2.0000\n\ngrad 0.2203\n\n\n\n140471696887376->140471696889872*\n\n\n\n\n\n140471696890448\n\n \n\ndata 1.0000\n\ngrad -0.1036\n\n\n\n140471696890640+\n\n+\n\n\n\n140471696890448->140471696890640+\n\n\n\n\n\n140471696895440\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140471696895440->140471696896544+\n\n\n\n\n\n140471696895440*->140471696895440\n\n\n\n\n\n140471696890256\n\n \n\ndata -1.0000\n\ngrad 0.1464\n\n\n\n140471696890160+\n\n+\n\n\n\n140471696890256->140471696890160+\n\n\n\n\n\n140471696896208\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140471696896208->140471696895440*\n\n\n\n\n\n140471696888576\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140471696888576*\n\n*\n\n\n\n140471696888576*->140471696888576\n\n\n\n\n\n140471696890640\n\n \n\ndata 6.8284\n\ngrad -0.1036\n\n\n\n140471696888720**-1\n\n**-1\n\n\n\n140471696890640->140471696888720**-1\n\n\n\n\n\n140471696890640+->140471696890640\n\n\n\n\n\n140471696890112\n\n \n\ndata 5.8284\n\ngrad 0.0429\n\n\n\n140471696890112->140471696890640+\n\n\n\n\n\n140471696890112->140471696890160+\n\n\n\n\n\n140471696890112exp->140471696890112\n\n\n\n\n\n140471696899856\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140471696899856->140471696889872*\n\n\n\n\n\n140471696899856+->140471696899856\n\n\n\n\n\n140471696890160\n\n \n\ndata 4.8284\n\ngrad 0.1464\n\n\n\n140471696890160->140471696888576*\n\n\n\n\n\n140471696890160+->140471696890160\n\n\n\n\n\n140471696892752\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140471696900000*\n\n*\n\n\n\n140471696892752->140471696900000*\n\n\n\n\n\n140471696892800\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140471696892800->140471696899856+\n\n\n\n\n\n140471696888720\n\n \n\ndata 0.1464\n\ngrad 4.8284\n\n\n\n140471696888720->140471696888576*\n\n\n\n\n\n140471696888720**-1->140471696888720\n\n\n\n\n\n140471696900000\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140471696900000->140471696896544+\n\n\n\n\n\n140471696900000*->140471696900000\n\n\n\n\n\n140471696892368\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140471696892368->140471696900000*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The outputs of this resembles exactly the output if we directly use tanh fucntion. So, it doesn't matter how we implement operations. \n", + "\n", + "All that matters is we can correctly calulate the forward and backward pass." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now the same thing is PyTorch" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7071066904050358\n", + "---\n", + "x2 0.5000001283844369\n", + "w2 0.0\n", + "x1 -1.5000003851533106\n", + "w1 1.0000002567688737\n" + ] + } + ], + "source": [ + "x1 = torch.Tensor([2.0]).double() ; x1.requires_grad = True\n", + "x2 = torch.Tensor([0.0]).double() ; x2.requires_grad = True\n", + "w1 = torch.Tensor([-3.0]).double() ; w1.requires_grad = True\n", + "w2 = torch.Tensor([1.0]).double() ; w2.requires_grad = True\n", + "b = torch.Tensor([6.8813735870195432]).double() ; b.requires_grad = True\n", + "n = x1*w1 + x2*w2 + b\n", + "o = torch.tanh(n)\n", + "\n", + "print(o.data.item())\n", + "o.backward()\n", + "\n", + "print('---')\n", + "print('x2', x2.grad.item())\n", + "print('w2', w2.grad.item())\n", + "print('x1', x1.grad.item())\n", + "print('w1', w1.grad.item())\n", + "\n", + "# The result is exactly the same" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Neural Net(s) now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5997ca9db1a4ba5d14d170787a4d54c54f219c8b Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sun, 18 Sep 2022 10:31:10 +0200 Subject: [PATCH 05/20] micrograd turotial complete --- ...icrograd_lecture_second_half_roughly.ipynb | 675 +---------- ..._activation_optmization_grad_descent.ipynb | 1067 ++++++++++++++++- 2 files changed, 1062 insertions(+), 680 deletions(-) diff --git a/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb b/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb index 67a25a68..a0211aaa 100644 --- a/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb +++ b/lectures/micrograd/micrograd_lecture_second_half_roughly.ipynb @@ -60,7 +60,7 @@ }, { "data": { - "image/png": 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\n", 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" ] @@ -195,14 +195,14 @@ " return out\n", " \n", " def __pow__(self, other):\n", - " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", - " out = Value(self.data**other, (self,), f'**{other}')\n", + " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", + " out = Value(self.data**other, (self,), f'**{other}')\n", "\n", - " def _backward():\n", - " self.grad += other * (self.data ** (other - 1)) * out.grad\n", - " out._backward = _backward\n", + " def _backward():\n", + " self.grad += other * (self.data ** (other - 1)) * out.grad\n", + " out._backward = _backward\n", "\n", - " return out\n", + " return out\n", " \n", " def __rmul__(self, other): # other * self\n", " return self * other\n", @@ -330,234 +330,7 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "140307325553680\n", - "\n", - "x1*w1\n", - "\n", - "data -6.0000\n", - "\n", - "grad 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a/my_code/2_activation_optmization_grad_descent.ipynb b/my_code/2_activation_optmization_grad_descent.ipynb index e49ce77f..22dc4327 100644 --- a/my_code/2_activation_optmization_grad_descent.ipynb +++ b/my_code/2_activation_optmization_grad_descent.ipynb @@ -17,6 +17,15 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [], + "source": [ + "random.seed(1993) # For consistency" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { @@ -24,7 +33,7 @@ "20.0" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -38,16 +47,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -70,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -198,7 +207,6 @@ " for child in v._prev:\n", " build_topo(child)\n", " topo.append(v)\n", - " self.grad += other * (self.data ** (other - 1)) * out.grad\n", "\n", " self.grad = 1.0 \n", " for node in reversed(topo):\n", @@ -207,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -381,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -390,7 +398,7 @@ "Value(data=0.5)" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -411,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -450,17 +458,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140471696892416\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140471696895440*\n\n*\n\n\n\n140471696892416->140471696895440*\n\n\n\n\n\n140471696889872\n\n \n\ndata 1.7627\n\ngrad 0.2500\n\n\n\n140471696890112exp\n\nexp\n\n\n\n140471696889872->140471696890112exp\n\n\n\n\n\n140471696889872*\n\n*\n\n\n\n140471696889872*->140471696889872\n\n\n\n\n\n140471696896544\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140471696899856+\n\n+\n\n\n\n140471696896544->140471696899856+\n\n\n\n\n\n140471696896544+\n\n+\n\n\n\n140471696896544+->140471696896544\n\n\n\n\n\n140471696887376\n\n \n\ndata 2.0000\n\ngrad 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0.0429\n\n\n\n140471696890112->140471696890640+\n\n\n\n\n\n140471696890112->140471696890160+\n\n\n\n\n\n140471696890112exp->140471696890112\n\n\n\n\n\n140471696899856\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140471696899856->140471696889872*\n\n\n\n\n\n140471696899856+->140471696899856\n\n\n\n\n\n140471696890160\n\n \n\ndata 4.8284\n\ngrad 0.1464\n\n\n\n140471696890160->140471696888576*\n\n\n\n\n\n140471696890160+->140471696890160\n\n\n\n\n\n140471696892752\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140471696900000*\n\n*\n\n\n\n140471696892752->140471696900000*\n\n\n\n\n\n140471696892800\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140471696892800->140471696899856+\n\n\n\n\n\n140471696888720\n\n \n\ndata 0.1464\n\ngrad 4.8284\n\n\n\n140471696888720->140471696888576*\n\n\n\n\n\n140471696888720**-1->140471696888720\n\n\n\n\n\n140471696900000\n\nx1*w1\n\ndata -6.0000\n\ngrad 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0.1464\n\n\n\n140679288689840->140679288691712*\n\n\n\n\n\n140679288689840+\n\n+\n\n\n\n140679288689840+->140679288689840\n\n\n\n\n\n140679288692912\n\n \n\ndata 2.0000\n\ngrad 0.2203\n\n\n\n140679288692912->140679288689696*\n\n\n\n\n\n140679288695984\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140679288695984->140679288689696*\n\n\n\n\n\n140679288695984+->140679288695984\n\n\n\n\n\n140679288687824\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140679288700160*\n\n*\n\n\n\n140679288687824->140679288700160*\n\n\n\n\n\n140679288688880\n\n \n\ndata 6.8284\n\ngrad -0.1036\n\n\n\n140679288691568**-1\n\n**-1\n\n\n\n140679288688880->140679288691568**-1\n\n\n\n\n\n140679288688880+\n\n+\n\n\n\n140679288688880+->140679288688880\n\n\n\n\n\n140679288700160\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140679288700160->140679288695840+\n\n\n\n\n\n140679288700160*->140679288700160\n\n\n\n\n\n140679288690944\n\nx2\n\ndata 0.0000\n\ngrad 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"" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -487,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -542,10 +550,1027 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Neuron:\n", + "\n", + " def __init__(self, nin):\n", + " self.w = [Value(random.uniform(-1,1)) for _ in range(nin)]\n", + " self.b = Value(random.uniform(-1,1))\n", + "\n", + " def __call__(self, x):\n", + " # w*x +b # w*x is a dot product\n", + " act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b )\n", + " out = act.tanh()\n", + " return out\n", + "\n", + " def parameters(self):\n", + " return self.w + [self.b]\n", + "\n", + "\n", + "class Layer:\n", + "\n", + " def __init__(self, nin, nout):\n", + " self.neurons = [Neuron(nin) for _ in range(nout)]\n", + "\n", + " def __call__(self, x):\n", + " outs = [n(x) for n in self.neurons]\n", + " return outs[0] if len(outs) == 1 else outs\n", + "\n", + " def parameters(self): \n", + " return [p for n in self.neurons for p in n.parameters()]\n", + "\n", + " # for neuron in self.neurons:\n", + " # ps = neuron.parameters()\n", + " # params.extend(ps)\n", + " # return params\n", + "\n", + "\n", + "class MLP:\n", + " \n", + " def __init__(self, nin, nouts):\n", + " sz = [nin] + nouts\n", + " self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))]\n", + "\n", + " def __call__(self, x):\n", + " for layer in self.layers:\n", + " x = layer(x)\n", + " return x\n", + "\n", + " def parameters(self):\n", + " return [p for layer in self.layers for p in layer.parameters()]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=0.007657120625740258)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = [2.0, 3.0, -1.0]\n", + "n = MLP(3, [4, 4, 1])\n", + "n(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "41" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(n.parameters())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#draw_dot(n(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Examples for training\n", + "xs = [\n", + " [2.0, 3.0, -1.0],\n", + " [3.0, -1.0, 0.5],\n", + " [0.5, 1.0, 1.0],\n", + " [1.0, 1.0, -1.0],\n", + "]\n", + "ys = [1.0, -1.0, -1.0, 1.0] # desired targets" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Value(data=0.007657120625740258),\n", + " Value(data=0.9629698232963938),\n", + " Value(data=0.6722963345252638),\n", + " Value(data=-0.24414394993705751)]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ypred =[n(x) for x in xs]\n", + "ypred" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=9.182464116048688)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We wil calculate the loss to measure the correcteness of the prediction: how well the neural net is performing\n", + "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + "loss" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# We want the loss to be low\n", + "loss.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.17473429586587344" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.layers[0].neurons[0].w[0].grad # If the gradient is negative, that means by increasing the weight of this neuron, the loss will go down\n", + "\n", + "# We have this information for all of our neurons and their parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w1 grad 0.17473429586587344\n", + "w1 data -0.03862055212227311\n" + ] + } + ], + "source": [ + "print('w1 grad', n.layers[0].neurons[0].w[0].grad)\n", + "print('w1 data', n.layers[0].neurons[0].w[0].data)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# we will itirate through all the parameters\n", + "\n", + "for p in n.parameters():\n", + " p.data += -0.01 * p.grad # if gradient is negative, we increase the weight, if it is positive, we decrease the weight" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w1 grad 0.17473429586587344\n", + "w1 data -0.040367895080931845\n" + ] + } + ], + "source": [ + "# after the change\n", + "print('w1 grad', n.layers[0].neurons[0].w[0].grad)\n", + "print('w1 data', n.layers[0].neurons[0].w[0].data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=8.607967674232228)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now we recalculate the loss # we expecct it to be lower\n", + "ypred =[n(x) for x in xs]\n", + "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + "loss\n", + "\n", + "# It is lower\n", + "\n", + "# You won't get the exact same results as I did, but you should get a loss that is lower than the initial loss (it was randomly initialized) : \n", + "# but now you will do because i seeded the random number generator" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Now we will do backward pass\n", + "loss.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "for p in n.parameters():\n", + " p.data += -0.1 * p.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=6.247012816219952)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ypred =[n(x) for x in xs]\n", + "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + "loss\n", + "\n", + "# Now the loss should decrease more" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is gradient descent, we are doing forward pass, backward pass and then update. As a result neural net is improving its predictions<>" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Value(data=0.8009931947462453),\n", + " Value(data=-0.6030405624037342),\n", + " Value(data=0.7932559750217888),\n", + " Value(data=0.7905048504993913)]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's now do this a few more time with an increased learning rate\n", + "\n", + "for e in range(5):\n", + " ypred =[n(x) for x in xs]\n", + " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + " loss.backward()\n", + " for p in n.parameters():\n", + " p.data += -0.1 * p.grad\n", + "\n", + "ypred\n", + "\n", + "# You can see the output is getting closer to the desired output" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=3.4568357132496788)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the loss here is:\n", + "loss\n", + "\n", + "# see it is very low now\n", + "# loss = Value(data=3.1391947868174866e-07)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's look at the parameters\n", + "# There are a set of trained neural net parameters that will give us the desired output\n", + "\n", + "#n.parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's do it in a proper way from begining like we do in regular trainings" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Data\n", + "xs = [\n", + " [2.0, 3.0, -1.0],\n", + " [3.0, -1.0, 0.5],\n", + " [0.5, 1.0, 1.0],\n", + " [1.0, 1.0, -1.0],\n", + "]\n", + "ys = [1.0, -1.0, -1.0, 1.0] # desired targets" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current step: 0 Loss: 8.590244752121581\n", + "Current step: 2 Loss: 3.251279565120799\n", + "Current step: 4 Loss: 0.4314166532029969\n", + "Current step: 6 Loss: 0.008903970131844198\n", + "Current step: 8 Loss: 0.00017030867419075724\n", + "Current step: 10 Loss: 6.909809892444565e-06\n", + "Current step: 12 Loss: 3.79564352360394e-07\n", + "Current step: 14 Loss: 2.3918872973270876e-08\n", + "Current step: 16 Loss: 1.6092511096719524e-09\n", + "Current step: 18 Loss: 1.3445988501866946e-06\n", + "[Value(data=0.9999971000178582), Value(data=-0.9999999999981305), Value(data=-0.999999999998144), Value(data=0.9999981856078193)]\n" + ] + } + ], + "source": [ + "x = [2.0, 3.0, -1.0]\n", + "n = MLP(3, [4, 4, 1])\n", + "\n", + "learning_rate = 0.05\n", + "\n", + "for e in range(20):\n", + " # forward pass\n", + " ypred =[n(x) for x in xs]\n", + " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + " \n", + " # backward pass\n", + " loss.backward()\n", + "\n", + " # update\n", + " for p in n.parameters():\n", + " p.data += -learning_rate * p.grad\n", + " \n", + " # print every\n", + " if e % 2 == 0:\n", + " print(\"Current step: \", e, \"Loss: \", loss.data)\n", + "\n", + "# After last step of training let's predict again\n", + "ypred =[n(x) for x in xs]\n", + "print(ypred)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current step: 0 Loss: 3.0671681764088587\n", + "Current step: 2 Loss: 0.7522797144376534\n", + "Current step: 4 Loss: 0.21049320414541034\n", + "Current step: 6 Loss: 0.12931817259939735\n", + "Current step: 8 Loss: 0.09173860850980789\n", + "Current step: 10 Loss: 0.07046366092766423\n", + "Current step: 12 Loss: 0.05690191664948921\n", + "Current step: 14 Loss: 0.04755552992867856\n", + "Current step: 16 Loss: 0.04074910543584072\n", + "Current step: 18 Loss: 0.03558485523377747\n", + "Current step: 20 Loss: 0.03154041675170391\n", + "Current step: 22 Loss: 0.028292096705550128\n", + "Current step: 24 Loss: 0.025629064823938723\n", + "Current step: 26 Loss: 0.023408339828180122\n", + "Current step: 28 Loss: 0.021529657545075617\n", + "Current step: 30 Loss: 0.019920702795380423\n", + "Current step: 32 Loss: 0.018528051621906523\n", + "Current step: 34 Loss: 0.01731141003069202\n", + "Current step: 36 Loss: 0.016239832977886108\n", + "Current step: 38 Loss: 0.015289174466104255\n", + "Current step: 40 Loss: 0.014440326294789117\n", + "Current step: 42 Loss: 0.013677975530100375\n", + "Current step: 44 Loss: 0.012989711231352619\n", + "Current step: 46 Loss: 0.012365371303898295\n", + "Current step: 48 Loss: 0.011796557581364723\n", + "Current step: 50 Loss: 0.01127627078763775\n", + "Current step: 52 Loss: 0.01079863225421862\n", + "Current 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"Current step: 998 Loss: 0.00046489443090355924\n", + "[Value(data=0.9920687382052715), Value(data=-0.9920858581792807), Value(data=-0.9881691234740339), Value(data=0.9859137885520525)]\n" + ] + } + ], + "source": [ + "# There is a very common bug in the code above. We are not setting the gradients back to zero. lol\n", + "# The only reason previous one was working because the data is very simple.\n", + "# Also you can see the number of epochs has increased by a lot. Gradients are being set back to zero.\n", + "# Previously they were being added.\n", + "# Let's fix that!\n", + "\n", + "x = [2.0, 3.0, -1.0]\n", + "n = MLP(3, [4, 4, 1])\n", + "\n", + "learning_rate = 0.05\n", + "\n", + "# We are doing gradient descent here\n", + "for e in range(1000):\n", + " # forward pass\n", + " ypred =[n(x) for x in xs]\n", + " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + " \n", + " # zero grad\n", + " for p in n.parameters():\n", + " p.grad = 0.0 # we need to set the gradients back to zero, otherwise they will accumulate\n", + " \n", + " # backward pass\n", + " loss.backward()\n", + "\n", + " # update\n", + " for p in n.parameters():\n", + " p.data += -learning_rate * p.grad\n", + " \n", + " # print every\n", + " if e % 2 == 0:\n", + " print(\"Current step: \", e, \"Loss: \", loss.data)\n", + "\n", + "# After last step of training let's predict again\n", + "ypred =[n(x) for x in xs]\n", + "print(ypred)\n" + ] }, { "cell_type": "code", From 541a0c857bdd61fbdb1a4077458814af21c8344d Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sun, 18 Sep 2022 11:32:21 +0200 Subject: [PATCH 06/20] micrograd_ir complete --- my_code/micrograd_demo.ipynb | 0 my_code/micrograd_ir/micrograd_ir/__init__.py | 0 .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 173 bytes .../__pycache__/engine.cpython-310.pyc | Bin 0 -> 4445 bytes my_code/micrograd_ir/micrograd_ir/engine.py | 130 ++++++++++++++++++ my_code/micrograd_ir/micrograd_ir/nn.py | 58 ++++++++ .../test_engine.cpython-310-pytest-7.1.3.pyc | Bin 0 -> 4168 bytes my_code/micrograd_ir/test/test_engine.py | 72 ++++++++++ 8 files changed, 260 insertions(+) create mode 100644 my_code/micrograd_demo.ipynb create mode 100644 my_code/micrograd_ir/micrograd_ir/__init__.py create mode 100644 my_code/micrograd_ir/micrograd_ir/__pycache__/__init__.cpython-310.pyc create mode 100644 my_code/micrograd_ir/micrograd_ir/__pycache__/engine.cpython-310.pyc create mode 100644 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visited = set() + + def build_topo(v): + if v not in visited: + visited.add(v) + for child in v._prev: + build_topo(child) + topo.append(v) + + build_topo(self) + + self.grad = 1.0 + for node in reversed(topo): + node._backward() + + + def __neg__(self): # -self + return self * -1 + + def __radd__(self, other): # other + self + return self + other + + def __sub__(self, other): # self - other + return self + (-other) + + def __rsub__(self, other): # other - self + return other + (-self) + + def __rmul__(self, other): # other * self + return self * other + + def __truediv__(self, other): # self / other + return self * other**-1 + + def __rtruediv__(self, other): # other / self + return other * self**-1 + + def __repr__(self): + return f"Value(data={self.data}, grad={self.grad})" \ No newline at end of file diff --git a/my_code/micrograd_ir/micrograd_ir/nn.py b/my_code/micrograd_ir/micrograd_ir/nn.py new file mode 100644 index 00000000..7ec8e60b --- /dev/null +++ b/my_code/micrograd_ir/micrograd_ir/nn.py @@ -0,0 +1,58 @@ +from micrograd_ir.engine import Value +import random + +class Module: + + def zero_grad(self): + for p in self.parameters(): + p.grad = 0 + + def parameters(self): + return [] + + +class Neuron(Module): + + def __init__(self, nin): + self.w = [Value(random.uniform(-1,1)) for _ in range(nin)] + self.b = Value(random.uniform(-1,1)) + + def __call__(self, x): + # w * x + b + act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b) + out = act.tanh() + return out + + def parameters(self): + return self.w + [self.b] + + +class Layer(Module): + + def __init__(self, nin, nout): + self.neurons = [Neuron(nin) for _ in range(nout)] + + def __call__(self, x): + outs = [n(x) for n in self.neurons] + return outs[0] if len(outs) == 1 else outs + + def parameters(self): + return [p for neuron in self.neurons for p in neuron.parameters()] + + +class MLP(Module): + + def __init__(self, nin, nouts): + sz = [nin] + nouts + self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))] + + def __call__(self, x): + for layer in self.layers: + x = layer(x) + return x + + def parameters(self): + return [p for layer in self.layers for p in layer.parameters()] + + def __repr__(self): + return f"MLP of [{', '.join(str(layer) for layer in self.layers)}]" \ No newline at end of file diff --git a/my_code/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc b/my_code/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d951a3e08997fcea2e7dd9003cd1e11fc55b0f9a GIT binary patch literal 4168 zcmb_f&5t8T6|d^H+jjeVJYQi03=s)qq@7vM>=&>T7LjlWVh>@-!9p@^SC7Xtw%e)h zA?u;~!bUZaLoSF5Hx9XwKY(M2TZCW_Gh z@4bFq@44r_pMzr2W%zvKuP=3e;4tcCYP;?N|KpC<;c~!{Ox;PL^k+kF83EAlgU zz?M>}i8Vz56jLe7iqo+Z*^`vuT|VWLH11%+R;=Y*;4?CDRyl{6IH$5eSGkATIIr^e zSzJ&#RXDQZqAG@ejB*oKq*QUAi!|x?t!YX)QGQaGvLCbXZP<&-nR5AaNF|bn9Cng( 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zj%f<0pKB$obSp^#OmR#4Z=qFyVa`=LGt(vIr>{xd;Wz|=_acyBZ#43UqFGz~nY@+I wvxGgTN3u7J4AU~_Sje8(pfn}O-WmkrxbMGA|N9})(H9tUwF=y|vsPjKpVCh>M*si- literal 0 HcmV?d00001 diff --git a/my_code/micrograd_ir/test/test_engine.py b/my_code/micrograd_ir/test/test_engine.py new file mode 100644 index 00000000..b7cf5e2a --- /dev/null +++ b/my_code/micrograd_ir/test/test_engine.py @@ -0,0 +1,72 @@ +import torch +import sys + +sys.path.append('/home/ibad/Desktop/nn-zero-to-hero/my_code/micrograd_ir/') +from micrograd_ir.engine import Value + +def test_sanity_check(): + + x = Value(-4.0) + z = 2 * x + 2 + x + q = z.relu() + z * x + h = (z * z).relu() + y = h + q + q * x + y.backward() + xmg, ymg = x, y + + x = torch.Tensor([-4.0]).double() + x.requires_grad = True + z = 2 * x + 2 + x + q = z.relu() + z * x + h = (z * z).relu() + y = h + q + q * x + y.backward() + xpt, ypt = x, y + + # forward pass went well + assert ymg.data == ypt.data.item() + # backward pass went well + assert xmg.grad == xpt.grad.item() + +def test_more_ops(): + + a = Value(-4.0) + b = Value(2.0) + c = a + b + d = a * b + b**3 + c += c + 1 + c += 1 + c + (-a) + d += d * 2 + (b + a).relu() + d += 3 * d + (b - a).relu() + e = c - d + f = e**2 + g = f / 2.0 + g += 10.0 / f + g.backward() + amg, bmg, gmg = a, b, g + + a = torch.Tensor([-4.0]).double() + b = torch.Tensor([2.0]).double() + a.requires_grad = True + b.requires_grad = True + c = a + b + d = a * b + b**3 + c = c + c + 1 + c = c + 1 + c + (-a) + d = d + d * 2 + (b + a).relu() + d = d + 3 * d + (b - a).relu() + e = c - d + f = e**2 + g = f / 2.0 + g = g + 10.0 / f + g.backward() + apt, bpt, gpt = a, b, g + + tol = 1e-6 + # forward pass went well + assert abs(gmg.data - gpt.data.item()) < tol + # backward pass went well + assert abs(amg.grad - apt.grad.item()) < tol + assert abs(bmg.grad - bpt.grad.item()) < tol + +# python -m pytest From 765051b31106a67e9e6947911a385c6710f2ade3 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sun, 18 Sep 2022 11:46:05 +0200 Subject: [PATCH 07/20] micrograd demo done --- my_code/micrograd_demo.ipynb | 310 +++++++++++++++++++++++++++++++++++ 1 file changed, 310 insertions(+) diff --git a/my_code/micrograd_demo.ipynb b/my_code/micrograd_demo.ipynb index e69de29b..217da7a5 100644 --- a/my_code/micrograd_demo.ipynb +++ b/my_code/micrograd_demo.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#%pip install micrograd --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from micrograd.engine import Value\n", + "from micrograd.nn import Neuron, Layer, MLP" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1337)\n", + "random.seed(1337)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make up a dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_moons, make_blobs\n", + "X, y = make_moons(n_samples=100, noise=0.1)\n", + "\n", + "y = y*2 - 1 # make y be -1 or 1\n", + "# visualize in 2D\n", + "plt.figure(figsize=(5,5))\n", + "plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize a model " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MLP of [Layer of [ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2)], Layer of [ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16)], Layer of [LinearNeuron(16)]]\n", + "number of parameters 337\n" + ] + } + ], + "source": [ + "model = MLP(2, [16, 16, 1]) # 2-layer neural network\n", + "print(model)\n", + "print(\"number of parameters\", len(model.parameters()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Loss function " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value(data=0.9219220440708354, grad=0) 0.5\n" + ] + } + ], + "source": [ + "def loss(batch_size=None):\n", + " \n", + " # inline DataLoader :)\n", + " if batch_size is None:\n", + " Xb, yb = X, y\n", + " else:\n", + " ri = np.random.permutation(X.shape[0])[:batch_size]\n", + " Xb, yb = X[ri], y[ri]\n", + " inputs = [list(map(Value, xrow)) for xrow in Xb]\n", + " \n", + " # forward the model to get scores\n", + " scores = list(map(model, inputs))\n", + " \n", + " # svm \"max-margin\" loss\n", + " losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(yb, scores)]\n", + " data_loss = sum(losses) * (1.0 / len(losses))\n", + " \n", + " # L2 regularization\n", + " alpha = 1e-4\n", + " reg_loss = alpha * sum((p*p for p in model.parameters()))\n", + " total_loss = data_loss + reg_loss\n", + " \n", + " # also get accuracy\n", + " accuracy = [(yi > 0) == (scorei.data > 0) for yi, scorei in zip(yb, scores)]\n", + " return total_loss, sum(accuracy) / len(accuracy)\n", + "\n", + "total_loss, acc = loss()\n", + "print(total_loss, acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 0 loss 0.19804839220213744, accuracy 94.0%\n", + "step 5 loss 0.24672248029881824, accuracy 90.0%\n", + "step 10 loss 0.11462603871211018, accuracy 97.0%\n", + "step 15 loss 0.16748079173634225, accuracy 93.0%\n", + "step 20 loss 0.061716686945202125, accuracy 99.0%\n", + "step 25 loss 0.06513144508540747, accuracy 98.0%\n", + "step 30 loss 0.04222444942627202, accuracy 99.0%\n", + "step 35 loss 0.04889118476767827, accuracy 99.0%\n", + "step 40 loss 0.024106844141993013, accuracy 99.0%\n", + "step 45 loss 0.02942947332012279, accuracy 100.0%\n", + "step 50 loss 0.024905708104035043, accuracy 99.0%\n", + "step 55 loss 0.019293939608525292, accuracy 100.0%\n", + "step 60 loss 0.01778576221766042, accuracy 100.0%\n", + "step 65 loss 0.015836073894976793, accuracy 100.0%\n", + "step 70 loss 0.014353628187808278, accuracy 100.0%\n", + "step 75 loss 0.014283006307858672, accuracy 100.0%\n", + "step 80 loss 0.013501075237247298, accuracy 100.0%\n", + "step 85 loss 0.013070661438117028, accuracy 100.0%\n", + "step 90 loss 0.012660005324128556, accuracy 100.0%\n", + "step 95 loss 0.01286610384023592, accuracy 100.0%\n" + ] + } + ], + "source": [ + "for k in range(100):\n", + " \n", + " # forward\n", + " total_loss, acc = loss()\n", + " \n", + " # backward\n", + " model.zero_grad()\n", + " total_loss.backward()\n", + " \n", + " # update (sgd)\n", + " learning_rate = 1.0 - 0.9*k/100\n", + " for p in model.parameters():\n", + " p.data -= learning_rate * p.grad\n", + " \n", + " if k % 5 == 0:\n", + " print(f\"step {k} loss {total_loss.data}, accuracy {acc*100}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualize decision boundary\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.7222875713140986, 2.027712428685901)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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v3agh0iYipYSUct0F2TIl1AyOV0tg5qIbAW8QOw53ZbzpNWIwNEQCEQgh0HxF44rTRiuI6JHxqvZKKKqCHTduxdDvRjBz0Z04nMnofp+h7lF0HmjL6N9AVKiW9qHkUrLgssSswQUosPCSiePHj+PYsWOxv3u9XrS18cWOMjc/7cPEuSm4R7yAjNY9qd1ag/rtNdF2ABvE4SqBtcSSfhNECSzM+DFz0Z3Rxl2/N4DRd5MsGS1jXTzVVLOlClpYu9SyYCl3ScDhLMHWD2yJBSmLTcWWa9ugh3W4h5PMlEpgdtCN1n1NsHL5iIrE6g20uZQouAhHvamDC1Bg4aWxsRHj4ytfNMfHx+F0OuPOugCA3W6H3W7fiOHRJjB9cRYXXxlaUWwuHIhg9J1xzA64sePGrg3bhyGEQMPOumggyMDkhem0w4vfG8C5kxega7qh65c/fv32WlS1V2KmfxaBuSAUi4LKZifK68pix6qXW5hdW313DRldwrI2ZP8IuhFSSizM+OEe8ULXdJRU2FHdXrmhIZaKB4NL9hRUeDl48CB+9atfrbjtySefxMGDB/M0ItpMgr7QpaWPOMsZgfkgBl4fQdfB9rWfzJG67TXwewOY7ptN3UNpFSOVeFcbemMkGlwMNJtceiNfzmq3oGFnnbEnM7gqFif3GCZ1icB8EJDRNg3pLKOFA2H0vjgA3/TCipmkoe5RtF3VnLXj6LQ5MLhkV07Dy/z8PHp6emJ/7+vrQ3d3N6qrq9He3o7jx49jeHgYP/nJTwAAn/nMZ/Dd734Xf/d3f4c///M/x1NPPYX//b//N/7jP/4jl8MkAgBMXZhOfoEE3MMehPxh2BzZKwKXjBAC7ftbUNVWiakL01iY9UMCCC+krquS7n6X4HwIcxM+Q9eW15ah833t6yok52yowFTfTNKgJBSRUbNGqUuMn5vExPkpRILRzcuKRUFtZxUaL6vHwmwAAe+lonurv5+6puP8M32xWjbLxyh1iYHTw1AsyprwRhRPoloruZRucFlihuAC5Di8vPbaa7jxxhtjf1/am3L77bfjwQcfxOjoKAYGBmKf7+zsxH/8x3/gb/7mb/Dtb38bra2t+OEPf8hj0pQT81M+TPRMwzcZfcM2NOMgo0XZbK2u3A9wkRACzoZyOBeXTqSUePfx95LPrAigqi29MQa8AUPXWR0W7DjcldZjx1O7tRpTvTOJLxBATWdV2ks0Upe48OLFaK+pZfSIjonz05jomV7zfa5qd6F9fwtUS/S5Zgc9CHiDSZ9n5K0xVLW54i6JES0xUt02Z9IILs89oZsmuAA5Di+HDx+GlInfDeJVzz18+DDeeOONHI6KCBh5ZzxabyTNpRggg+uzTAiBxl11uPjacNJr6rbVpPe4BmdRsrXfo7TSgbarmzH4+kjc70NplQMtexrTftyZi7NrgssKcb5/s4MehBbC2PHBLghFYNpANeDQQhi+6QXD/Z9o8ymU4HJJcQQXoMD2vBBthNkhz6VCaRkEkdLq9JcxjNK16Akcz4gXuiZR4rSjtrMa9nLbiuuqt1Qh6Atj7MzaACYUga5DHSipSG8je3lNKRSLAj2SZLOuACpbsvciV7e1BiXOEky8NwnP6BwgAVuZFXXbalG3tTqjZamJnhTLf/FIwDe1APeIF1WtLoT9qZflAKR/Eow2jUIKLtFZl+IJLgDDC21C4+dSdDZORET3adjLbKmvzYDfG0DPs30I+y+9IXpGgfGzk2i6vB6Nu+tjSxTR2ioNqGx1YurCDBZm/RCKgLOxArVdVbCWpL8nR7EoqN9eg7Ezib8+QhGo7UpvRieViroyVNSVQUqJsD+MoC8MRRGGN/QuJ6WE321s+SueqQszqGp1weqwGtrwbC3hSyitZaSDc64Vc3ABGF5ok9HCGhZmDBzRXU0A1hIr2ve3ZH9QACIhDeefjvYKWmFxRmX0nQlY7BbUbV0ZHEorHVkdU9NlDQh4g9H6K8tndEQ0MG091JHVjtVLgr4Qhn83GqutAwCqTUX9jlo07qpLa1+JECLpcnXycUT3udRsqcL8ZPLNy7ZSK8pqcr/xkszFSHBZuZSTO6mCi5kxvNCmkkm3YkUVqOmsRuPuuoxmNIyY7p+JnYpJZOzdCdR2Ved0g6hQBDoPtsM7OofJCzPwewLRei0tTtRtrYatNPuzTkFftHP26gaUWkjD6NvjCHgC2HKgzdC/WwiB8voyzI3PZzSWpf08VW0ujJ2dRHDxmHU8zVc2crMurWBkqaiQgotZZ10AhhfaZCx2FapNhbZ6hmMVq8OKXb+/FVKTsNgt6zoSbMTMRXfKa8KByIZsEBVCwNXshKs5+sIW8ocx1TuD88/2Qw9rsFfYUbu1GlUtLghl/W/ew78bTdo5e3bQg+r2yth4kokEI9HAkaGlo8+KqmDHBztx4YWL0WJ6i6tYUkYDXuu+Jh6TphVSBpdl/YRy7dLJouIMLgDDC20yQgjUba2JbnRNom5bDaz2janlAiBlmEr3umyZn/Kh57n+FcfIw4EI5id9mKqbwbYPbFlXB+twILxiqSguAUz2TKcML1JKXHjhIkIGauDEew7VqqKmsyp2k9Vhxc4PbYVveiG2gdpeYUN1exUsNlbYpbXSa4SY05EAKN7gAjC80CbUsLMW7mFPtADZ6jdNEd1HUp/mMeP1spXZDL3p2nK0WTieSEhDz/P9CU8fzU/6MNg9go5rWjN+joA38bJMjAQWPKk34fqmF6LVcI1YdULLYrNg2w1bYLGtfEkUQqC8tozHoSmpTDs451IxBxeA4YU2IdWqYseNWzHy1him+2ch9ei7mFAFajur0Xxlw7pmEzJR21WdcoOoo7IEDlfJBo0ImO6fhR5Ovqlvun8WLVc2ZtzvyWjnbMXA8tTskCd13R4RnVUTQiCwuJ/H1exEVZsr50uDVJwYXPKD4YU2FSkl/J4Awv4wyuvKoEU0uIe90b0tNhVWhzXlTEBgPojp3hn4vUEoqgJXcwWqWtf35lfV6sLE+ano/op4zy+A1r1NGT9+JjwjSTo+L5GAd2Ie1W2VGT1HaaUj9R4kAUP7XbQUQWvxoaCoClquTL/4HdFqqYLLkkTVbXOpmIMLwPBCm4h3bA5Db44i4Im/oTPsj0RnY/pmsOPGrWtqeEgpMfruxJrKvO4hD0beHMO2GzrTmhmRUl6q26IIbL+hExdfG4J7aDE0LD6H1WFBxzWtqKjf2M7KRjtLywxOcC1R1GhtmdF3Uu9BSsVelnqPkpTIWZ0e2lyMBBej/YRypViDC8DwQpuEe9iL3hcvGrp2qbv0tuu3rLh9qncmYWXecDCC88/04rKbdibdzKmFNUxemMHUhWmEFsIQqkBliwsNO2pRWuVA18EOBH0heEfnoGs6SpwlcDaW5+VIbmmlI/FM0DIlzvQq+a7WuKsefk8Q7tXLPov/5M4D7YaqBddsqUoZgoQq0u75RLTa0skigMElXxheqOjpuo6Lrw0Zv4OMztIE54Owl0ffNKUenXVJdp9IUMN0/wwadtTFvSQSjOC9p3tXNPyTmsTsoBuzg250vq8dVa0u2MtsafclyoXartSNEx3Okoy6Pq94GEWg831t8IxUYvLCNPxuP4SyWFtmW43hNge2UhsadtVh/GziCsEtVzZmrTcTbU7Lj0QzuOQPwwsVPc/wXEZHjOcmfbHw4ptZQMRAH5uZi+6E4WXg9HD0hNNqizMN/S8NouwjpbA5Nu6IdjKlVQ7U76jFxHtTaz+5WHG3/ZqWrMwKCSFQ2eJcd9+k5isaoFoVjJ2ZXHFKSrWpaLmyEbVd1esdKm1i2QouG1HdtpiDC8DwQptAwBvIqHv00ikkYP11WEIL4WjJ/WTPJyWmemfQfHmD8UHmWMueRthKrRg7O7kivJXXlqF1b9O6Z12yLdpxux5122rhHZtDJBiB1WGFs7EcisLTRJS5bAaXYg8WG4HhhYqalBLz0wsZdY8uq750pNFofZXV3Z+XzE8lPwa9ZG58Hiig8CKEQP32WtRtrcHCrB9aRIe93FaQm16llFiY8SMcjMDmsKKyxcny/ZQVRoPLJQwuucbwQkVt9J2J9PvcCMDhWrmXw+EqgaOyJGXH4poEyxJGGwVm2lAw14QiCroJ4czFWYy8Pb6i0J+9woaWPU2oNHDMmiiRdIJLsXZwLkScR6WiFQ6EMXY2+emTNQSgWBRsua5tzada9zXFTsDEu19ZTSmqWuKfZFk+i5PsucsLOCAUqsmeafS/MrSmQnFwLoTeFy5iZsCdn4GR6a3uVwQwuBQKhhcqWjMX3WkvFykWBfVba2ArXbtptqKuHNuu3xItZLdKVasL267fkrBRYUmFHeV1ZYnDDwBIoHZr/k8ZmUkkGMFQ90jSawZODydscUCUSLxGi5kGF8o+LhtR0QothCFEtDCZUXpYx9jZSUxfdGPH4c7YaaMlzoYKXPGRnZgbn0fAG4RQBVxNFbCVpt4D0nFNK8491ZOwg3Lr3ibDx4KXW15zIpcGx7ZsyPOkY7p/NuX3V4/omB3yoGZLVfILiRZlO7hw1iX7GF6oaKk2NZN9ugCiS049z/fjsqM71mz6FELA2VgBZ2NFWo9pL7dh15FtGH13AjMX3bHTTKXVDjTuql/XMeHl09q5sNA7iLbG/oILMIG5YMqTZEIg/hF1ojjSDy5RDC4bi+GFilZVq+tSRdx0yeieCe/4PFxphpRkbKU2dFzTitZ9zQgHwlAtCqwlmdd1WV6iPJdKu9oKMsAY6SclASgGG0DS5pZZcCllcMkD7nmhouVwlUQb+mX6viUAr5HmhBlQLQpKyu2mCC5Llp5ro5apjHA1VaTe1yQBVxPfRCg5Bhdz4cwLFbUtB9rQd+oivGPzl5YX0ihYp+uFeXQ5XlO4XAu/uVBwMzAVDeUocdqjy0IJunGX15QWXDE9KizxgksMg0tBYnihoqNHdMwMuuEZ8ULXJEqcdmzpqML8pA/hQATWEgssdhVjZxL3wAEASKDEabxL9EZJ2s02R/SJAVj3lBZcgBFCYOsHtuD8M70I+cJrPl9SYUfnwY41twfnQ5i8MA3PqBdSkyitcqBuWw3K68pY2G6TSRRclPp2BpcCxvBCRWXB7UfPs32IBC+V6Z+bmMeknEbj7jq0Xd0MIQR0TcdkzzS0cOJjjEIRqOmo3IBRG5e0m20OKfXtcQNMIbCX2bD7w9sxc9GNmYuzCAeiFXZrOqtR1eZasy/GPexB36mB6ETN4mxNyB9t31DTVYX2q7PTr4kKH4OLeTG8UNGIBCM4/0wftPCq/kKLb1BjZyZhdVhRt7UGiqqg/ZpW9J0aSPh4bVc1w2IvnP9FjFT6zJnevrgBpg35n30BANWiom5rDepS1MkJeAPoPTWwdolp8e/TvbNwVJSgfkdtbgZKBSPT4LKEwSW/CueVmWidpvpmUzZQHHt3ArVd1RBCoKrVBeX6LRh5cwx+z6Wy//ZyG5qvaERVW/xqufmQfm+V7JJdKPgAY8Rkz3TKa8bPTaJuew1nX4rYeoLLz3v9DC4FgOGFioaRMvDhQAS+6QWU15YBAFyNFXA2lCPgDSLsD8Nit8BRWVJQb1z5Di4xXZ2mDzAzQ56Um7XDgQj87gA3+RappMFlEYNL4eNRaSoaqWZdYtet2ucihIDDVQJnYwVKqxwMLnHEnmPxOZfGsDSm0q62gjpCHc/MoBta0NjPiK6zpHsxShlcujoZXEyCMy9UNOzlNoT9a0+crLmuLPPaKhsp/W62ubWrcjukfyLhDMzSmAtxBsYz4kX/S8Y3GNvKUrd7IHNicCkOnHmholHbVZ3ymtJqR0Eef14ts262ufyIPk+yGZhCLGIHAFJKDL05ltZ9gl62Eyg2SUsMpAguSxhcCgfDCxWNqlYXympKE1fUFdHmh4Uu06ZwuWXeAON3BxBMs7dRgOGlqKw3uJSolQwuBYbhhYqGUAS23bAl7ikhW5kN22/ojG3ULVSFGVyWmDPAGFlKXE2wF1LRYHApTtzzQkVFtajoPNCOlj1heMfmIBcr7Jqhcup6u9nm2p92ORB9YV+IjSXRHphSFE4V3kxq9aTbMZwKU6rgsoTBxXwYXqgo2RxW1Ham3gNTKLLVFC5XApobP+/1xwLMWfd57KrcbooAU1rtgK3UitCCgRkYAVS1VcLmMMembkrMSHARjnoGF5PishFRnmWrKVwuLT3HpVme0thYEi0hAYXRiVoIgeYrGw1dW15Thvb9LTkeEeVa0jYaDC5FgeGFKI+y1VtlI6QbYJbXgAHyG2Cq2yvRelUT4q4cCqCsphSdB9ux/YOdUC25f1kMzAXhGZvD/LQPUhZm53KzMlpiYOlnd3rEjld/HcQP/mUevh4ddnFpzxyDS+HishFRnmS7KVyuXX9UQYlamdYSknXPQMF0oq7fVovqtkpM988iMBeEoipwNVWgoqF8w/ZD+aYXMNg9goWZS/uUrCUWNF5WH2tbQZlLJ7gseCV++QOg723fpROKEph0zmC2rRwNjcn7ZFF+bUh4uf/++/H1r38dY2Nj2Lt3L77zne/guuuui3vtgw8+iE996lMrbrPb7QgEAnGvp81FC2uYueiG3xOAogo4mypQUb9xbz7ZYrZutuN+L557XMNVOzRMvyKwMBrCNyxhXH2tDVf/nsRZmCPAWOwWNOysy8tzz0360PNML1ZPtIQDEQy+PoKwP4zmK4wtb9Fa6RR1DAcl/vVrEtOji8F/2fck5NVR/q4XuqMMiqvwa0JtVjkPLz/72c9w7NgxPPDAAzhw4ADuu+8+HD16FOfOnUN9ffyy5k6nE+fOnYv93WxvTJQb0/2zGHh9GFKTsd+UJs5Po8Rpx9YPbIHdJFVRzdjNtr6kAnNvj6LvVDC62Lz4mv/y4yG8+mvgj+9SgKvNEWCWSCkxNz6PiZ5pLMwsQAiBisZy1G+rzXpfIyklLr46tCa4LDd2ZhLVHVUoqbBn9bk3g3SrUb/1ghVTw/F/IRYAIIFIzwxs+5tzOWxah5wv7v7jP/4j7rjjDnzqU5/CZZddhgceeAClpaX40Y9+lPA+Qgg0NjbGPhoaGnI9TCpw7mFv9MVfW3z1l4j9thSYC+K93/ZCCxvrW5NPZu1mqw14YZ9YLNy2fJVKAroGPHK/jpkxmXAPDICC2gMjpcTg6yPoea4f3rE5RIIawoEIZi66cfY3PZi8kLr7dDrmJ30I+ULJLxLAVO9MVp93M8ikGnX30ym+FwCkOwiZQY0g2hg5DS+hUAinT5/GkSNHLj2houDIkSM4depUwvvNz8+jo6MDbW1t+NjHPoZ33nkn4bXBYBBer3fFBxUXKSVG3kpS3l1GC5FN989u3KAyYNZutlKX0FJ07NZ14PRJFQDiBph4jRyB/AWYqd6ZS0Fh+WzI4p8HXx/B/LQva89nqGKvBPweLo+nI9OijpPjxvaJyUDh/0K0WeU0vExNTUHTtDUzJw0NDRgbi/9mtHPnTvzoRz/CL37xC/zP//k/oes6Dh06hKGhobjXnzhxAi6XK/bR1tYW9zoyL78ngICB8u7jZycLthuwmbvZyvkQEErxdZVA98shLK/CCxRmgJFSYvzcVMrrBl4bztpzGq3Yq7Cyr2GZBJeFeR33fWkOutHuD1YeyC1UBfedOXjwIG677Tbs27cPH/zgB/Hv//7vqKurw/e///241x8/fhwejyf2MThovHMsmUMkaOy3n3AgggvPXyzYAFOIwUVGdGhDXkTem0bkwgz0eDMEmrGjvHpk6Rh1YQeYkC+UegkH0dkSvzc7MyHOBmMVe11NPJprRCbBRdckHvjqPPwDxn6eRakFwiQd6DejnIaX2tpaqKqK8fHxFbePj4+jsdHYrnqr1YqrrroKPT09cT9vt9vhdDpXfFBxsZYY31c+Nz6P8bOpf6veSIXazTYy5EXouQFEzk1DG/JCu+hB+NURhF4bgQxdCoyi1MALuAAc9cuvK9wAo+vG66pMXcjOHhRbqTXacytJ01CLTUVVe2VWnq+YZdpG4ydPLCA4ipXLhEmoXVU8LFLAchpebDYb9u/fj5MnT8Zu03UdJ0+exMGDBw09hqZpeOutt9DUVPjdgCk3Spx2ONI4sjjZMw2Z4g1KSgnPqBd9Lw+i57k+DJwehm96IesFwwqtKZwMaYhcmEHw6X5o56YBfe0GaOkNIvzGaOxrKOwqRF2Sbt2L96++tgQlauWKInbL/22FEmDspTYIxdibkm96IWvP276/BWXVi6fGVj29alGx7YaNKZBnZutpo+H9nZ78Z3gZdUc11IbybA2bciDn/6ccO3YM//zP/4yHHnoIZ86cwZ133gmfzxer5XLbbbfh+PHjseu//OUv49e//jV6e3vx+uuv47/8l/+Cixcv4i/+4i9yPVQqUEIINO8xXv8iEowguJB4WSAciODsb3pw4fmLmB10wzs2j6m+GZx76gL6Tg1A17Kz7FRoTeFkIILQK8PQ+j3Jl4IkIOfD0CcubVi1bq8G1MQvF+EqK94assbGnTLAAHkLMIpFQXmdse7iRkOOEapVxY7DXdhyoA3ltWWwOqwocdrRfEUDLrt5R9aPZxeb9bbR0OdVQ7Mu6rYqWOJ0pqfCkvPw8vGPfxzf+MY38MUvfhH79u1Dd3c3Hn/88dgm3oGBAYyOjsaun52dxR133IHdu3fjD/7gD+D1evHiiy/isssuy/VQqYC5GivQ+b40NmMneJGSUuLC8/2XTnXIlde7h70YfGMk43EuKcSmcOG3JwCD+4cAQBuZi/1ZOKywXdsMUbPqDVYVUDtcKNvXAggRG3PKALOqD9JGB5jWvcbCsLMxu799C0Wgur0SOw534co/3IXLju5A4+56WDPofL2ZZKOoo7VCMbRiJExSL2qzE7LIGmt4vV64XC48/8efQbmVxZ7MbGHWj5kBNyLBCKwlFlR3VOHi6SEsTPuT3s9iV3HlH+6O+1uzd3wePc/2pXzuK/5wV8adhdPtrbIRwUWfDyH8cnqnZ0SpFbaDrWtul4EI9PkQhCIgXHaIZTMy4/5oqYLrj0ZvC2juxTYCABBdgtlVuT36OP4JoDf6vdAnBgAA4Tej1yz0Dua8iN2FF/vhGZ5L+HmhCFzxkZ2wlnDTZj5lqxr1Sw8HUHYu8fc7+uAKbB9oBwQgZwPQZ/yALiEqbFDqy1b8rFP2hfw+PPT3N8Hj8aTcv8rvBBUcPaLjwgv9OPubHkycn8LMgBvj703hzK/PG9pAV7etJuF0v3vIY2jd2z3kSXfYAAozuADRF+K02dW4N4sSC9TaUijVjjUv5ktjXt5vKdMZmFzPvnRc0wZHZZy9VAIQAug62M7gkmfZrEYdrrZCVNiS/v9v2VoFBCIIvzSM8Btj0AY80dN4704h9NwAtMns1f6h9WF4oYLT9/IgPKOLvyFJrNhM6ptagL0i8bRueV1Z0t41kZCWet1bAFo4/X0v2QguS7JfxyX9CVa1KbMlk+UBJl4naqAwAozFpmLnjVvRelUTSirsEIqAalVR21mNXR/eDlczTy7mU9arUZe6YL2qEWIpsApcCjICULdXQ6kvQ+j06KXKustee6BJRN6cgD6bfOaXNgYXWqmgLLj98Iwkr5IcnAuhZU9jrEEjAFgdVtRvr0HdthooSaZ27WXW6AtWsvdyCdjSrO+QreDy3BN6TgrQCWcaDeZEdI+LUm9sU2s8DQ5ntJnjEzquP7q2EzWwsLIbdRdifZD0iQFY95TGeiG1IXd9kBSLgvpttajfVpuTx6fM5KoatbCqsF3dBH0uCH1iAdD06M96YxmEVUWkzw2Eku8Li1yYhe0abq7ON868UEGZHTS2rCMlsPvD27H3lsuw549244qP7ETDzrqkwQUAajqrU05CKBYFVS3GTxuk0xQuamODCwAIpw2i3NhGRFFug/XqxnWv76czAwMgr0tIVDg2ohq1UmGHZWsVLDtqoLY5IazRJdLlm9QTkR72PCoEDC+Uc7qmG66fEglGDF03O+iGFtagWlVY7BbDxaRKKuyo3Vqd9JqWPY1QDNbbSLebbT6CCxA9bm65vA6wKAnDoahxwLq/CdZrmyGydPrFaIBZ3ciRAWZzynsbDYPNXWWqdhmUcwwvlBNaWMPomQm89X/OoPvf38Eb/9/buPDCRcxPJd/wZjV4wsfvDmTcSbrtqmY07Kpbs6lXtSpo39+Cuq01xh4ng262wMYHl9jYym2wXdcMpbF8RYARVSWwXt0I275GKJUlhoKglBL6bACRc9MIvzuJSJ8bMhA/eGYrwAD57URNGyOvbTSs8TepryZsfOvMN+55oayLhCJ477e9KzvpSsAz6oVnxIuOa1tRs6Uq7n1rOiox9u6EoefxewIYf28KzZc3pL54GSEEWq5sRMPOOnhGvdCCGqylVriaKlIuOy3JtJttvOCykYTDCutldZA7a6Jr+xYlNmVulAxpCP9uHNIbvBSCJKD1zkLtrITaWbkmAC3tgQGi//Zke2CkfyL6NVy1B6YUbVjoHURbY+72wFD+FEIbDbW5HFqvO+k1wmWHyLCMAmUP4yNl3eDrI/G7QC+uHF18dQjB+fhtXe3ldtR2JV/WWW6yZzrjkv4Wm4qajirU76hFVasrb8FlIztELxGqAuGwph9cdBltHbD0/V1+GgOA1ueGNhh/w3WDw7miiB2Q/gxMvjpRZ5MW0TE76MZEzxRmhzxZq+hsZoXSRkNtcQK25P9PWLri/+JFG4vhhbIqHAhjdsiTfFOsACaTNLxru6rZcIDRQhoiCZYrciHTpnCFFFzWQ59agJwPJ/3+an1uyARvyEYCDICiDDBSSoydncRb/+cM+l4axNAbo+g7NYA3f3kGE+enst5XyywKqY2GsKmw7m+CKF1clFh+nFoVsFxZD6WaJ40KAcMLZZVveiF1SREJzE3MJ/y0UATarmo2/JzZ7D+TzHqawgHmDy4AoI2mPo2BiB6tTJpAqgATr5EjYP4AM/ruBEbeGoMeWRns9IiOoe5RTLxXWN3QN0IhttFQSq2wvq8V1n0NUNucUFucsOyuhe36dqjrKB9A2cXwQlll+JfHFNcJRaCivizlsWlHZQksG9AXZr1N4YohuEhdGq/Um6LI33oDDJDdPkhaRENoIbwmWKyHlBKRkAYtoiPsD2PsTPK9XCNvjyMS2rhZxHxb/n0rlOCyRAgBpaYUlu01sOysgdpcwdYABYYbdimryoxMqQoY6upbv6MOcxPJTyclq6abLdnqrWLm4AIAWr87eTfq5VLsG1gSLWKnxN3Eu6KI3apNvNY9A7EiduvZxLsw68fYmQm4hxf36Qigqs2Fxt31cKRT2G8ZLaJh/MwkpvtnEV5c0rQ6LCkDu9QlZgc8qNtm7LSbmRV2NWoyA0ZJyipbqQ3OporkMyYSKWutAICrqQLNVyyeJFr+eIt/rt9Zi6oct65ncImSuoQ2lLzycYxVMbQvYHUfpHRnYNbbido7NodzJ3vgXl7RWUYLJZ77TU90CTQNUkpM9c3gzV+ewdjZyVhwAYCwP/WMihBA0BdK6znNqNCrUZM5MLxQ1rXvb4k2tEsQYFr2Nhn+rbZxdz123NiFyhYXLDYVqk2Fq8mJbTd0onVPk+HidJlgcLlE+sMpl4KWWLZVG96HlK8Ao0d09L00EF3mXD0jIgFdk+g9NZDWJtqRt8Yx8NowpNHZqbVPC9Va3C/JDC6ULcX9fwrlhc1hxa4j26J9hpZVqi2rKUXX+zvQsCO9PjLltWXoOtiOPR+7DHs/dhm2vr8DzobMmgYalc1utoC5g0taBKA2V6R1l9QBBlkPMLNDnpTNN8P+8KUGoSnMT/kwfm7S0LUJSaCyNbcziflkhjYaZB7c80I5YS2xoG1fM1r2NCIS0KBYBCw2c/24La+cC8RvCrfsagArK30uKYYXWuGwRlsLpNjQGuvYm6blReyAS4XsoqJF7C6NZXEPzKJ4e2BS8U0vpG7QKaLXVRroLj3RM5368ZIR0WXSTPfZFDqztNEg8+DMC+WUoiiwlVpNF1xoJaEIqK2pZ1TUtuJ6YzG6KumbMlAiYMUDr/xveW0ZtlzXlvByMzNbGw0yB76jEJEh6pZK6LMBSE/86shKSwWU2tK4nys05XVlmOpNXCgRACCNnYoDjIccIHq8v8RZgkggDKvDipotVSivK8vp/q18MWsbDSp8DC9EZIhQFVivaoQ26I22AAhFm2KKMivUdheUpnLTvAFXtjph6VYRCSZo7CkAe5kNFfXG9lY5Gysw1TdjaPal+YoGuJqKfwahGNpoUOFieCEiw4SqwLKlEmqHCwjp0YVni2Ka0LJEURR0vb8DPc/0QdflytAhANWioOtQh+F/V922mtQzOQDqttfA2ZjehmYz2uxtNCj3uOeFiNImhICwqxBW1XTBZUl5TRl2/f521HZWQ6jRf4NiUVC3rQa7P7wdDpfxzbMOVwna97dE/xLnyyFUgfZrWtC6N7fH+wsB22jQRuDMCxFtWiUVdrTvb0Hb1c2QuoRQRMbhorarGiVOOybem4JndA5Sl7CX21DbVR0tG7AJysuzjQZtFIYXItr0hBCx2Zf1KK8tQ3ltdJOvlLLoZ1mWK/SijtIfhjbkhTY6Hy24aFehNldAbXVCGGxnQYWD4YWIKAeSBRcpJTyjc/COzkHXJRxOO2q2VG1Ik9FcKPTgonuDCL8+urI3V1CD1ueGNjwH2zVN0VpGZBrm/D+FiMikAnNB9DzXj5AvdGl/jIy2F2i9qgl1W83VmLHQg4vUJcK/G0/cVDSsIfzWBKzXNm+qmTKzY3ihnJBSYmbAjcmeafjdAQhFwNlYgfrtNbFpdaLNJhKK4L2nexEJLjZqXPZ+KqXE4OsjsNgtqDJJmwAztNHQJ3yxY/1xSUDOhSC9QYg0NmlTfjG8UNZJXaL3xYsr+sJIXcI97IF7yIPWq5pQvy29/kYUfXOT8yHIoAZhUyEqbPxN0WSm+2YRCSTvMD3y9jgqW5wF/71dT3BZ3kYj15tz9Rm/odYN+owfCsOLaTC8UNaNnZ2I39Bu8cVj6I1RlFWXoqzaHNVYC4E26YPWMwO5cOmNT5RaoG6thlrPmSyzmO6fTXlNcC4IvyeA0krHBowoM0mDy6JCCC6GracvFeVF8Z/dow2l6zomzk8nv0gAE+enNmZARUAbnUfkzYkVwQUA5EIEkbcmoI2k7nwsdQl9egHa2Dx0dwC6rkNGdEiNZdc3UsKKvqtoyZY58ixlcOnqLKjgolTYUgcTCQinPafjoOzizAtlVcATTP3CKwHv2PzGDMjkZERH5FzyoBc5Nw2lvgzCsvZ3ESlltJx/nzthR2hRaYfaUQnVJH2JCoWu6Zgd9GC6fxZh/6U+RVVtroQ1XawOy6X9LklYC/zki1mCCwAojeVAzyygJ0kwdhVKTeHOdNFanHmhrJLS4Nyr0es2OX3cl/iUROwiCT1BGNR6Z6Gdn0kYXABAuoOI/G4ckX73Oka6uYQDYZx9sgcXXx3C/KQPwfkQ5id9uPjqEM7+pgfhQDju/Wq7qlM+dmmVAyUVhTkL0NbYn3FwWbLRS0XCqsJyWV3iCxQB6+X1Bb/HiFbizAtlVUmFHUIRkMl+yxHRF2hKTS4dp02WXwSg+0JYXWZL+sPQ+j2Gn0u7MAulqoSbFlOQUuLCCxcRmI/fXTswF0TviwPYcWPXmjfE6o4qTJyfQnA+lPB72rKnMdtDzor1BpcStTLt4KJPLyAy6IWcDQAAhMsOtdUJpa40rbChNpRB2BoR6XVDugOx20VtKSxdlVAKNCxSYgwvtC5SSnjH5i4diVYF7OU2BOaCid9wJVDH00bGKMZeoIWydhLVyF6YlQ8CaENehpcUfNMLWJjxJ75AXrqmrGblUpxqUbDjcBf6Tg1gfmoBEIvZVAKqTUXHta2GO1lvpFTBZUk2g0vkwsya8C1nA4jMBqA0l8OyqzatAKNUOWDb74AMRiDDeqw3F5kTwwtlTEqJ/lcGMTvgSWu3fnVHJVzNxd9ZNxuU2lJoF1PMnkhAqV07kyUX4i9dJHscfTaQ+rpNzjPiNTQb5h7xrgkvAGAtsWLHjVuxMOuHZ2wOUpNwuErgaqmAEieE5puR4CIc9VkNLtqkL+msoT4yD91VAnXZ64iU0e7gIkXgF3YLBCdaTI/hhTI2fnYyGlyAhC/ky5eQrKVWNOyoRd22Gq4vGyRcdogKG2SSZQZRboOoXDtbIvk1zgldk0ayC/Qk+4yA6NJpoS+fLp0sAjYuuACANpB6uTNyfgb6QhjCqkCfDUDO+KPfFJsKtXWxZxFnVooWwwtlRNd1jL+X+rhzeW0ZWvc1QSjR5SSGlvQIIWDd04DQ66OAP84plRILrHsb4n5dhUizdIUAlDghiFYqqbCn3G8uJVBi8qO3y49Eb2RwkbqEdMffT7RCRIceb1YypEHrdUMbnYftmmY2XSxShTdHSabgnw0YqkUxNzGPuYn56EZeBpeMiBILbAdaYNlZA+G0AXYVwmmDZWcNbAdaIEri/w6iewy8ASwnAbUtz8XCTKCqvTL10oQiUN1euTEDyoFsBJcleStAF4ggfGYyP89NOceZF8qInuw00SpD3aNwNTlhL7flcETFTagK1FYn1NY03gjSLHSmdlVys64BFpuK1n1NGHx9JOE1bVc1QzXpkkW2gstzT+gZBRehCIhSy5qijGmTgJzyQ/rDEA4rpD8c3QemCAhXScoASoVtQ2Ze7r//fmzZsgUlJSU4cOAAXnnllaTX/9u//Rt27dqFkpISXHnllfjVr361EcOkNJRUpBFEBDDVO5O7wVB8BqfLRYUNlj31sHRW5XhAxaNuaw22HGiDtXRlMTlbqRVbDrQZqudSiIwGl0uyG1yWqG3Za0ypjc8j9PooQi8OIdw9jvDrYwg9N4BI36zxulRUcHIeXn72s5/h2LFj+NKXvoTXX38de/fuxdGjRzExMRH3+hdffBH/6T/9J3z605/GG2+8gVtuuQW33HIL3n777VwPldJgLbGissXgi5MEfDMLuR0QraE2GThyW2GD7boWqHXr648kpYQ+F4Q24YM+409e5yeJpT0S8Vz6Tb8wVLdX4oo/2IkdN3ah82A7dtzYhcv/YKdpl4vSCS7R70VuggsAKM0VENXZmQXU+jyxOjExER1arxuRdycZYEwq5+HlH//xH3HHHXfgU5/6FC677DI88MADKC0txY9+9KO413/729/GTTfdhL/927/F7t278ZWvfAVXX301vvvd7+Z6qJSmlr1NUCxG65CYZ4p2+QkLIHrKIl5NCyD+i/iSZG/EG0FtcUZnX5J86a1b1z/bos/6EX5lBOFXRhB5awLhN8YQen4AkYuetN4Y4r3hXfqaRr/GKwLMqu/F0hsusPZ7mCtCCJTXlqGq1YXy2jLT7uta3a8IyF9wAaKvF9a9jVA7KwHrOt+mkgRpfcy3NtiQKeQ0vIRCIZw+fRpHjhy59ISKgiNHjuDUqVNx73Pq1KkV1wPA0aNHE15P+WMvs2HrBzpTXwjA2XBpFkBKCe/4HHpPDeDMk+dx/pleTPXOQEtxtHQjxGs6l+mLOJDfbrnCpsJ6dSOwtKFX4FKQUQQsV9RBiVOHJB36jB/hN8aiR7mXC+vQemag9aS3XNjgcMa+dktfy3gBJrZ0sfg9WfoeWfeUxr53GxVgzC7bP/PZIhQBS1cVbB9oh/VAC4QrB6e3BBAZ8mb/cSnnchpepqamoGkaGhoaVtze0NCAsbGxuPcZGxtL6/pgMAiv17vigzZORV1ZtOBckl84hSpQsyX6G76u6bjw/EX0PNsP97AHfncAcxM+DJwexruPvxetzJsn6b+IRxVicFmilNlgO9gKy94GqC1OKM0V0VNK17dDbVhfJVcpJcJnppKex9YGvNBXB5sUjAQYAAwwWWCGsC4UAaXcBsu26qSvM2uoBi6WWBu8yRRMf9roxIkTuPfee/M9jE2t45pWvPfb3rXBQ0Sn1bsOdcBij/6oDXaPwDu2WLZ+1ZteOBBGz7N9uOymHQm78uZKZsGltKCDyxIhRLRjdJa7RsvZABBIcSJEANrwHJSdNWk9djTAeHH9UQUlaiUCmhs/7/XjT7scAEpx1n0euyq3QzjqIf0T0e9Rbx+U+nboEwPRAIM2LPQOoq2xH4NjWzL+d+bLRgUvs4R1pbIElj0NiLw9kbJZqVLjgNJYjsg7qY9Km2lJmy7JaXipra2FqqoYHx9fcfv4+DgaG+M3H2tsbEzr+uPHj+PYsWOxv3u9XrS1tcW9lnLDYrdg54e2Yqp3BpMXZhBaCEG1KKhqq0Td9ho4nNGNd+FABNN9s4kfSAKhhTDcw94N3fQYL7jEmDy45JKh9gNysblkBtYbYIDo99TMAWb5HpRcMktYV2tLoVzfDn3cB90dgJwPQfrDQEQCCiBqy6BucUGtsENqenT2JVXQqV/fZnXKj5yGF5vNhv379+PkyZO45ZZbAEQrs548eRJ333133PscPHgQJ0+exD333BO77cknn8TBgwfjXm+322G3m7uSZTFQrSoadtahYWfi1vPesTlDJV/dQ54NCy+JgotS317QL+IFwci0fDrXJfDcE3pGAca6ZwDhNxdMGWDaGvvh6GzFyLtzeO/5GcwOBSAUgZbLK7DjA1VwNWWvHo/ZwrpQFajNFWv6Gi3fLC2lBHQJpdUZvwrvEkVAbWGfNTPK+dz8sWPH8M///M946KGHcObMGdx5553w+Xz41Kc+BQC47bbbcPz48dj1f/3Xf43HH38c3/zmN3H27Fn81//6X/Haa68lDDtkHlrEWNG0jdq4y+CyPkq1w9AehPUcw176mibbA7P0vYm3B2bpe2qmPTBtjf1wbGnFy/9rBM/88yDGzvkQmNPg90Rw4aVZ/Orrveh71Z2V5yqWn/ml4CKDGiLnZxB65iJCzw5Eg8tSvaPVP6uKiLbWsJt+98SmlPPv2sc//nFMTk7ii1/8IsbGxrBv3z48/vjjsU25AwMDKzqpHjp0CA8//DC+8IUv4POf/zy2b9+ORx99FFdccUWuh0o5VlJuYIZMRHvH5FqmwWVJob6IbyRht0BpLIc+Op/4IpsKpWF90/INDifG/d4kMzAomhmYpQ7OZ5+eRt+r0RkDuSzLL/35pf81gtrDlajeuv59TGYPLktkIILQayNAcNUvSUuVpstsQESDUBQo9aVQW5wJW2tQ4ROyyCr0eL1euFwuPP/Hn0G5lctJhURKibf/4yzC8RoMLrP7w9vhyGGZ+vUEl5/3+gv+RXwjSU1H+HfjCWtlKK1OWLoqs9Ldd9wfPUl4/dHoLzsBzQ0AiwEmWgRxV+X26Lj8i0Uwe/sAAPpENMAAwELvIAAUXIBZCi66LvHLr/bAP5N4T5FQBbb9UTve/+X963/iIgguABA6PQrpCSRemhaA7f1tnGkpYCG/Dw/9/U3weDxwOpP/jLExI20YIQTar25Jek3t1moGFxMRqgLrVY1Qd8Q/xqoPeRF6eTi6qXKdki8hrSxit3oJCUBBLyEtH8uCUyQNLgAgNYmBp0fX/8RFMsuo+0KQ7iTBBQAkoI3MbdiYKLcYQWlDuZqd2PqBLRjqHkFwWX0FxaKgYWcdGncn3vC7XkmDyyIGl8xoA97EbxxBDeE3x2G9rmXdFWiTLyGVAliIu4SkALEj1IW2hLS6LL92zmfofnoEa6oMZ6IYfual21h9KN3NarrFguGFNpyrqQLOxh3wTS8g6AtBtapwNpTntLZLyuDS1VkUL+L5oE/7U9Z7kfNhSHcQomr9s2rFFGDi9RNy7d0KxXoOejjxxnWhClTtbV7VJDFzm+Znvqg2SWxuXDaivFjqCVPTUYXKZueGFKVjcMkNfdpv6NSRnsXmnJkuIS2vwgvkdwkpUSNEu9OGzo/vhkhyxFxqErv/n/jlI9JVCD/zUtOhDXsR6h5D6PQowmenoKdRbdto6wClMndL0rSxGF6o6C1thATSDy5LGFySkNLQb7SGitqlIVGAiSrsAJOqg/P+//oBOJqcEJY4L9FCoO2PdmPLrXvWPY5CCC76XAihFwYROTsNOe2HdAegj8xFG32+N22ouadSboOotCcP0QKs6VJEeNqIitp6g0uJWsngkoI25EXk3HTqCx0W2A9lv2JsvFNIS0eok51CWqrCu9GnkFIFFyAathZGvPjNX/8vTD/aF6sSq7psaPrLy9Hy/14FZb3dlmPy9zMvwxpCp4aAJEtk6rYqWDoqUz+WP4zQa6NAWIsbpi2X10FtXF8/L8qtdE4bMbxQ0UoVXIDomwSDy/rIiI7QMxcNXWs71ArhsGZ9DGkHmGVHqIGNCzCZNEIMTy3Af34WwqKgbE8dlBwc9c3Xz3xkwAPtfIrO4xYFtuvbDfUgksEItAEPtOG5WOATtQ5YOiq5ZGQCPCpNmx6Dy8YRFsVwt1+Z5Dfs9Yi3hLS6E/WKJaQ4naiB3C4hZdrB2VpbCufBFlRc21RUwQUA9PEkBQ6XRPToMWgDhN0Cy/Ya2G7ogO2GdtgOd8C2t5HBpQgxvFDRWf7Gw+CyMYxWKhX29RerSyTbASabMg0uuZb3n3mDYVam2TJEKALCqkJscHd62jg8Kk1Fxeh+AgaX7FJaKqD1JOkYDkBUl+S8uunqY9QAVjRyXHOMugsrOlEvP0bdhuwcoU4/uCA27o2Q15/5UiuQouI2AAgH36poJf5EUNHIRnBZwuAS3QCpDc9Bnw0AkFAqS6L9YErX7llRmyugDc0BwUj8k0cCsGytzvmYgdUBZm0n6o0MMJkFl7Vl+XMpn2FdbalAZDp5SBPlNigb0O+MzIVzalQUshVcnntCZ3ABoI3OIfTiELSLHkhvENIbgjbgRejUELQh75rrhVWFZXctsHz5aGkfjE2FdV8jFOfGvQEtX0KK14kaWDnLkWwJKdP9L4kKIy5/vnwHlyW5/JnX50PQRuehjc9DrmqaqNSWQlQn2Y8iAMuOmujj+EKIXJhB+MwkIj0z0JdV6KbNhzMvZHpGg8slDC7J6O4AIu9OJfx85Nw0hMMKpSZ6mkf3hRA5MwXpWVlUTJTZoG5xQakrM3RSJNvSmYFZ3Yl6vTMwmfbQStQIMZdy9TMf9+dCAEpDGSw7ayEsCoQQsO5pQOS96Wh38uWzdg4LrLtqIVx2hN+ZgD7mW7ExXLvoiT7W7lrubdmEGF7I1NIJLss3QjK4JBa56Im+SSQpohC56IatxgF9IYzwa6NAnA2Vcj4EbdALpa4sd4NNIVsBBoDhNgLZDi5m/LmUiX4uJKCP+RD2hWHd3wShKtHmnrvrILdWQ59eADQJUWaFqCyBEALhdyejwWXx/svp4z5EAFivyE6bBDIPxlUyrdVvEgCDy3pJXUJOLaSsmCtnA5ARHZGeGUBLPDsgPUHoYwaOw66DjOjQphagjfviLiUYXUJa3Yl6+RKS0SPUDC5RkQspfi7mQtGZlmWETYXaVAG11QmlygEhBKQ/vOa61fRxH/QsV2+mwsfwQqaUdCPkomRHTzdyWt5UdOM1K2UgAjmZOujE2yOTDVKXiJyfQei5AUR+N47I2xMIvzyM0Gsja/riLA8BuQowmQaX1eMye3CRYQ36hIGfi+G5lI+ljRvssJ3jgEyFh+GFTCtb9TjM+iaRE6oAjJSdVwVkWEt9HbLf0wgApJQIvzUBbcCzJnBJTxDh10ahz62chWlwOBM0cgTWG2DWE1yKrfmnNHD0GTD4cxHSUhdAFIAMGftZpOLB8EJEMUIIqC2p3zjVlgoIq8GCc1naTCmlhDY+j9BrIwg91R9d3kpEl4i8t7bfkpEAAyCtAJM0uKx6vGIPLgCAeM0k49Elwm+OJw8edjV100+Z2+KHVJgYXohoBbXdCTgs8X/jFQDsKtSOSogy68qj0fEIQG1Y/4ZdKSUi704i8vbkmlNNCe/jDsT97T5VgInXiRpIHmAybf5ZdMEF0YJyotTYWRB9agHh06MJK+iqDcYaKbLh4ubD8EJEKwirCtv+ZojatSXqRbUDtmuaIWwqhBCwbKlM+XhK6/rfkPXhuUsnTtK5X4KlifUGGOBSYCn24CIjOiKDHoRODSH4dD+Czw0gcn4G0h//ayuEgGrg5yL64NHlI23QE/+xSixQ2lI06GupyEmzTypsDC9EtIawq7DtaYDt/W2wXFEHy+V1sB1qhW1f44o+RkpzOdQO1+KdVj2IImDd2wAlTkXedEgpERmI/+aWilCTb5jINMCs7oOUbnBZ/fwFG1xCGsKvjUB7byY6i6VJIKRBG/Ag9PIw9AQNE9WmCqidlYafRxtKvHnXsr16ZYBZ9i1VWitiRexoc2F4IaKERIkFakM51MbyuL/dCiFg2VYN67XNUJrKIcptEE471K5K2A61QqnJQoPBoGao/80aFgXClbh6a7xGjkD6ASaT4GKWHlrhdychfQk21moS4d+NJ17y2VIJpcPgvy2kQSY46SaEgHVHDWwfaIO6vRpqmwvqtmrY3t8G687avBRApPxjeCGidVOcdlh318F2oAW2a5th6azKeRPGVNQOV8o3ttQBBoYCzOr6QsvvZ9bgIhfCkCn6DiGixz2mrE0tIPTcAPSLBo/JC6Q8VSTsFljaXbBsr4alw2W4kzkVJ4YXIipsNtXY8e1lRGPZpeWsFJIHmJV9kOIFmGLtWq7PGOtqvfo63R1A5M3xuFWX4xKAUlcKITiDQsYxvBDRhpCBCHRvEDKQ3hKQUATUNDf9Wjoq03ozXE+AWf7nYgkuQHSvkaHrVi33RHpnUx9vXvEAgNpuLGgSLWF4IaKc0mf80dosLwwi/Gr0v6HTI9Bnjf1mDywuAaXTlVrTITUd2sgcIj0ziPTOrqm6u1rGAaYIgwsAKOXGvt5KxaXrZCACORt/E29cArBcXgclyd4kongYXogoZ7QJH8JvjK2pzSLdQYRfH4M2maTQ3DJCVWC9utHw8pHuCUTbBpyZgjbggdbvRviVEYROjyYtipZRgEHq4LL68c1AVNoN1WtRWypif06n0q3a4YLtYCtrtFBGGF6IKCekpiPy7mTSayLvTkImaeC3nFAVqClqfgAAym3Qzs9Gj/UC0SWMpT96Agi/MZrwZAuQfoAxElyWN/+UYQ2RvlkEXxhE8Km+aN2Unpm0l9NyTQgBy+X1QJJNz+qO6hUbZ4XNYKVbuwrLtmrWZ6GMMbwQUU7o475LASKRiA59InXxOX16AaFXR6D1upNfqCDaDycRCcj5cMrnTBRgolYGmNW3Jw0u/jBCLw9H/x2BSDRUhTRoFxfrpniNVQ/eKIrTDus1zRC1jhW3izIrLFfUw9K2cq+KKLFAuFIvN6nNFSmvIUqG4YWIckKfCxprqjcfSnqJNjKHcPc4ZKo3docF6o6a5OFl2WOmEi/AxGvkuLxredLgsthMMuH4Inq0bkoanb03glJhg21vI2wfaIP12mZYD7bCeqAlYdsHS1dV8ge0KiuWmogywfBCRDlh+LRPkutkUEPk7FTy+1sVWPY1wHaw1XizSINLNEYCjJHgAgDSG4ScCyU/iRPSDM1EZYMMRKB7ggnL/K8m7BYoTjuUUmvS761S7YDlyvpoh3JgZQ2XEgusVzflvQYQmR9/gogoJ0S1AxhMUaRMRt/sEtFG51Ifuw3rEIqAEMJ4eDG6NwPRADPu9+K5J3Rcf1RZDDBu/GmXA8mCy2qG6qYIQJ/153QTq+4JIHJhdsWpIFFhg9pVBTVOP6tMqPVlUKod0Mfnoc+Fot+bageUWgfruVBWcOaFiHJCqXEk7k4NAAIQpVaIqsTHZFMuFS3SvdGlJ1FpB+ypg0m6ey6SzcAkCi5rThZJpF5GW7a5OBf0GX+0i/Oq48xyLoTI78ajYTFLhEWB2uKEdVctLDtroLIQHWURwwsR5YQQ0caMCY83W1VY9zYkf0MzvPR06TktW5PsuRAAHBYoCfZrJLM6wABpBBcAosJuLJikMSuUDqlLhN+ZTDqGyJkpyLDx485E+cLwQkQ5o5TZYDvQCrWrEiixRI/dlligbq2C7UALRIqO00q1seJly5ee1KYKqDuqL81yLNtzIcqssF3dBKFm9tK3PMCUqJWxj6Xbll+zZoy1DkPBRL/oQej15PVoMqFPL6TezCwBbXRtryKiQsM9L0SUU8KmwtJZBUtnilMocSgN5UDPbOI+OQIQLjuUctuKmy1tLqgN5dBG5yEXQhCKEu2fU1Wy7qWL1XtggNTBBVicibqiDuE3xlLOwEh3AOHXR2G9tjlu0JJSQp/2QxvyRk9rKYDiLIFS7YBSVRK3foqcC0VDXKrnTnH6i6gQMLwQUcESFgXWPQ0Id48BUq5947VbYL28Pv59bSosBpszpmt5gFl+W0o2FbCqhmZApC8MfXR+TV8nqUtE3pmAPrGyOrHu90Vr6wAQVSWw7KhZGeoUkXrZykB3Z6JCwGUjIipoSlUJbAdaoLY4AcviS1aJGl16uq55RYXXRKSU0N0BaJML0OeChpsOJrM8rBgJLjKsIfz6GJDGnpJ49Wi0Pvea4LLmuWYDCL82An3ZLEqyU12X7ggo1ZdOHEldQpv0QRv0QhubhzTaKZoox3IaXmZmZvBnf/ZncDqdqKysxKc//WnMzydfTz18+HD0WN2yj8985jO5HCYRFThRaoVlZw3sH+yA/UOdsL+/HZYtlYaORmvDc9GmkKdHEXlzHOFXRhB+eRj6tPHGkIk0OJyG+xVpI/PRGZc0ctPqlgFS06ENeozdWZOInJuO/VVx2qPNLZPNrNhVKHXR8KKNziH0/AAib04g8t40Iu9MRvtFXZjJSvgjWo+chpc/+7M/wzvvvIMnn3wSjz32GJ599ln85V/+Zcr73XHHHRgdHY19/MM//EMuh0lERSrS744WuQuunO2QvjDC3cYbQ2aDnsEx5NXhTHqCqVsuLL/eHUDwZB9Cr41AG5+H5Yo6IFGBOIsC695GCEVEu3G/OwWEV8206BJav2dFKCLKh5zteTlz5gwef/xxvPrqq7jmmmsAAN/5znfwB3/wB/jGN76B5ubmhPctLS1FY2NjroZGRJuADESgXZhNek3kzCSUmnaIJM0Hszae1UHAAKVpZbG6TFsHSE8QEc8klIYyWK9tgj7qgz7sjZ5osijRE1qtFRB2S7Sh5vmZpI+nD89Bb3Wu2ShNtFFyNvNy6tQpVFZWxoILABw5cgSKouDll19Oet9//dd/RW1tLa644gocP34cCwsb99sRERUHI/2LENajR4gzIKVMa/nEyN6cFaxKLLzIsBY9WWSkSm8S+rgP+pgPlg4XbIfaYD+8BfYPtMOytSpWsl+fWkh8umuJyGwmiShbcjbzMjY2hvr6lacALBYLqqurMTY2lvB+//k//2d0dHSgubkZb775Jv7+7/8e586dw7//+7/HvT4YDCIYvFSF0+tNUY6ciDYF6TPQs0csXldn8DGlhD65AG3QA+mOvu4Ipx1qmxNKQ1nSY9hqSwUi6XSNDusIPz8IlFmBhbCxCr0GaAMeqG3OhGOV/kjqI9Vy8TqiPEk7vHzuc5/D1772taTXnDlzJuMBLd8Tc+WVV6KpqQkf+tCHcOHCBWzdunXN9SdOnMC9996b8fMRUZEyshQkDV6HaHCJvDcNfWjljIP0BhF5ZxLK9AIsl9UlDAVKQxnEgAdyKYjEEy80LA9h2dgnG9SijSnj1IIBosfTDR2ptvCwKuVP2uHls5/9LD75yU8mvaarqwuNjY2YmJhYcXskEsHMzExa+1kOHDgAAOjp6YkbXo4fP45jx47F/u71etHW1mb48YmoOCm1pdDHUleLVQw2I9QnFtYElxWfH/NBd5WsqcuyRKgKrFc3IfzOBOTMYm+hpbBiVaJhwGC36/WSMvEkjlJXCqTakCsBpT79FgtE2ZJ2eKmrq0NdXeo51oMHD8LtduP06dPYv38/AOCpp56CruuxQGJEd3c3AKCpqSnu5+12O+x2u+HHI6LNQakrjbYkSBIIRK0DSooWBUu0gdRHlLVBD5SWioSzL8KmwnZVE/T5UHSvjS4hymyATUXk9KihcaxRZl05O5OKKpLuvxF2C5TmcugjCYKfiLZZUGoM1I0hypGczfvt3r0bN910E+644w688soreOGFF3D33XfjE5/4ROyk0fDwMHbt2oVXXnkFAHDhwgV85StfwenTp9Hf349f/vKXuO2223DDDTdgz549uRoqERUhoQhYr2pM2E9IVNhgvczYZhepS0MdruVCZO3x4jiUchssHZWwdFZBrS+LHoHOhIjWb0lnL4za4kx5usqysxZisd7L6scWpVZY9zWyQzTlVU7bA/zrv/4r7r77bnzoQx+Coij4kz/5E/zTP/1T7PPhcBjnzp2LnSay2Wz4zW9+g/vuuw8+nw9tbW34kz/5E3zhC1/I5TCJqEgppVbYDrZCG52DPjoPGdYhSixQWyqg1Jfl5oj0RhZwk9FTTJbdtdG6LCmIcivUzsrU1ykC1ivrIb1BaCNzkP4IhFWF0lgGpaZ0Q46WEyWT0/BSXV2Nhx9+OOHnt2zZsuKoYVtbG5555plcDomINhlhUWBpcwFtmfc5EoqAKLVGN9smY1MNdY5eTXHZkWkPaaWhDEqZDaLEgki/+9J+muVUAbWlAmpnVXRDrgFCCAhXCRSXsc7eRBuJjRmJiAxQ25wpK8smO4KcjHDZIcqsxo53L6PUR4MLAChVDtiqHNH+Q5oOaVGiJ4t0CeGwxO1OTWRW/GkmIjJAaa6ASHIySVSWQG3PbHZHCAHLFfXRE0epss/i50VdKSyX1a79tEWJbrpVFSilVijlNgYXKjqceSEiMmBpH4g26I02R1zql2RToLY6oXZUrmsviFJug+26ZkT63NEj3osr6qLGET05NR+GDGsQNhVKY3l0oy7RJsXwQkRkkFAELB0uqO3OS0ew7ZasbWAVDiusl9VB7qyJnlqyKIb3qBBtJgwvRERpEkIkrFCblcdXFYBLPUQJ8f8OIiIiMhWGFyIiIjIVhhciIiIyFYYXIiIiMhWGFyIiIjIVnjYiIkqTDESge6Jl+BWnHSKHJ4+IaC2GFyIig2RIQ+TsFPTJhRW3i2oHrLtrIUr4kkq0EbhsRERkgIzoCJ8ehT61sPZzs36EXhuBDEbyMDKizYfhhYjIAG3QE+0qLeN8UgIIadD6PRs9LKJNieGFiMgAbWgu+QUS0EbnIPV46YaIsonhhUxroXcw30OgTULqEghpqS/UDF5HROvC8EKmNDi2BcDKAKNPDKy4Rvonlv1t5T6FgOYGAFx/VMG435uLIVIxEYsfRqjZadJIRIkxvJBpLQ8w4Tej4SQWYHr7AEQDzK7K7Yv3iF7zp10OAAwwZJwQAqK2NGWAES47hFXdmEERbWIML2Rq2QowABhgKClLhyv+Zt1l1C2VGzIWos2O4YVMbynAAMg4wFx/NPq/AgMMJaK4SmC5vG7t7Mvi3y07qqHWlm74uIg2I4YXKgqDY1ti+18YYChX1MZy2A62Qu1wQThtEBU2qK1OWN/XArXNle/hEW0aDC9UNBhgaCMIhxWWbdWwXdsC23UtsOyogVJmy/ewiDYVhhcqKkYCDAAGGCIiE2N4oaKTKsAsHaFmgCEiMieGFypK6w0wABhgiIgKFMMLFbVMA8zyGjAAAwwRUSFheKGitboKLwMMEVFxYHihopYywAAMMEREJsPwQkUvaYBZdoQaYIAhIjIDhhfaFNYTYAAwwBARFRCGF9o0Mg0w8Ro5AgwwRET5wvBCmwoDDBGR+TG80KbDAENEZG4ML7QpJQowABhgiIgKHMMLbVrxAky8Ro4AAwwRUSFheKFNLbMAAwYYIqI8YnihTS/9ALO2kSPAAENEtFEYXoiQ/QBDRES5w1daokWrAwyAjAMMZ1+IiHInZ+Hlq1/9Kg4dOoTS0lJUVlYauo+UEl/84hfR1NQEh8OBI0eO4Pz587kaItEaywNMvEaOAAMMEVG+5Sy8hEIh3HrrrbjzzjsN3+cf/uEf8E//9E944IEH8PLLL6OsrAxHjx5FIBDI1TCJ1lgKMED8TtRrrQwwSxhgiIhyI2fh5d5778Xf/M3f4MorrzR0vZQS9913H77whS/gYx/7GPbs2YOf/OQnGBkZwaOPPpqrYRKltKIGzCrLTyAREdHGKJg9L319fRgbG8ORI0dit7lcLhw4cACnTp1KeL9gMAiv17vig4iIiIpXwYSXsbExAEBDQ8OK2xsaGmKfi+fEiRNwuVyxj7a2tpyOk4iIiPIrrfDyuc99DkKIpB9nz57N1VjjOn78ODweT+xjcHAw9Z2IiIjItCzpXPzZz34Wn/zkJ5Ne09XVldFAGhsbAQDj4+NoamqK3T4+Po59+/YlvJ/dbofdbs/oOYmIiMh80govdXV1qKury8lAOjs70djYiJMnT8bCitfrxcsvv5zWiSUiIiIqbjnb8zIwMIDu7m4MDAxA0zR0d3eju7sb8/PzsWt27dqFRx55BAAghMA999yD//bf/ht++ctf4q233sJtt92G5uZm3HLLLbkaJhEREZlMWjMv6fjiF7+Ihx56KPb3q666CgDw29/+FocPHwYAnDt3Dh6PJ3bN3/3d38Hn8+Ev//Iv4Xa78YEPfACPP/44SkpKcjVMIiIiMpmchZcHH3wQDz74YNJrpJQr/i6EwJe//GV8+ctfztWwiIiIyOQK5qg0ERERkREML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqOQsvX/3qV3Ho0CGUlpaisrLS0H0++clPQgix4uOmm27K1RCJiIjIhCy5euBQKIRbb70VBw8exP/4H//D8P1uuukm/PjHP4793W6352J4REREZFI5Cy/33nsvAODBBx9M6352ux2NjY05GBEREREVg4Lb8/L000+jvr4eO3fuxJ133onp6emk1weDQXi93hUfREREVLwKKrzcdNNN+MlPfoKTJ0/ia1/7Gp555hncfPPN0DQt4X1OnDgBl8sV+2hra9vAERMREdFGSyu8fO5zn1uzoXb1x9mzZzMezCc+8Qn80R/9Ea688krccssteOyxx/Dqq6/i6aefTnif48ePw+PxxD4GBwczfn4iIiIqfGntefnsZz+LT37yk0mv6erqWs941jxWbW0tenp68KEPfSjuNXa7nZt6iYiINpG0wktdXR3q6upyNZY1hoaGMD09jaampg17TiIiIipsOdvzMjAwgO7ubgwMDEDTNHR3d6O7uxvz8/Oxa3bt2oVHHnkEADA/P4+//du/xUsvvYT+/n6cPHkSH/vYx7Bt2zYcPXo0V8MkIiIik8nZUekvfvGLeOihh2J/v+qqqwAAv/3tb3H48GEAwLlz5+DxeAAAqqrizTffxEMPPQS3243m5mZ8+MMfxle+8hUuCxEREVFMzsLLgw8+mLLGi5Qy9meHw4EnnngiV8MhIiKiIlFQR6WJiIiIUmF4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlOx5HsA2SalBAD4wqE8j4TMzBsMIxK49DNk9alQ5v2Ln1yACM/HPjfvXYj9OTDvB1Rr7O/hgEQIau4HTERkcqGAD8Cl9/FkhDRylYkMDQ2hra0t38MgIiKiDAwODqK1tTXpNUUXXnRdx8jICCoqKiCESHm91+tFW1sbBgcH4XQ6N2CEBPDrnk/82ucHv+75w699fqT7dZdSYm5uDs3NzVCU5Ltaim7ZSFGUlIktHqfTyR/qPODXPX/4tc8Pft3zh1/7/Ejn6+5yuQxdxw27REREZCoML0RERGQqmz682O12fOlLX4Ldbs/3UDYVft3zh1/7/ODXPX/4tc+PXH7di27DLhERERW3TT/zQkRERObC8EJERESmwvBCREREpsLwQkRERKbC8LKov78fn/70p9HZ2QmHw4GtW7fiS1/6EkIh9kjaCF/96ldx6NAhlJaWorKyMt/DKVr3338/tmzZgpKSEhw4cACvvPJKvoe0KTz77LP46Ec/iubmZggh8Oijj+Z7SJvCiRMncO2116KiogL19fW45ZZbcO7cuXwPq+h973vfw549e2LF6Q4ePIj/+3//b1afg+Fl0dmzZ6HrOr7//e/jnXfewbe+9S088MAD+PznP5/voW0KoVAIt956K+688858D6Vo/exnP8OxY8fwpS99Ca+//jr27t2Lo0ePYmJiIt9DK3o+nw979+7F/fffn++hbCrPPPMM7rrrLrz00kt48sknEQ6H8eEPfxg+ny/fQytqra2t+O///b/j9OnTeO211/B7v/d7+NjHPoZ33nkna8/Bo9JJfP3rX8f3vvc99Pb25nsom8aDDz6Ie+65B263O99DKToHDhzAtddei+9+97sAon3A2tra8Fd/9Vf43Oc+l+fRbR5CCDzyyCO45ZZb8j2UTWdychL19fV45plncMMNN+R7OJtKdXU1vv71r+PTn/50Vh6PMy9JeDweVFdX53sYROsWCoVw+vRpHDlyJHaboig4cuQITp06lceREW0cj8cDAHxd30CapuGnP/0pfD4fDh48mLXHLbrGjNnS09OD73znO/jGN76R76EQrdvU1BQ0TUNDQ8OK2xsaGnD27Nk8jYpo4+i6jnvuuQfvf//7ccUVV+R7OEXvrbfewsGDBxEIBFBeXo5HHnkEl112WdYev+hnXj73uc9BCJH0Y/WL9/DwMG666SbceuutuOOOO/I0cvPL5GtPRJQLd911F95++2389Kc/zfdQNoWdO3eiu7sbL7/8Mu68807cfvvtePfdd7P2+EU/8/LZz34Wn/zkJ5Ne09XVFfvzyMgIbrzxRhw6dAg/+MEPcjy64pbu155yp7a2FqqqYnx8fMXt4+PjaGxszNOoiDbG3XffjcceewzPPvssWltb8z2cTcFms2Hbtm0AgP379+PVV1/Ft7/9bXz/+9/PyuMXfXipq6tDXV2doWuHh4dx4403Yv/+/fjxj38MRSn6iamcSudrT7lls9mwf/9+nDx5MrZRVNd1nDx5EnfffXd+B0eUI1JK/NVf/RUeeeQRPP300+js7Mz3kDYtXdcRDAaz9nhFH16MGh4exuHDh9HR0YFvfOMbmJycjH2Ov5nm3sDAAGZmZjAwMABN09Dd3Q0A2LZtG8rLy/M7uCJx7Ngx3H777bjmmmtw3XXX4b777oPP58OnPvWpfA+t6M3Pz6Onpyf2976+PnR3d6O6uhrt7e15HFlxu+uuu/Dwww/jF7/4BSoqKjA2NgYAcLlccDgceR5d8Tp+/DhuvvlmtLe3Y25uDg8//DCefvppPPHEE9l7EklSSil//OMfSwBxPyj3br/99rhf+9/+9rf5HlpR+c53viPb29ulzWaT1113nXzppZfyPaRN4be//W3cn+/bb78930Mraole03/84x/ne2hF7c///M9lR0eHtNlssq6uTn7oQx+Sv/71r7P6HKzzQkRERKbCTR1ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQq/z/m9RNT/1IhsAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h = 0.25\n", + "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", + " np.arange(y_min, y_max, h))\n", + "Xmesh = np.c_[xx.ravel(), yy.ravel()]\n", + "inputs = [list(map(Value, xrow)) for xrow in Xmesh]\n", + "scores = list(map(model, inputs))\n", + "Z = np.array([s.data > 0 for s in scores])\n", + "Z = Z.reshape(xx.shape)\n", + "\n", + "fig = plt.figure()\n", + "plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n", + "plt.xlim(xx.min(), xx.max())\n", + "plt.ylim(yy.min(), yy.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From bf7e2b56648f011268d8887f775308e161bf7f75 Mon Sep 17 00:00:00 2001 From: Ibad Rather <35704690+ibadrather@users.noreply.github.com> Date: Thu, 22 Sep 2022 15:13:58 +0200 Subject: [PATCH 08/20] Created using Colaboratory --- micrograd_exercises.ipynb | 250 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 250 insertions(+) create mode 100644 micrograd_exercises.ipynb diff --git a/micrograd_exercises.ipynb b/micrograd_exercises.ipynb new file mode 100644 index 00000000..0fda0b28 --- /dev/null +++ b/micrograd_exercises.ipynb @@ -0,0 +1,250 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# micrograd exercises\n", + "\n", + "1. watch the [micrograd video](https://www.youtube.com/watch?v=VMj-3S1tku0) on YouTube\n", + "2. come back and complete these exercises to level up :)" + ], + "metadata": { + "id": "JnGHatCI51JP" + } + }, + { + "cell_type": "markdown", + "source": [ + "## section 1: derivatives" + ], + "metadata": { + "id": "OFt6NKOz6iBZ" + } + }, + { + "cell_type": "code", + "source": [ + "# here is a mathematical expression that takes 3 inputs and produces one output\n", + "from math import sin, cos\n", + "\n", + "def f(a, b, c):\n", + " return -a**3 + sin(3*b) - 1.0/c + b**2.5 - a**0.5\n", + "\n", + "print(f(2, 3, 4))" + ], + "metadata": { + "id": "3Jx9fCXl5xHd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# write the function df that returns the analytical gradient of f\n", + "# i.e. use your skills from calculus to take the derivative, then implement the formula\n", + "# if you do not calculus then feel free to ask wolframalpha, e.g.:\n", + "# https://www.wolframalpha.com/input?i=d%2Fda%28sin%283*a%29%29%29\n", + "\n", + "def gradf(a, b, c):\n", + " return [0, 0, 0] # todo, return [df/da, df/db, df/dc]\n", + "\n", + "# expected answer is the list of \n", + "ans = [-12.353553390593273, 10.25699027111255, 0.0625]\n", + "yours = gradf(2, 3, 4)\n", + "for dim in range(3):\n", + " ok = 'OK' if abs(yours[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {yours[dim]}\")\n" + ], + "metadata": { + "id": "qXaH59eL9zxf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# now estimate the gradient numerically without any calculus, using\n", + "# the approximation we used in the video.\n", + "# you should not call the function df from the last cell\n", + "\n", + "# -----------\n", + "numerical_grad = [0, 0, 0] # TODO\n", + "# -----------\n", + "\n", + "for dim in range(3):\n", + " ok = 'OK' if abs(numerical_grad[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad[dim]}\")\n" + ], + "metadata": { + "id": "_27n-KTA9Qla" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# there is an alternative formula that provides a much better numerical \n", + "# approximation to the derivative of a function.\n", + "# learn about it here: https://en.wikipedia.org/wiki/Symmetric_derivative\n", + "# implement it. confirm that for the same step size h this version gives a\n", + "# better approximation.\n", + "\n", + "# -----------\n", + "numerical_grad2 = [0, 0, 0] # TODO\n", + "# -----------\n", + "\n", + "for dim in range(3):\n", + " ok = 'OK' if abs(numerical_grad2[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad2[dim]}\")\n" + ], + "metadata": { + "id": "BUqsGb5o_h2P" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## section 2: support for softmax" + ], + "metadata": { + "id": "tklF9s_4AtlI" + } + }, + { + "cell_type": "code", + "source": [ + "# Value class starter code, with many functions taken out\n", + "from math import exp, log\n", + "\n", + "class Value:\n", + " \n", + " def __init__(self, data, _children=(), _op='', label=''):\n", + " self.data = data\n", + " self.grad = 0.0\n", + " self._backward = lambda: None\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " self.label = label\n", + "\n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + " \n", + " def __add__(self, other): # exactly as in the video\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data + other.data, (self, other), '+')\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + " out._backward = _backward\n", + " \n", + " return out\n", + " \n", + " # ------\n", + " # re-implement all the other functions needed for the exercises below\n", + " # your code here\n", + " # TODO\n", + " # ------\n", + "\n", + " def backward(self): # exactly as in video \n", + " topo = []\n", + " visited = set()\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " build_topo(self)\n", + " \n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()" + ], + "metadata": { + "id": "nAPe_RVrCTeO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# without referencing our code/video __too__ much, make this cell work\n", + "# you'll have to implement (in some cases re-implemented) a number of functions\n", + "# of the Value object, similar to what we've seen in the video.\n", + "# instead of the squared error loss this implements the negative log likelihood\n", + "# loss, which is very often used in classification.\n", + "\n", + "# this is the softmax function\n", + "# https://en.wikipedia.org/wiki/Softmax_function\n", + "def softmax(logits):\n", + " counts = [logit.exp() for logit in logits]\n", + " denominator = sum(counts)\n", + " out = [c / denominator for c in counts]\n", + " return out\n", + "\n", + "# this is the negative log likelihood loss function, pervasive in classification\n", + "logits = [Value(0.0), Value(3.0), Value(-2.0), Value(1.0)]\n", + "probs = softmax(logits)\n", + "loss = -probs[3].log() # dim 3 acts as the label for this input example\n", + "loss.backward()\n", + "print(loss.data)\n", + "\n", + "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", + "for dim in range(4):\n", + " ok = 'OK' if abs(logits[dim].grad - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits[dim].grad}\")\n" + ], + "metadata": { + "id": "VgWvwVQNAvnI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# verify the gradient using the torch library\n", + "# torch should give you the exact same gradient\n", + "import torch\n" + ], + "metadata": { + "id": "q7ca1SVAGG1S" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From edb5ff88cd259f45ba1b5f8c65859f585f17952d Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Thu, 22 Sep 2022 15:18:39 +0200 Subject: [PATCH 09/20] changed structure --- .../1_gradients_and_backpropagation.ipynb | 0 .../2_activation_optmization_grad_descent.ipynb | 0 my_code/{ => micrograd}/micrograd_demo.ipynb | 0 .../micrograd/micrograd_exercises.ipynb | 0 .../micrograd_ir/micrograd_ir/__init__.py | 0 .../__pycache__/__init__.cpython-310.pyc | Bin .../micrograd_ir/__pycache__/engine.cpython-310.pyc | Bin .../micrograd_ir/micrograd_ir/engine.py | 0 .../{ => micrograd}/micrograd_ir/micrograd_ir/nn.py | 0 .../test_engine.cpython-310-pytest-7.1.3.pyc | Bin .../micrograd_ir/test/test_engine.py | 0 my_code/{ => micrograd}/neuron_model.jpeg | Bin 12 files changed, 0 insertions(+), 0 deletions(-) rename my_code/{ => micrograd}/1_gradients_and_backpropagation.ipynb (100%) rename my_code/{ => micrograd}/2_activation_optmization_grad_descent.ipynb (100%) rename my_code/{ => micrograd}/micrograd_demo.ipynb (100%) rename micrograd_exercises.ipynb => my_code/micrograd/micrograd_exercises.ipynb (100%) rename my_code/{ => micrograd}/micrograd_ir/micrograd_ir/__init__.py (100%) rename my_code/{ => micrograd}/micrograd_ir/micrograd_ir/__pycache__/__init__.cpython-310.pyc (100%) rename my_code/{ => micrograd}/micrograd_ir/micrograd_ir/__pycache__/engine.cpython-310.pyc (100%) rename my_code/{ => micrograd}/micrograd_ir/micrograd_ir/engine.py (100%) rename my_code/{ => micrograd}/micrograd_ir/micrograd_ir/nn.py (100%) rename my_code/{ => micrograd}/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc (100%) rename my_code/{ => micrograd}/micrograd_ir/test/test_engine.py (100%) rename my_code/{ => micrograd}/neuron_model.jpeg (100%) diff --git a/my_code/1_gradients_and_backpropagation.ipynb b/my_code/micrograd/1_gradients_and_backpropagation.ipynb similarity index 100% rename from my_code/1_gradients_and_backpropagation.ipynb rename to my_code/micrograd/1_gradients_and_backpropagation.ipynb diff --git a/my_code/2_activation_optmization_grad_descent.ipynb b/my_code/micrograd/2_activation_optmization_grad_descent.ipynb similarity index 100% rename from my_code/2_activation_optmization_grad_descent.ipynb rename to my_code/micrograd/2_activation_optmization_grad_descent.ipynb diff --git a/my_code/micrograd_demo.ipynb b/my_code/micrograd/micrograd_demo.ipynb similarity index 100% rename from my_code/micrograd_demo.ipynb rename to my_code/micrograd/micrograd_demo.ipynb diff --git a/micrograd_exercises.ipynb b/my_code/micrograd/micrograd_exercises.ipynb similarity index 100% rename from micrograd_exercises.ipynb rename to my_code/micrograd/micrograd_exercises.ipynb diff --git a/my_code/micrograd_ir/micrograd_ir/__init__.py b/my_code/micrograd/micrograd_ir/micrograd_ir/__init__.py similarity index 100% rename from my_code/micrograd_ir/micrograd_ir/__init__.py rename to my_code/micrograd/micrograd_ir/micrograd_ir/__init__.py diff --git 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a/my_code/micrograd_ir/micrograd_ir/nn.py b/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py similarity index 100% rename from my_code/micrograd_ir/micrograd_ir/nn.py rename to my_code/micrograd/micrograd_ir/micrograd_ir/nn.py diff --git a/my_code/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc b/my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc similarity index 100% rename from my_code/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc rename to my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc diff --git a/my_code/micrograd_ir/test/test_engine.py b/my_code/micrograd/micrograd_ir/test/test_engine.py similarity index 100% rename from my_code/micrograd_ir/test/test_engine.py rename to my_code/micrograd/micrograd_ir/test/test_engine.py diff --git a/my_code/neuron_model.jpeg b/my_code/micrograd/neuron_model.jpeg similarity index 100% rename from my_code/neuron_model.jpeg rename to my_code/micrograd/neuron_model.jpeg From e4a4c60ae73c908e84b9b6546b3e2eac1d4998ba Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Thu, 22 Sep 2022 15:52:32 +0200 Subject: [PATCH 10/20] micrograd assignment half done --- my_code/micrograd/micrograd_exercises.ipynb | 268 ++++++++++++++------ 1 file changed, 192 insertions(+), 76 deletions(-) diff --git a/my_code/micrograd/micrograd_exercises.ipynb b/my_code/micrograd/micrograd_exercises.ipynb index 0fda0b28..d9b52fb4 100644 --- a/my_code/micrograd/micrograd_exercises.ipynb +++ b/my_code/micrograd/micrograd_exercises.ipynb @@ -1,26 +1,10 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "collapsed_sections": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -28,27 +12,40 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "JnGHatCI51JP" + }, "source": [ "# micrograd exercises\n", "\n", "1. watch the [micrograd video](https://www.youtube.com/watch?v=VMj-3S1tku0) on YouTube\n", "2. come back and complete these exercises to level up :)" - ], - "metadata": { - "id": "JnGHatCI51JP" - } + ] }, { "cell_type": "markdown", - "source": [ - "## section 1: derivatives" - ], "metadata": { "id": "OFt6NKOz6iBZ" - } + }, + "source": [ + "## section 1: derivatives" + ] }, { "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "3Jx9fCXl5xHd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.336362190988558\n" + ] + } + ], "source": [ "# here is a mathematical expression that takes 3 inputs and produces one output\n", "from math import sin, cos\n", @@ -57,15 +54,25 @@ " return -a**3 + sin(3*b) - 1.0/c + b**2.5 - a**0.5\n", "\n", "print(f(2, 3, 4))" - ], - "metadata": { - "id": "3Jx9fCXl5xHd" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "qXaH59eL9zxf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected -12.353553390593273, yours returns -12.353553390593273\n", + "OK for dim 1: expected 10.25699027111255, yours returns 10.25699027111255\n", + "OK for dim 2: expected 0.0625, yours returns 0.0625\n" + ] + } + ], "source": [ "# write the function df that returns the analytical gradient of f\n", "# i.e. use your skills from calculus to take the derivative, then implement the formula\n", @@ -73,7 +80,10 @@ "# https://www.wolframalpha.com/input?i=d%2Fda%28sin%283*a%29%29%29\n", "\n", "def gradf(a, b, c):\n", - " return [0, 0, 0] # todo, return [df/da, df/db, df/dc]\n", + " df_da = -3*a**2 - 0.5*a**-0.5\n", + " df_db = 3*cos(3*b) + 2.5*b**1.5\n", + " df_dc = c**-2\n", + " return [df_da, df_db, df_dc] # todo, return [df/da, df/db, df/dc]\n", "\n", "# expected answer is the list of \n", "ans = [-12.353553390593273, 10.25699027111255, 0.0625]\n", @@ -81,36 +91,60 @@ "for dim in range(3):\n", " ok = 'OK' if abs(yours[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {yours[dim]}\")\n" - ], - "metadata": { - "id": "qXaH59eL9zxf" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "_27n-KTA9Qla" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected -12.353553390593273, yours returns -12.35355817641448\n", + "OK for dim 1: expected 10.25699027111255, yours returns 10.256986371359744\n", + "OK for dim 2: expected 0.0625, yours returns 0.06250111539429781\n" + ] + } + ], "source": [ "# now estimate the gradient numerically without any calculus, using\n", "# the approximation we used in the video.\n", "# you should not call the function df from the last cell\n", "\n", "# -----------\n", - "numerical_grad = [0, 0, 0] # TODO\n", + "h = 1e-10\n", + "df_da = (f(2+h, 3, 4) - f(2, 3, 4)) / h\n", + "df_db = (f(2, 3+h, 4) - f(2, 3, 4)) / h\n", + "df_dc = (f(2, 3, 4+h) - f(2, 3, 4)) / h\n", + "numerical_grad = [df_da, df_db, df_dc] # TODO\n", "# -----------\n", "\n", "for dim in range(3):\n", " ok = 'OK' if abs(numerical_grad[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad[dim]}\")\n" - ], - "metadata": { - "id": "_27n-KTA9Qla" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "BUqsGb5o_h2P" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected -12.353553390593273, yours returns -12.353553735522382\n", + "OK for dim 1: expected 10.25699027111255, yours returns 10.256990812251843\n", + "OK for dim 2: expected 0.0625, yours returns 0.06250111539429781\n" + ] + } + ], "source": [ "# there is an alternative formula that provides a much better numerical \n", "# approximation to the derivative of a function.\n", @@ -119,30 +153,34 @@ "# better approximation.\n", "\n", "# -----------\n", - "numerical_grad2 = [0, 0, 0] # TODO\n", + "h = 1e-10\n", + "df_da = (f(2+h, 3, 4) - f(2-h, 3, 4)) / (2*h)\n", + "df_db = (f(2, 3+h, 4) - f(2, 3-h, 4)) / (2*h)\n", + "df_dc = (f(2, 3, 4+h) - f(2, 3, 4-h)) / (2*h)\n", + "numerical_grad2 = [df_da, df_db, df_dc] # TODO\n", "# -----------\n", "\n", "for dim in range(3):\n", " ok = 'OK' if abs(numerical_grad2[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad2[dim]}\")\n" - ], - "metadata": { - "id": "BUqsGb5o_h2P" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## section 2: support for softmax" - ], "metadata": { "id": "tklF9s_4AtlI" - } + }, + "source": [ + "## section 2: support for softmax" + ] }, { "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "nAPe_RVrCTeO" + }, + "outputs": [], "source": [ "# Value class starter code, with many functions taken out\n", "from math import exp, log\n", @@ -171,6 +209,46 @@ " \n", " return out\n", " \n", + " def __radd__(self, other):\n", + " return other + self.data\n", + "\n", + " def __mul__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def log(self):\n", + " out = Value(log(self.data), (self, ), \"log\")\n", + " def _backward():\n", + " self.grad += (1/out.data) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + " def __rmul__(self, other): #other * self\n", + " return self * other\n", + "\n", + " def __truediv__(self, other):\n", + " return self * (other**-1)\n", + "\n", + " def exp(self):\n", + " out = Value(exp(self.data), (self,), 'exp')\n", + "\n", + " def _backward():\n", + " self.grad += out.data * out.grad\n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " \n", " # ------\n", " # re-implement all the other functions needed for the exercises below\n", " # your code here\n", @@ -191,15 +269,27 @@ " self.grad = 1.0\n", " for node in reversed(topo):\n", " node._backward()" - ], - "metadata": { - "id": "nAPe_RVrCTeO" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "VgWvwVQNAvnI" + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "bad operand type for unary -: 'NoneType'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/ibad/Desktop/nn-zero-to-hero/my_code/micrograd/micrograd_exercises.ipynb Cell 10\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m logits \u001b[39m=\u001b[39m [Value(\u001b[39m0.0\u001b[39m), Value(\u001b[39m3.0\u001b[39m), Value(\u001b[39m-\u001b[39m\u001b[39m2.0\u001b[39m), Value(\u001b[39m1.0\u001b[39m)]\n\u001b[1;32m 17\u001b[0m probs \u001b[39m=\u001b[39m softmax(logits)\n\u001b[0;32m---> 18\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39m-\u001b[39;49mprobs[\u001b[39m3\u001b[39;49m]\u001b[39m.\u001b[39;49mlog() \u001b[39m# dim 3 acts as the label for this input example\u001b[39;00m\n\u001b[1;32m 19\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n\u001b[1;32m 20\u001b[0m \u001b[39mprint\u001b[39m(loss\u001b[39m.\u001b[39mdata)\n", + "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'NoneType'" + ] + } + ], "source": [ "# without referencing our code/video __too__ much, make this cell work\n", "# you'll have to implement (in some cases re-implemented) a number of functions\n", @@ -226,25 +316,51 @@ "for dim in range(4):\n", " ok = 'OK' if abs(logits[dim].grad - ans[dim]) < 1e-5 else 'WRONG!'\n", " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits[dim].grad}\")\n" - ], - "metadata": { - "id": "VgWvwVQNAvnI" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "q7ca1SVAGG1S" + }, + "outputs": [], "source": [ "# verify the gradient using the torch library\n", "# torch should give you the exact same gradient\n", "import torch\n" - ], - "metadata": { - "id": "q7ca1SVAGG1S" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 }, - "execution_count": null, - "outputs": [] + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} From dcbd32e22e56c3ff89a43338e576f5e0fd649c54 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Thu, 22 Sep 2022 17:01:53 +0200 Subject: [PATCH 11/20] exercise done micrograd --- ...ipynb => solved_micrograd_exercises.ipynb} | 284 ++++++++++++++++-- 1 file changed, 267 insertions(+), 17 deletions(-) rename my_code/micrograd/{micrograd_exercises.ipynb => solved_micrograd_exercises.ipynb} (56%) diff --git a/my_code/micrograd/micrograd_exercises.ipynb b/my_code/micrograd/solved_micrograd_exercises.ipynb similarity index 56% rename from my_code/micrograd/micrograd_exercises.ipynb rename to my_code/micrograd/solved_micrograd_exercises.ipynb index d9b52fb4..faf178cf 100644 --- a/my_code/micrograd/micrograd_exercises.ipynb +++ b/my_code/micrograd/solved_micrograd_exercises.ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { "id": "qXaH59eL9zxf" }, @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": { "id": "_27n-KTA9Qla" }, @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": { "id": "BUqsGb5o_h2P" }, @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 5, "metadata": { "id": "nAPe_RVrCTeO" }, @@ -231,7 +231,7 @@ " \n", " out._backward = _backward\n", "\n", - " return out\n", + " return out\n", " \n", " def __rmul__(self, other): #other * self\n", " return self * other\n", @@ -273,20 +273,154 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " \n", + " def __add__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other) # now, we can add integer directly\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + " def __radd__(self, other):\n", + " return self + other\n", + "\n", + " \n", + " def __mul__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + "\n", + " def __pow__(self, other):\n", + " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", + " out = Value(self.data**other, (self,), f'**{other}')\n", + "\n", + " def _backward():\n", + " self.grad += (other*self.data**(other - 1)) * out.grad\n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + "\n", + " def __rmul__(self, other): #other * self\n", + " return self * other\n", + "\n", + " \n", + " def __truediv__(self, other):\n", + " return self * (other**-1)\n", + "\n", + " \n", + " def __sub__(self, other):\n", + " return self + (-other) \n", + " \n", + " def __neg__(self):\n", + " return -1*self\n", + "\n", + " def exp(self):\n", + " x = self.data\n", + " out = Value(math.exp(x), (self, ), \"exp\")\n", + "\n", + " def _backward():\n", + " self.grad += out.data * out.grad # exp(x) * out.grad\n", + " \n", + " out._backward = _backward\n", + " \n", + " return out\n", + "\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + " \n", + " def log(self):\n", + " out = Value(log(self.data), (self, ), \"log\")\n", + " def _backward():\n", + " self.grad += (1/self.data) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "id": "VgWvwVQNAvnI" }, "outputs": [ { - "ename": "TypeError", - "evalue": "bad operand type for unary -: 'NoneType'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/ibad/Desktop/nn-zero-to-hero/my_code/micrograd/micrograd_exercises.ipynb Cell 10\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m logits \u001b[39m=\u001b[39m [Value(\u001b[39m0.0\u001b[39m), Value(\u001b[39m3.0\u001b[39m), Value(\u001b[39m-\u001b[39m\u001b[39m2.0\u001b[39m), Value(\u001b[39m1.0\u001b[39m)]\n\u001b[1;32m 17\u001b[0m probs \u001b[39m=\u001b[39m softmax(logits)\n\u001b[0;32m---> 18\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39m-\u001b[39;49mprobs[\u001b[39m3\u001b[39;49m]\u001b[39m.\u001b[39;49mlog() \u001b[39m# dim 3 acts as the label for this input example\u001b[39;00m\n\u001b[1;32m 19\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n\u001b[1;32m 20\u001b[0m \u001b[39mprint\u001b[39m(loss\u001b[39m.\u001b[39mdata)\n", - "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'NoneType'" + "name": "stdout", + "output_type": "stream", + "text": [ + "2.1755153626167147\n", + "[Value(data=0.0), Value(data=3.0), Value(data=-2.0), Value(data=1.0)]\n", + "OK for dim 0: expected 0.041772570515350445, yours returns 0.041772570515350445\n", + "OK for dim 1: expected 0.8390245074625319, yours returns 0.8390245074625319\n", + "OK for dim 2: expected 0.005653302662216329, yours returns 0.005653302662216329\n", + "OK for dim 3: expected -0.8864503806400986, yours returns -0.8864503806400986\n" ] } ], @@ -313,14 +447,19 @@ "print(loss.data)\n", "\n", "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", + "\n", + "print(logits)\n", + "\n", + "logits_grad_value = []\n", "for dim in range(4):\n", " ok = 'OK' if abs(logits[dim].grad - ans[dim]) < 1e-5 else 'WRONG!'\n", - " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits[dim].grad}\")\n" + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits[dim].grad}\")\n", + " logits_grad_value.append(logits[dim].grad)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "id": "q7ca1SVAGG1S" }, @@ -328,8 +467,119 @@ "source": [ "# verify the gradient using the torch library\n", "# torch should give you the exact same gradient\n", - "import torch\n" + "import torch" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.Tensor([0.0]).double() ; a.requires_grad = True\n", + "b = torch.Tensor([3.0]).double() ; b.requires_grad = True\n", + "c = torch.Tensor([-2.0]).double() ; c.requires_grad = True\n", + "d = torch.Tensor([1.0]).double() ; d.requires_grad = True\n", + "\n", + "logits = torch.Tensor([a, b, c, d]); logits.requires_grad = True" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.0418, 0.8390, 0.0057, 0.1135], grad_fn=)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probs = torch.nn.functional.softmax(logits, dim=-1)\n", + "probs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2.1755)\n" + ] + } + ], + "source": [ + "loss = -1*probs[3].log() # dim 3 acts as the label for this input example\n", + "loss.backward()\n", + "print(loss.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected 0.041772570515350445, yours returns 0.041772566735744476\n", + "OK for dim 1: expected 0.8390245074625319, yours returns 0.8390244841575623\n", + "OK for dim 2: expected 0.005653302662216329, yours returns 0.005653302650898695\n", + "OK for dim 3: expected -0.8864503806400986, yours returns -0.8864504098892212\n" + ] + } + ], + "source": [ + "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", + "logits_grad_tensor = []\n", + "for dim in range(4):\n", + " ok = 'OK' if abs(logits.grad[dim].item() - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits.grad[dim].item()}\")\n", + " logits_grad_tensor.append(logits.grad[dim].item())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.7796059684791494e-09\n", + "2.330496962077433e-08\n", + "1.1317634329710557e-11\n", + "2.9249122568408836e-08\n" + ] + } + ], + "source": [ + "# Checking the difference between two\n", + "\n", + "for v, t in zip(logits_grad_value, logits_grad_tensor):\n", + " print(v-t)\n", + "\n", + "# As you can see the difference is very small" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From eae6b14a881e0ac2f213d80524edc731cb6cdb6d Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Fri, 7 Oct 2022 01:20:35 +0200 Subject: [PATCH 12/20] makemore 1 complete --- my_code/makemore/1_bigram_torch.ipynb | 2653 ++ my_code/makemore/names.txt | 32033 ++++++++++++++++++++++++ 2 files changed, 34686 insertions(+) create mode 100644 my_code/makemore/1_bigram_torch.ipynb create mode 100644 my_code/makemore/names.txt diff --git a/my_code/makemore/1_bigram_torch.ipynb b/my_code/makemore/1_bigram_torch.ipynb new file mode 100644 index 00000000..64ac3720 --- /dev/null +++ b/my_code/makemore/1_bigram_torch.ipynb @@ -0,0 +1,2653 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['emma',\n", + " 'olivia',\n", + " 'ava',\n", + " 'isabella',\n", + " 'sophia',\n", + " 'charlotte',\n", + " 'mia',\n", + " 'amelia',\n", + " 'harper',\n", + " 'evelyn']" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()\n", + "\n", + "words[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('.', 'e'): 1531,\n", + " ('e', 'm'): 769,\n", + " ('m', 'm'): 168,\n", + " ('m', 'a'): 2590,\n", + " ('a', '.'): 6640,\n", + " ('.', 'o'): 394,\n", + " ('o', 'l'): 619,\n", + " ('l', 'i'): 2480,\n", + " ('i', 'v'): 269,\n", + " ('v', 'i'): 911,\n", + " ('i', 'a'): 2445,\n", + " ('.', 'a'): 4410,\n", + " ('a', 'v'): 834,\n", + " ('v', 'a'): 642,\n", + " ('.', 'i'): 591,\n", + " ('i', 's'): 1316,\n", + " ('s', 'a'): 1201,\n", + " ('a', 'b'): 541,\n", + " ('b', 'e'): 655,\n", + " ('e', 'l'): 3248,\n", + " ('l', 'l'): 1345,\n", + " ('l', 'a'): 2623,\n", + " ('.', 's'): 2055,\n", + " ('s', 'o'): 531,\n", + " ('o', 'p'): 95,\n", + " ('p', 'h'): 204,\n", + " ('h', 'i'): 729,\n", + " ('.', 'c'): 1542,\n", + " ('c', 'h'): 664,\n", + " ('h', 'a'): 2244,\n", + " ('a', 'r'): 3264,\n", + " ('r', 'l'): 413,\n", + " ('l', 'o'): 692,\n", + " ('o', 't'): 118,\n", + " ('t', 't'): 374,\n", + " ('t', 'e'): 716,\n", + " ('e', '.'): 3983,\n", + " ('.', 'm'): 2538,\n", + " ('m', 'i'): 1256,\n", + " ('a', 'm'): 1634,\n", + " ('m', 'e'): 818,\n", + " ('.', 'h'): 874,\n", + " ('r', 'p'): 14,\n", + " ('p', 'e'): 197,\n", + " ('e', 'r'): 1958,\n", + " ('r', '.'): 1377,\n", + " ('e', 'v'): 463,\n", + " ('v', 'e'): 568,\n", + " ('l', 'y'): 1588,\n", + " ('y', 'n'): 1826,\n", + " ('n', '.'): 6763,\n", + " ('b', 'i'): 217,\n", + " ('i', 'g'): 428,\n", + " ('g', 'a'): 330,\n", + " ('a', 'i'): 1650,\n", + " ('i', 'l'): 1345,\n", + " ('l', '.'): 1314,\n", + " ('y', '.'): 2007,\n", + " ('i', 'z'): 277,\n", + " ('z', 'a'): 860,\n", + " ('e', 't'): 580,\n", + " ('t', 'h'): 647,\n", + " ('h', '.'): 2409,\n", + " ('r', 'y'): 773,\n", + " ('o', 'f'): 34,\n", + " ('f', 'i'): 160,\n", + " ('c', 'a'): 815,\n", + " ('r', 'i'): 3033,\n", + " ('s', 'c'): 60,\n", + " ('l', 'e'): 2921,\n", + " ('t', '.'): 483,\n", + " ('.', 'v'): 376,\n", + " ('i', 'c'): 509,\n", + " ('c', 't'): 35,\n", + " ('t', 'o'): 667,\n", + " ('o', 'r'): 1059,\n", + " ('a', 'd'): 1042,\n", + " ('d', 'i'): 674,\n", + " ('o', 'n'): 2411,\n", + " ('.', 'l'): 1572,\n", + " ('l', 'u'): 324,\n", + " ('u', 'n'): 275,\n", + " ('n', 'a'): 2977,\n", + " ('.', 'g'): 669,\n", + " ('g', 'r'): 201,\n", + " ('r', 'a'): 2356,\n", + " ('a', 'c'): 470,\n", + " ('c', 'e'): 551,\n", + " ('h', 'l'): 185,\n", + " ('o', 'e'): 132,\n", + " ('.', 'p'): 515,\n", + " ('e', 'n'): 2675,\n", + " ('n', 'e'): 1359,\n", + " ('a', 'y'): 2050,\n", + " ('y', 'l'): 1104,\n", + " ('.', 'r'): 1639,\n", + " ('e', 'y'): 1070,\n", + " ('.', 'z'): 929,\n", + " ('z', 'o'): 110,\n", + " ('.', 'n'): 1146,\n", + " ('n', 'o'): 496,\n", + " ('e', 'a'): 679,\n", + " ('a', 'n'): 5438,\n", + " ('n', 'n'): 1906,\n", + " ('a', 'h'): 2332,\n", + " ('d', 'd'): 149,\n", + " ('a', 'u'): 381,\n", + " ('u', 'b'): 103,\n", + " ('b', 'r'): 842,\n", + " ('r', 'e'): 1697,\n", + " ('i', 'e'): 1653,\n", + " ('s', 't'): 765,\n", + " ('a', 't'): 687,\n", + " ('t', 'a'): 1027,\n", + " ('a', 'l'): 2528,\n", + " ('a', 'z'): 435,\n", + " ('z', 'e'): 373,\n", + " ('i', 'o'): 588,\n", + " ('u', 'r'): 414,\n", + " ('r', 'o'): 869,\n", + " ('u', 'd'): 136,\n", + " ('d', 'r'): 424,\n", + " ('.', 'b'): 1306,\n", + " ('o', 'o'): 115,\n", + " ('o', 'k'): 68,\n", + " ('k', 'l'): 139,\n", + " ('c', 'l'): 116,\n", + " ('i', 'r'): 849,\n", + " ('s', 'k'): 82,\n", + " ('k', 'y'): 379,\n", + " ('u', 'c'): 103,\n", + " ('c', 'y'): 104,\n", + " ('p', 'a'): 209,\n", + " ('s', 'l'): 279,\n", + " ('i', 'n'): 2126,\n", + " ('o', 'v'): 176,\n", + " ('g', 'e'): 334,\n", + " ('e', 's'): 861,\n", + " ('s', 'i'): 684,\n", + " ('s', '.'): 1169,\n", + " ('.', 'k'): 2963,\n", + " ('k', 'e'): 895,\n", + " ('e', 'd'): 384,\n", + " ('d', 'y'): 317,\n", + " ('n', 't'): 443,\n", + " ('y', 'a'): 2143,\n", + " ('.', 'w'): 307,\n", + " ('w', 'i'): 148,\n", + " ('o', 'w'): 114,\n", + " ('w', '.'): 51,\n", + " ('k', 'i'): 509,\n", + " ('n', 's'): 278,\n", + " ('a', 'o'): 63,\n", + " ('o', 'm'): 261,\n", + " ('i', '.'): 2489,\n", + " ('a', 'a'): 556,\n", + " ('i', 'y'): 779,\n", + " ('d', 'e'): 1283,\n", + " ('c', 'o'): 380,\n", + " ('r', 'u'): 252,\n", + " ('b', 'y'): 83,\n", + " ('s', 'e'): 884,\n", + " ('n', 'i'): 1725,\n", + " ('i', 't'): 541,\n", + " ('t', 'y'): 341,\n", + " ('u', 't'): 82,\n", + " ('t', 'u'): 78,\n", + " ('u', 'm'): 154,\n", + " ('m', 'n'): 20,\n", + " ('g', 'i'): 190,\n", + " ('t', 'i'): 532,\n", + " ('.', 'q'): 92,\n", + " ('q', 'u'): 206,\n", + " ('u', 'i'): 121,\n", + " ('a', 'e'): 692,\n", + " ('e', 'h'): 152,\n", + " ('v', 'y'): 121,\n", + " ('p', 'i'): 61,\n", + " ('i', 'p'): 53,\n", + " ('y', 'd'): 272,\n", + " ('e', 'x'): 132,\n", + " ('x', 'a'): 103,\n", + " ('.', 'j'): 2422,\n", + " ('j', 'o'): 479,\n", + " ('o', 's'): 504,\n", + " ('e', 'p'): 83,\n", + " ('j', 'u'): 202,\n", + " ('u', 'l'): 301,\n", + " ('.', 'd'): 1690,\n", + " ('k', 'a'): 1731,\n", + " ('e', 'e'): 1271,\n", + " ('y', 't'): 104,\n", + " ('d', 'l'): 60,\n", + " ('c', 'k'): 316,\n", + " ('n', 'z'): 145,\n", + " ('z', 'i'): 364,\n", + " ('a', 'g'): 168,\n", + " ('d', 'a'): 1303,\n", + " ('j', 'a'): 1473,\n", + " ('h', 'e'): 674,\n", + " ('.', 'x'): 134,\n", + " ('x', 'i'): 102,\n", + " ('i', 'm'): 427,\n", + " ('e', 'i'): 818,\n", + " ('.', 't'): 1308,\n", + " ('.', 'f'): 417,\n", + " ('f', 'a'): 242,\n", + " ('n', 'd'): 704,\n", + " ('r', 'g'): 76,\n", + " ('a', 's'): 1118,\n", + " ('s', 'h'): 1285,\n", + " ('b', 'a'): 321,\n", + " ('k', 'h'): 307,\n", + " ('s', 'm'): 90,\n", + " ('o', 'd'): 190,\n", + " ('r', 's'): 190,\n", + " ('g', 'h'): 360,\n", + " ('s', 'y'): 215,\n", + " ('y', 's'): 401,\n", + " ('s', 's'): 461,\n", + " ('e', 'c'): 153,\n", + " ('c', 'i'): 271,\n", + " ('m', 'o'): 452,\n", + " ('r', 'k'): 90,\n", + " ('n', 'l'): 195,\n", + " ('d', 'n'): 31,\n", + " ('r', 'd'): 187,\n", + " ('o', 'i'): 69,\n", + " ('t', 'r'): 352,\n", + " ('m', 'b'): 112,\n", + " ('r', 'm'): 162,\n", + " ('n', 'y'): 465,\n", + " ('d', 'o'): 378,\n", + " ('o', 'a'): 149,\n", + " ('o', 'c'): 114,\n", + " ('m', 'y'): 287,\n", + " ('s', 'u'): 185,\n", + " ('m', 'c'): 51,\n", + " ('p', 'r'): 151,\n", + " ('o', 'u'): 275,\n", + " ('r', 'n'): 140,\n", + " ('w', 'a'): 280,\n", + " ('e', 'b'): 121,\n", + " ('c', 'c'): 42,\n", + " ('a', 'w'): 161,\n", + " ('w', 'y'): 73,\n", + " ('y', 'e'): 301,\n", + " ('e', 'o'): 269,\n", + " ('a', 'k'): 568,\n", + " ('n', 'g'): 273,\n", + " ('k', 'o'): 344,\n", + " ('b', 'l'): 103,\n", + " ('h', 'o'): 287,\n", + " ('e', 'g'): 125,\n", + " ('f', 'r'): 114,\n", + " ('s', 'p'): 51,\n", + " ('l', 's'): 94,\n", + " ('y', 'z'): 78,\n", + " ('g', 'g'): 25,\n", + " ('z', 'u'): 73,\n", + " ('i', 'd'): 440,\n", + " ('m', '.'): 516,\n", + " ('o', 'g'): 44,\n", + " ('j', 'e'): 440,\n", + " ('g', 'n'): 27,\n", + " ('y', 'r'): 291,\n", + " ('c', '.'): 97,\n", + " ('c', 'q'): 11,\n", + " ('u', 'e'): 169,\n", + " ('i', 'f'): 101,\n", + " ('f', 'e'): 123,\n", + " ('i', 'x'): 89,\n", + " ('x', '.'): 164,\n", + " ('o', 'y'): 103,\n", + " ('g', 'o'): 83,\n", + " ('g', 't'): 31,\n", + " ('l', 't'): 77,\n", + " ('g', 'w'): 26,\n", + " ('w', 'e'): 149,\n", + " ('l', 'd'): 138,\n", + " ('a', 'p'): 82,\n", + " ('h', 'n'): 138,\n", + " ('t', 'l'): 134,\n", + " ('m', 'r'): 97,\n", + " ('n', 'c'): 213,\n", + " ('l', 'b'): 52,\n", + " ('i', 'k'): 445,\n", + " ('.', 'y'): 535,\n", + " ('t', 'z'): 105,\n", + " ('h', 'r'): 204,\n", + " ('j', 'i'): 119,\n", + " ('h', 't'): 71,\n", + " ('r', 'r'): 425,\n", + " ('z', 'l'): 123,\n", + " ('w', 'r'): 22,\n", + " ('b', 'b'): 38,\n", + " ('r', 't'): 208,\n", + " ('l', 'v'): 72,\n", + " ('e', 'j'): 55,\n", + " ('o', 'h'): 171,\n", + " ('u', 's'): 474,\n", + " ('i', 'b'): 110,\n", + " ('g', 'l'): 32,\n", + " ('h', 'y'): 213,\n", + " ('p', 'o'): 59,\n", + " ('p', 'p'): 39,\n", + " ('p', 'y'): 12,\n", + " ('n', 'r'): 44,\n", + " ('z', 'm'): 35,\n", + " ('v', 'o'): 153,\n", + " ('l', 'm'): 60,\n", + " ('o', 'x'): 45,\n", + " ('d', '.'): 516,\n", + " ('i', 'u'): 109,\n", + " ('v', '.'): 88,\n", + " ('f', 'f'): 44,\n", + " ('b', 'o'): 105,\n", + " ('e', 'k'): 178,\n", + " ('c', 'r'): 76,\n", + " ('d', 'g'): 25,\n", + " ('r', 'c'): 99,\n", + " ('r', 'h'): 121,\n", + " ('n', 'k'): 58,\n", + " ('h', 'u'): 166,\n", + " ('d', 's'): 29,\n", + " ('a', 'x'): 182,\n", + " ('y', 'c'): 115,\n", + " ('e', 'w'): 50,\n", + " ('v', 'k'): 3,\n", + " ('z', 'h'): 43,\n", + " ('w', 'h'): 23,\n", + " ('t', 'n'): 22,\n", + " ('x', 'l'): 39,\n", + " ('g', 'u'): 85,\n", + " ('u', 'a'): 163,\n", + " ('u', 'p'): 16,\n", + " ('u', 'g'): 47,\n", + " ('d', 'u'): 92,\n", + " ('l', 'c'): 25,\n", + " ('r', 'b'): 41,\n", + " ('a', 'q'): 60,\n", + " ('b', '.'): 114,\n", + " ('g', 'y'): 31,\n", + " ('y', 'p'): 15,\n", + " ('p', 't'): 17,\n", + " ('e', 'z'): 181,\n", + " ('z', 'r'): 32,\n", + " ('f', 'l'): 20,\n", + " ('o', '.'): 855,\n", + " ('o', 'b'): 140,\n", + " ('u', 'z'): 45,\n", + " ('z', '.'): 160,\n", + " ('i', 'q'): 52,\n", + " ('y', 'v'): 106,\n", + " ('n', 'v'): 55,\n", + " ('d', 'h'): 118,\n", + " ('g', 'd'): 19,\n", + " ('t', 's'): 35,\n", + " ('n', 'h'): 26,\n", + " ('y', 'j'): 23,\n", + " ('k', 'r'): 109,\n", + " ('z', 'b'): 4,\n", + " ('g', '.'): 108,\n", + " ('a', 'j'): 175,\n", + " ('r', 'j'): 25,\n", + " ('m', 'p'): 38,\n", + " ('p', 'b'): 2,\n", + " ('y', 'o'): 271,\n", + " ('z', 'y'): 147,\n", + " ('p', 'l'): 16,\n", + " ('l', 'k'): 24,\n", + " ('i', 'j'): 76,\n", + " ('x', 'e'): 36,\n", + " ('y', 'u'): 141,\n", + " ('l', 'n'): 14,\n", + " ('u', 'x'): 34,\n", + " ('i', 'h'): 95,\n", + " ('w', 's'): 20,\n", + " ('k', 's'): 95,\n", + " ('m', 'u'): 139,\n", + " ('y', 'k'): 86,\n", + " ('e', 'f'): 82,\n", + " ('k', '.'): 363,\n", + " ('y', 'm'): 148,\n", + " ('z', 'z'): 45,\n", + " ('m', 'd'): 24,\n", + " ('s', 'r'): 55,\n", + " ('e', 'u'): 69,\n", + " ('l', 'h'): 19,\n", + " ('a', 'f'): 134,\n", + " ('r', 'w'): 21,\n", + " ('n', 'u'): 96,\n", + " ('v', 'r'): 48,\n", + " ('m', 's'): 35,\n", + " ('.', 'u'): 78,\n", + " ('f', 's'): 6,\n", + " ('y', 'b'): 27,\n", + " ('x', 'o'): 41,\n", + " ('g', 's'): 30,\n", + " ('x', 'y'): 30,\n", + " ('w', 'n'): 58,\n", + " ('j', 'h'): 45,\n", + " ('f', 'n'): 4,\n", + " ('n', 'j'): 44,\n", + " ('r', 'v'): 80,\n", + " ('n', 'm'): 19,\n", + " ('t', 'c'): 17,\n", + " ('s', 'w'): 24,\n", + " ('k', 't'): 17,\n", + " ('f', 't'): 18,\n", + " ('x', 't'): 70,\n", + " ('u', 'v'): 37,\n", + " ('k', 'k'): 20,\n", + " ('s', 'n'): 24,\n", + " ('u', '.'): 155,\n", + " ('j', 'r'): 11,\n", + " ('y', 'x'): 28,\n", + " ('h', 'm'): 117,\n", + " ('e', 'q'): 14,\n", + " ('u', 'o'): 10,\n", + " ('f', '.'): 80,\n", + " ('h', 'z'): 20,\n", + " ('h', 'k'): 29,\n", + " ('y', 'g'): 30,\n", + " ('q', 'r'): 1,\n", + " ('v', 'n'): 8,\n", + " ('s', 'd'): 9,\n", + " ('y', 'i'): 192,\n", + " ('n', 'w'): 11,\n", + " ('d', 'v'): 17,\n", + " ('h', 'v'): 39,\n", + " ('x', 'w'): 3,\n", + " ('o', 'z'): 54,\n", + " ('k', 'u'): 50,\n", + " ('u', 'h'): 58,\n", + " ('k', 'n'): 26,\n", + " ('s', 'b'): 21,\n", + " ('i', 'i'): 82,\n", + " ('y', 'y'): 23,\n", + " ('r', 'z'): 23,\n", + " ('l', 'g'): 6,\n", + " ('l', 'p'): 15,\n", + " ('p', '.'): 33,\n", + " ('b', 'u'): 45,\n", + " ('f', 'u'): 10,\n", + " ('b', 'h'): 41,\n", + " ('f', 'y'): 14,\n", + " ('u', 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'w'), 6),\n", + " (('n', 'x'), 6),\n", + " (('y', 'q'), 6),\n", + " (('w', 'k'), 6),\n", + " (('g', 'm'), 6),\n", + " (('x', 'u'), 5),\n", + " (('m', 'h'), 5),\n", + " (('m', 'l'), 5),\n", + " (('j', 'm'), 5),\n", + " (('c', 's'), 5),\n", + " (('j', 'v'), 5),\n", + " (('n', 'p'), 5),\n", + " (('d', 'f'), 5),\n", + " (('x', 'd'), 5),\n", + " (('z', 'b'), 4),\n", + " (('f', 'n'), 4),\n", + " (('x', 'c'), 4),\n", + " (('m', 't'), 4),\n", + " (('t', 'm'), 4),\n", + " (('z', 'n'), 4),\n", + " (('z', 't'), 4),\n", + " (('p', 'u'), 4),\n", + " (('c', 'z'), 4),\n", + " (('b', 'n'), 4),\n", + " (('z', 's'), 4),\n", + " (('f', 'w'), 4),\n", + " (('d', 't'), 4),\n", + " (('j', 'd'), 4),\n", + " (('j', 'c'), 4),\n", + " (('y', 'w'), 4),\n", + " (('v', 'k'), 3),\n", + " (('x', 'w'), 3),\n", + " (('t', 'j'), 3),\n", + " (('c', 'j'), 3),\n", + " (('q', 'w'), 3),\n", + " (('g', 'b'), 3),\n", + " (('o', 'q'), 3),\n", + " (('r', 'x'), 3),\n", + " (('d', 'c'), 3),\n", + " (('g', 'j'), 3),\n", + " (('x', 'f'), 3),\n", + " (('z', 'w'), 3),\n", + " (('d', 'k'), 3),\n", + " (('u', 'u'), 3),\n", + " (('m', 'v'), 3),\n", + " (('c', 'x'), 3),\n", + " (('l', 'q'), 3),\n", + " (('p', 'b'), 2),\n", + " (('t', 'g'), 2),\n", + " (('q', 's'), 2),\n", + " (('t', 'x'), 2),\n", + " (('f', 'k'), 2),\n", + " (('b', 't'), 2),\n", + " (('j', 'n'), 2),\n", + " (('k', 'c'), 2),\n", + " (('z', 'k'), 2),\n", + " (('s', 'j'), 2),\n", + " (('s', 'f'), 2),\n", + " (('z', 'j'), 2),\n", + " (('n', 'q'), 2),\n", + " (('f', 'z'), 2),\n", + " (('h', 'g'), 2),\n", + " (('w', 'w'), 2),\n", + " (('k', 'j'), 2),\n", + " (('j', 'k'), 2),\n", + " (('w', 'm'), 2),\n", + " (('z', 'c'), 2),\n", + " (('z', 'v'), 2),\n", + " (('w', 'f'), 2),\n", + " (('q', 'm'), 2),\n", + " (('k', 'z'), 2),\n", + " (('j', 'j'), 2),\n", + " (('z', 'p'), 2),\n", + " (('j', 't'), 2),\n", + " (('k', 'b'), 2),\n", + " (('m', 'w'), 2),\n", + " (('h', 'f'), 2),\n", + " (('c', 'g'), 2),\n", + " (('t', 'f'), 2),\n", + " (('h', 'c'), 2),\n", + " (('q', 'o'), 2),\n", + " (('k', 'd'), 2),\n", + " (('k', 'v'), 2),\n", + " (('s', 'g'), 2),\n", + " (('z', 'd'), 2),\n", + " (('q', 'r'), 1),\n", + " (('d', 'z'), 1),\n", + " (('p', 'j'), 1),\n", + " (('q', 'l'), 1),\n", + " (('p', 'f'), 1),\n", + " (('q', 'e'), 1),\n", + " (('b', 'c'), 1),\n", + " (('c', 'd'), 1),\n", + " (('m', 'f'), 1),\n", + " (('p', 'n'), 1),\n", + " (('w', 'b'), 1),\n", + " (('p', 'c'), 1),\n", + " (('h', 'p'), 1),\n", + " (('f', 'h'), 1),\n", + " (('b', 'j'), 1),\n", + " (('f', 'g'), 1),\n", + " (('z', 'g'), 1),\n", + " (('c', 'p'), 1),\n", + " (('p', 'k'), 1),\n", + " (('p', 'm'), 1),\n", + " (('x', 'n'), 1),\n", + " (('s', 'q'), 1),\n", + " (('k', 'f'), 1),\n", + " (('m', 'k'), 1),\n", + " (('x', 'h'), 1),\n", + " (('g', 'f'), 1),\n", + " (('v', 'b'), 1),\n", + " (('j', 'p'), 1),\n", + " (('g', 'z'), 1),\n", + " (('v', 'd'), 1),\n", + " (('d', 'b'), 1),\n", + " (('v', 'h'), 1),\n", + " (('h', 'h'), 1),\n", + " (('g', 'v'), 1),\n", + " (('d', 'q'), 1),\n", + " (('x', 'b'), 1),\n", + " (('w', 'z'), 1),\n", + " (('h', 'q'), 1),\n", + " (('j', 'b'), 1),\n", + " (('x', 'm'), 1),\n", + " (('w', 'g'), 1),\n", + " (('t', 'b'), 1),\n", + " (('z', 'x'), 1)]" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(bigrams.items(), key = lambda item: -item[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "N = torch.zeros((27, 27), dtype=torch.int32)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n" + ] + } + ], + "source": [ + "chars = sorted(list(set(\"\".join(words))))\n", + "print(chars)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}\n" + ] + } + ], + "source": [ + "# string to int mapping\n", + "stoi = {}\n", + "for i, char in enumerate(chars):\n", + " stoi[char] = i+1\n", + "\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n" + ] + } + ], + "source": [ + "stoi = {char:i+1 for i, char in enumerate(chars)}\n", + "stoi['.'] = 0\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], + "source": [ + "itos = {i+1:char for i, char in enumerate(chars)}\n", + "itos[0] = \".\"\n", + "print(itos)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# plt.figure(figsize=(16,16))\n", + "# plt.imshow(N, cmap='Blues')\n", + "# for i in range(27):\n", + "# for j in range(27):\n", + "# chstr = itos[i] + itos[j]\n", + "# plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n", + "# plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n", + "# plt.axis('off');" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's fill bigrams now into N\n", + "for w in words:\n", + " #chs = [''] + list(w) + ['']\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " itx1 = stoi[ch1]\n", + " itx2 = stoi[ch2]\n", + " N[itx1, itx2] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,16))\n", + "plt.imshow(N, cmap='Blues')\n", + "for i in range(27):\n", + " for j in range(27):\n", + " chstr = itos[i] + itos[j]\n", + " plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n", + " plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n", + "plt.axis('off');" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0, 4410, 1306, 1542, 1690, 1531, 417, 669, 874, 591, 2422, 2963,\n", + " 1572, 2538, 1146, 394, 515, 92, 1639, 2055, 1308, 78, 376, 307,\n", + " 134, 535, 929], dtype=torch.int32)" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now lets'make probability distributions row wise\n", + "#sum(N[0] / sum(N[0]))\n", + "N[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "#P = N.float()\n", + "#P.sum(1).shape # we need keepdim=True otherwise calculations areincorrect" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "P = (N+1).float()\n", + "P /= P.sum(1, keepdim=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mor.\n", + "axx.\n", + "minaymoryles.\n", + "kondlaisah.\n", + "anchshizarie.\n" + ] + } + ], + "source": [ + "g = torch.Generator().manual_seed(2147483647)\n", + "\n", + "for i in range(5):\n", + " out = []\n", + " ix = 0\n", + " while True:\n", + " p = P[ix]\n", + " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", + " out.append(itos[ix]) \n", + " if ix==0: # we get a \".\"\n", + " break\n", + " \n", + " print(\"\".join(out))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We want to make a metric to measure (Quality) how good the model is. i.e we want a loss" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".e: 0.0478\n", + "em: 0.0377\n", + "mm: 0.0253\n", + "ma: 0.3885\n", + "a.: 0.1958\n", + ".o: 0.0123\n", + "ol: 0.0779\n", + "li: 0.1774\n", + "iv: 0.0152\n", + "vi: 0.3508\n", + "ia: 0.1380\n", + "a.: 0.1958\n", + ".a: 0.1376\n", + "av: 0.0246\n", + "va: 0.2473\n", + "a.: 0.1958\n" + ] + } + ], + "source": [ + "# Let's fill bigrams now into N\n", + "for w in words[:3]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", + " print(f\"{ch1}{ch2}: {prob:.4f}\")\n", + "\n", + "# There are 27 characters. That means during initialization 1/27 should be probability of each.\n", + "# If the probability is higger than that for some that means the model has learned something\n", + "# Which it has actually\n", + "# These rpobability should be near to 1 atleast on the training set meaning the model is confident." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Likelihood\n", + "\n", + "The product of probability distributions should be high\n", + "\n", + "Log probability; which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", + "\n", + "Loss function has the semantic that lower is better but with log-liklihood lower is worse so we use **negative log likelihood**\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GOAL: maximize likelihood of the data w.r.t. model parameters (statistical modeling)\n", + "\n", + "equivalent to maximizing the log likelihood (because log is monotonic)\n", + "\n", + "equivalent to minimizing the negative log likelihood\n", + "\n", + "equivalent to minimizing the average negative log likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log_likelihood=tensor(-559951.5625)\n", + "nll/(n+1)=tensor(2.4543)\n" + ] + } + ], + "source": [ + "log_likelihood = 0.0\n", + "# Let's fill bigrams now into N\n", + "n = 0\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", + " logprob = torch.log(prob) # log probability # which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", + " log_likelihood += logprob\n", + " n += 1\n", + " #print(f\"{ch1}{ch2}: {prob:.4f} {logprob: .4f}\")\n", + "\n", + "print(f\"{log_likelihood=}\") # Maximum it can go is 0 when all probs are 1\n", + "nll = -log_likelihood\n", + "print(f\"{nll/(n+1)=}\") # We want average # the lower it is the better we are" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can estimate probability for any word (name)\n", + "\n", + "But if we check probability for a name like **ibadqq** the model gives a loss of inf which means the probability is exactly zero.\n", + "This is highly undesirable. q followed by q occurs 0 times as you can see from the initial graph.\n", + "\n", + "To tackle this we can do something like model smoothning. We will add 1 to all occurances of bigrams. It will eliminate this\n", + "problem. We could even 1000 to the counts. Adding a larger number will make the model more smooth. But here we will just add 1." + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log_likelihood=tensor(-559951.5625)\n", + "nll/(n+1)=tensor(2.4543)\n" + ] + } + ], + "source": [ + "log_likelihood = 0.0\n", + "# Let's fill bigrams now into N\n", + "n = 0\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", + " logprob = torch.log(prob) # log probability # which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", + " log_likelihood += logprob\n", + " n += 1\n", + " #print(f\"{ch1}{ch2}: {prob:.4f} {logprob: .4f}\")\n", + "\n", + "print(f\"{log_likelihood=}\") # Maximum it can go is 0 when all probs are 1\n", + "nll = -log_likelihood\n", + "print(f\"{nll/(n+1)=}\") # We want average # the lower it is the better we are" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now we will do try to reach the same place but with a different approach; using neural net" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".e: 0 5\n", + "em: 5 13\n", + "mm: 13 13\n", + "ma: 13 1\n", + "a.: 1 0\n", + "tensor([ 0, 5, 13, 13, 1])\n", + "tensor([ 5, 13, 13, 1, 0])\n" + ] + } + ], + "source": [ + "# First let's create a training set of bigrams (x, y)\n", + "\n", + "xs, ys = [], []\n", + "\n", + "for w in words[:1]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "\n", + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### one hot encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([5, 27])\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import torch.nn.functional as F\n", + "xenc = F.one_hot(xs, num_classes=27).float() # we dont want an int we want a float\n", + "print(xenc.shape)\n", + "plt.imshow(xenc, cmap='Blues')" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.float32" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xenc.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's construct first neuron" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-7.6148e-01, -3.1424e-01, 1.9481e-02, -2.2533e-01, 4.5341e-01,\n", + " 4.2244e-01, -6.9604e-01, -1.6960e+00, -3.3331e-01, 3.7474e-01,\n", + " -8.0611e-02, -1.1231e+00, -4.4109e-01, -1.2095e+00, 4.1020e-01,\n", + " -1.5015e+00, -8.7129e-01, 2.9803e-01, -2.3551e+00, 3.4084e-04,\n", + " -9.6937e-01, 6.0936e-01, -8.3752e-01, 3.3975e-01, 1.5214e+00,\n", + " -8.9153e-01, 1.4327e+00],\n", + " [ 9.3261e-01, -9.2786e-01, -6.2602e-01, -9.7203e-01, 7.9021e-01,\n", + " 4.0905e-01, -9.1781e-01, 1.3454e+00, -4.0126e-01, -6.0294e-01,\n", + " -5.3656e-01, 8.8367e-01, 5.4779e-01, -3.5715e-01, 1.8984e-01,\n", + " 4.1010e-01, 1.6740e+00, 1.7788e+00, -5.2731e-01, 1.9860e+00,\n", + " 2.4396e+00, -1.4672e+00, 1.3123e+00, -6.3350e-01, 3.4797e-01,\n", + " -8.2656e-01, 4.2729e-02],\n", + " [ 2.1002e-01, -8.5984e-01, -9.2525e-01, -8.6639e-01, -1.2768e+00,\n", + " 3.8859e-01, 8.8408e-01, 4.0335e-01, 4.6471e-01, -6.9420e-01,\n", + " -1.5782e+00, -1.1070e-01, 3.6890e-02, 7.1488e-01, 4.3240e-01,\n", + " -4.2933e-01, 1.3047e+00, -9.7873e-01, 9.5837e-02, 6.6297e-01,\n", + " 8.1560e-01, 1.0019e+00, 5.6406e-01, -9.2049e-01, 3.8910e-01,\n", + " 1.3193e+00, 1.3622e+00],\n", + " [ 2.1002e-01, -8.5984e-01, -9.2525e-01, -8.6639e-01, -1.2768e+00,\n", + " 3.8859e-01, 8.8408e-01, 4.0335e-01, 4.6471e-01, -6.9420e-01,\n", + " -1.5782e+00, -1.1070e-01, 3.6890e-02, 7.1488e-01, 4.3240e-01,\n", + " -4.2933e-01, 1.3047e+00, -9.7873e-01, 9.5837e-02, 6.6297e-01,\n", + " 8.1560e-01, 1.0019e+00, 5.6406e-01, -9.2049e-01, 3.8910e-01,\n", + " 1.3193e+00, 1.3622e+00],\n", + " [ 1.3709e+00, -1.9400e+00, -2.4378e-01, 3.2082e-01, -1.1422e+00,\n", + " -8.0290e-01, 1.4555e+00, 7.6867e-01, -4.1894e-01, 1.0218e+00,\n", + " -1.0205e+00, -5.4941e-03, -7.9474e-02, -5.8220e-01, 1.2662e+00,\n", + " 7.7145e-01, -1.6896e-01, 3.9349e-01, 5.0482e-01, -2.5368e+00,\n", + " 3.9033e-01, 8.8935e-01, 5.9883e-01, -3.6300e-01, -1.2209e-01,\n", + " 6.1462e-01, 4.4726e-01]])" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W = torch.randn((27, 27)) # We created 27 neurons \n", + "# (5, 27) @ (27, 27) = (5, 27) # all 5 inputs will be trained for all 27 possible outputs this way\n", + "xenc @ W\n", + "\n", + "# W will be our only NN layer, the simplest dumbest type lol.\n", + "\n", + "# For every input we want to generate a probability distibution over all possible outputs (27 here)\n", + "\n", + "## We want interpret these results now somehow.\n", + "# We dont want counts directly because they are integers. What we will do is we will get log of counts from neural net.\n", + "# We will then exponentiate them to get counts back (somewhat)\n", + "\n", + "# for exp(0) = 1\n", + "# for exp(>0) > 1\n", + "# for exp(<0) < 1 \n", + "\n", + "# these exponentiated values will be our counts they cant be below 0." + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.4670, 0.7303, 1.0197, 0.7983, 1.5737, 1.5257, 0.4986, 0.1834,\n", + " 0.7165, 1.4546, 0.9226, 0.3253, 0.6433, 0.2983, 1.5071, 0.2228,\n", + " 0.4184, 1.3472, 0.0949, 1.0003, 0.3793, 1.8393, 0.4328, 1.4046,\n", + " 4.5785, 0.4100, 4.1900],\n", + " [ 2.5411, 0.3954, 0.5347, 0.3783, 2.2039, 1.5054, 0.3994, 3.8397,\n", + " 0.6695, 0.5472, 0.5848, 2.4198, 1.7294, 0.6997, 1.2091, 1.5070,\n", + " 5.3336, 5.9228, 0.5902, 7.2860, 11.4683, 0.2306, 3.7148, 0.5307,\n", + " 1.4162, 0.4376, 1.0437],\n", + " [ 1.2337, 0.4232, 0.3964, 0.4205, 0.2789, 1.4749, 2.4208, 1.4968,\n", + " 1.5916, 0.4995, 0.2063, 0.8952, 1.0376, 2.0439, 1.5409, 0.6509,\n", + " 3.6867, 0.3758, 1.1006, 1.9405, 2.2605, 2.7235, 1.7578, 0.3983,\n", + " 1.4757, 3.7407, 3.9049],\n", + " [ 1.2337, 0.4232, 0.3964, 0.4205, 0.2789, 1.4749, 2.4208, 1.4968,\n", + " 1.5916, 0.4995, 0.2063, 0.8952, 1.0376, 2.0439, 1.5409, 0.6509,\n", + " 3.6867, 0.3758, 1.1006, 1.9405, 2.2605, 2.7235, 1.7578, 0.3983,\n", + " 1.4757, 3.7407, 3.9049],\n", + " [ 3.9389, 0.1437, 0.7837, 1.3783, 0.3191, 0.4480, 4.2868, 2.1569,\n", + " 0.6577, 2.7781, 0.3604, 0.9945, 0.9236, 0.5587, 3.5472, 2.1629,\n", + " 0.8445, 1.4821, 1.6567, 0.0791, 1.4775, 2.4335, 1.8200, 0.6956,\n", + " 0.8851, 1.8490, 1.5640]])" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(xenc @ W).exp()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(24)" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logits = xenc @ W # log-counts\n", + "counts = logits.exp() # counts\n", + "probs = counts / counts.sum(1, keepdim=True) # probabilities # normalizse the rows of counts: sum of every row is 1\n", + "probs[0].argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summarised" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ 0, 5, 13, 13, 1])\n", + "tensor([ 5, 13, 13, 1, 0])\n" + ] + } + ], + "source": [ + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [], + "source": [ + "xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + "logits = xenc @ W # log-counts\n", + "counts = logits.exp() # counts\n", + "probs = counts / counts.sum(1, keepdim=True) # probabilities # normalizse the rows of counts: sum of every row is 1\n", + "# these two previous lines are the same as softmax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([5, 27])" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "# the forward pass is back-propagateable" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------\n", + "bigram example 1: .e (indexes 0,5)\n", + "input to the neural net: 0\n", + "output probabilities from the neural net: tensor([0.0607, 0.0100, 0.0123, 0.0042, 0.0168, 0.0123, 0.0027, 0.0232, 0.0137,\n", + " 0.0313, 0.0079, 0.0278, 0.0091, 0.0082, 0.0500, 0.2378, 0.0603, 0.0025,\n", + " 0.0249, 0.0055, 0.0339, 0.0109, 0.0029, 0.0198, 0.0118, 0.1537, 0.1459],\n", + " grad_fn=)\n", + "label (actual next character): 5\n", + "probability assigned by the net to the the correct character: 0.01228625513613224\n", + "log likelihood: -4.399273872375488\n", + "negative log likelihood: 4.399273872375488\n", + "--------\n", + "bigram example 2: em (indexes 5,13)\n", + "input to the neural net: 5\n", + "output probabilities from the neural net: tensor([0.0290, 0.0796, 0.0248, 0.0521, 0.1989, 0.0289, 0.0094, 0.0335, 0.0097,\n", + " 0.0301, 0.0702, 0.0228, 0.0115, 0.0181, 0.0108, 0.0315, 0.0291, 0.0045,\n", + " 0.0916, 0.0215, 0.0486, 0.0300, 0.0501, 0.0027, 0.0118, 0.0022, 0.0472],\n", + " grad_fn=)\n", + "label (actual next character): 13\n", + "probability assigned by the net to the the correct character: 0.018050700426101685\n", + "log likelihood: -4.014570713043213\n", + "negative log likelihood: 4.014570713043213\n", + "--------\n", + "bigram example 3: mm (indexes 13,13)\n", + "input to the neural net: 13\n", + "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n", + " 0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n", + " 0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126],\n", + " grad_fn=)\n", + "label (actual next character): 13\n", + "probability assigned by the net to the the correct character: 0.026691533625125885\n", + "log likelihood: -3.623408794403076\n", + "negative log likelihood: 3.623408794403076\n", + "--------\n", + "bigram example 4: ma (indexes 13,1)\n", + "input to the neural net: 13\n", + "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n", + " 0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n", + " 0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126],\n", + " grad_fn=)\n", + "label (actual next character): 1\n", + "probability assigned by the net to the the correct character: 0.07367686182260513\n", + "log likelihood: -2.6080665588378906\n", + "negative log likelihood: 2.6080665588378906\n", + "--------\n", + "bigram example 5: a. (indexes 1,0)\n", + "input to the neural net: 1\n", + "output probabilities from the neural net: tensor([0.0150, 0.0086, 0.0396, 0.0100, 0.0606, 0.0308, 0.1084, 0.0131, 0.0125,\n", + " 0.0048, 0.1024, 0.0086, 0.0988, 0.0112, 0.0232, 0.0207, 0.0408, 0.0078,\n", + " 0.0899, 0.0531, 0.0463, 0.0309, 0.0051, 0.0329, 0.0654, 0.0503, 0.0091],\n", + " grad_fn=)\n", + "label (actual next character): 0\n", + "probability assigned by the net to the the correct character: 0.014977526850998402\n", + "log likelihood: -4.201204299926758\n", + "negative log likelihood: 4.201204299926758\n", + "=========\n", + "average negative log likelihood, i.e. loss = 3.7693049907684326\n" + ] + } + ], + "source": [ + "nlls = torch.zeros(5)\n", + "for i in range(5):\n", + " # i-th bigram:\n", + " x = xs[i].item() # input character index\n", + " y = ys[i].item() # label character index\n", + " print('--------')\n", + " print(f'bigram example {i+1}: {itos[x]}{itos[y]} (indexes {x},{y})')\n", + " print('input to the neural net:', x)\n", + " print('output probabilities from the neural net:', probs[i])\n", + " print('label (actual next character):', y)\n", + " p = probs[i, y]\n", + " print('probability assigned by the net to the the correct character:', p.item())\n", + " logp = torch.log(p)\n", + " print('log likelihood:', logp.item())\n", + " nll = -logp\n", + " print('negative log likelihood:', nll.item())\n", + " nlls[i] = nll\n", + "\n", + "print('=========')\n", + "print('average negative log likelihood, i.e. loss =', nlls.mean().item())" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "# Forwards pass\n", + "xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + "logits = xenc @ W # log-counts\n", + "counts = logits.exp() # counts, equivalent to N\n", + "probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", + "loss = -probs[torch.arange(5), ys].log().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [], + "source": [ + "# Backwards pass\n", + "W.grad = None # set the gradient to zero\n", + "loss.backward() # compute the gradient" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0121, 0.0020, 0.0025, 0.0008, 0.0034, -0.1975, 0.0005, 0.0046,\n", + " 0.0027, 0.0063, 0.0016, 0.0056, 0.0018, 0.0016, 0.0100, 0.0476,\n", + " 0.0121, 0.0005, 0.0050, 0.0011, 0.0068, 0.0022, 0.0006, 0.0040,\n", + " 0.0024, 0.0307, 0.0292],\n", + " [-0.1970, 0.0017, 0.0079, 0.0020, 0.0121, 0.0062, 0.0217, 0.0026,\n", + " 0.0025, 0.0010, 0.0205, 0.0017, 0.0198, 0.0022, 0.0046, 0.0041,\n", + " 0.0082, 0.0016, 0.0180, 0.0106, 0.0093, 0.0062, 0.0010, 0.0066,\n", + " 0.0131, 0.0101, 0.0018],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0058, 0.0159, 0.0050, 0.0104, 0.0398, 0.0058, 0.0019, 0.0067,\n", + " 0.0019, 0.0060, 0.0140, 0.0046, 0.0023, -0.1964, 0.0022, 0.0063,\n", + " 0.0058, 0.0009, 0.0183, 0.0043, 0.0097, 0.0060, 0.0100, 0.0005,\n", + " 0.0024, 0.0004, 0.0094],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", 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0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0125, -0.1705, 0.0194, 0.0133, 0.0270, 0.0080, 0.0105, 0.0100,\n", + " 0.0490, 0.0066, 0.0030, 0.0316, 0.0052, -0.1893, 0.0059, 0.0045,\n", + " 0.0234, 0.0049, 0.0260, 0.0023, 0.0083, 0.0031, 0.0053, 0.0081,\n", + " 0.0482, 0.0187, 0.0051],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000]])" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W.grad\n", + "\n", + "# W[0, 0] is positive. It means the loss will increse if this increases.\n", + "# We can use this gradient information to update the weights" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss before update: 3.7693049907684326\n", + "loss after update: 3.7693049907684326\n" + ] + } + ], + "source": [ + "W.data += -0.1 * W.grad\n", + "\n", + "# Loss should descrease after this update\n", + "\n", + "print('loss before update:', loss.item())\n", + "\n", + "loss = -probs[torch.arange(5), ys].log().mean()\n", + "\n", + "print('loss after update:', loss.item())\n", + "# It is slightly lower" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's train properly now" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples: 228146\n" + ] + } + ], + "source": [ + "# Create the dataset\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "num = xs.nelement()\n", + "\n", + "print(\"Number of examples: \", num)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", + "W = torch.zeros((27, 27), requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 3.295837640762329\n", + "loss: 2.52099871635437\n", + "loss: 2.4878063201904297\n", + "loss: 2.4761619567871094\n", + "loss: 2.4702982902526855\n", + "loss: 2.466809034347534\n", + "loss: 2.4645133018493652\n", + "loss: 2.4628946781158447\n", + "loss: 2.4616944789886475\n", + "loss: 2.4607694149017334\n", + "loss: 2.4600346088409424\n", + "loss: 2.459437370300293\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 50\n", + "\n", + "for k in range(300):\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + " logits = xenc @ W # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", + " loss = -probs[torch.arange(num), ys].log().mean()\n", + " if k % 25 == 0:\n", + " print('loss:', loss.item())\n", + "\n", + " # Backward pass\n", + " W.grad = None # set gradient to zero\n", + " loss.backward()\n", + "\n", + " # update\n", + " W.data += -lr * W.grad # gradient descent\n", + "\n", + "# The loss is same as calculated with probability distrubuted\n", + "# We did the same thing but it is way more flexible\n", + "# static approach is not scalable but NN one is" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regularisation" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", + "W = torch.zeros((27, 27), requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 3.295837640762329\n", + "loss: 2.529900074005127\n", + "loss: 2.500406265258789\n", + "loss: 2.491044044494629\n", + "loss: 2.4867959022521973\n", + "loss: 2.4845311641693115\n", + "loss: 2.4832026958465576\n", + "loss: 2.482370615005493\n", + "loss: 2.4818227291107178\n", + "loss: 2.4814486503601074\n", + "loss: 2.4811856746673584\n", + "loss: 2.4809961318969727\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 50\n", + "alpha = 0.01\n", + "\n", + "for k in range(300):\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + " logits = xenc @ W # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", + " \n", + " # regularised loss\n", + " loss = -probs[torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", + " if k % 25 == 0:\n", + " print('loss:', loss.item())\n", + "\n", + " # Backward pass\n", + " W.grad = None # set gradient to zero\n", + " loss.backward()\n", + "\n", + " # update\n", + " W.data += -lr * W.grad # gradient descent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Effect of regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "# regularisation here will want to make W elements smaller (0 best case for it)\n", + "# the regular loss will try to make the probility distribution according to training data\n", + "# regularisation is similar to smoothening that we did before\n", + "# Increasing counts: (N + 1) or even (N + 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample from neural net model" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mor.\n", + "axx.\n", + "minaymoryles.\n", + "kondlaisah.\n", + "anchthizarie.\n" + ] + } + ], + "source": [ + "# finally, sample from the 'neural net' model\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "\n", + "for i in range(5):\n", + " \n", + " out = []\n", + " ix = 0\n", + " while True:\n", + " \n", + " # ----------\n", + " # BEFORE:\n", + " #p = P[ix]\n", + " # ----------\n", + " # NOW:\n", + " xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float()\n", + " logits = xenc @ W # predict log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n", + " # ----------\n", + " \n", + " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", + " out.append(itos[ix])\n", + " if ix == 0:\n", + " break\n", + " print(''.join(out))\n", + "\n", + "# same output as before exactly.\n", + "# identical models and acheive the same loss\n", + "# mor.\n", + "# axx.\n", + "# minaymoryles.\n", + "# kondlaisah.\n", + "# anchshizarie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/makemore/names.txt b/my_code/makemore/names.txt new file mode 100644 index 00000000..736d88e1 --- /dev/null +++ b/my_code/makemore/names.txt @@ -0,0 +1,32033 @@ +emma +olivia +ava +isabella +sophia +charlotte +mia +amelia +harper +evelyn +abigail +emily +elizabeth +mila +ella +avery +sofia +camila +aria +scarlett +victoria +madison +luna +grace +chloe +penelope +layla +riley +zoey +nora +lily +eleanor +hannah +lillian +addison +aubrey +ellie +stella +natalie +zoe +leah +hazel +violet +aurora +savannah +audrey +brooklyn +bella +claire +skylar +lucy +paisley +everly +anna +caroline +nova +genesis +emilia +kennedy +samantha +maya +willow +kinsley +naomi +aaliyah +elena +sarah +ariana +allison +gabriella +alice +madelyn +cora +ruby +eva +serenity +autumn +adeline +hailey +gianna +valentina +isla +eliana +quinn +nevaeh +ivy 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+zaviel +zavin +zaye +zaytoven +zee +zeev +zeferino +zell +zenas +zerrick +zeshan +zevan +zhander +zhen +zheng +zhiheng +ziaan +zichen +zidon +zien +zier +zierre +zihir +zim +zin +zishe +zmari +zoel +zola +zuber +zubeyr +zyell +zyheem +zykeem +zylas +zyran +zyrie +zyron +zzyzx \ No newline at end of file From 0f70ced318d0b56b675a2cd1e16bbff67e979e22 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Fri, 7 Oct 2022 02:45:43 +0200 Subject: [PATCH 13/20] working on trigram exercise --- .../makemore/1_bigram_torch_exercise.ipynb | 2173 + .../1_bigram_torch_exercise_approach_2.ipynb | 198920 +++++++++++++++ 2 files changed, 201093 insertions(+) create mode 100644 my_code/makemore/1_bigram_torch_exercise.ipynb create mode 100644 my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb diff --git a/my_code/makemore/1_bigram_torch_exercise.ipynb b/my_code/makemore/1_bigram_torch_exercise.ipynb new file mode 100644 index 00000000..17dfc448 --- /dev/null +++ b/my_code/makemore/1_bigram_torch_exercise.ipynb @@ -0,0 +1,2173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises:\n", + "E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model?\n", + "\n", + "E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n", + "\n", + "E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n", + "\n", + "E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of F.one_hot in favor of simply indexing into rows of W?\n", + "\n", + "E05: look up and use F.cross_entropy instead. You should achieve the same result. Can you think of why we'd prefer to use F.cross_entropy instead?\n", + "\n", + "E06: meta-exercise! Think of a fun/interesting exercise and complete it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n", + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))\n", + "\n", + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('.e', 'm'): 288,\n", + " ('em', 'm'): 100,\n", + " ('mm', 'a'): 72,\n", + " ('ma', '.'): 174,\n", + " ('.o', 'l'): 104,\n", + " ('ol', 'i'): 69,\n", + " ('li', 'v'): 54,\n", + " ('iv', 'i'): 78,\n", + " ('vi', 'a'): 147,\n", + " ('ia', '.'): 903,\n", + " ('.a', 'v'): 243,\n", + " ('av', 'a'): 161,\n", + " ('va', '.'): 93,\n", + " ('.i', 's'): 124,\n", + " ('is', 'a'): 142,\n", + " ('sa', 'b'): 76,\n", + " ('ab', 'e'): 173,\n", + " ('be', 'l'): 201,\n", + " ('el', 'l'): 822,\n", + " ('ll', 'a'): 337,\n", + " ('la', '.'): 684,\n", + " ('.s', 'o'): 152,\n", + " ('so', 'p'): 21,\n", + " ('op', 'h'): 37,\n", + " ('ph', 'i'): 61,\n", + " ('hi', 'a'): 81,\n", + " ('.c', 'h'): 352,\n", + " ('ch', 'a'): 236,\n", + " ('ha', 'r'): 329,\n", + " ('ar', 'l'): 287,\n", + " ('rl', 'o'): 44,\n", + " ('lo', 't'): 14,\n", + " ('ot', 't'): 34,\n", + " ('tt', 'e'): 121,\n", + " ('te', '.'): 175,\n", + " ('.m', 'i'): 393,\n", + " ('mi', 'a'): 95,\n", + " ('.a', 'm'): 384,\n", + " ('am', 'e'): 226,\n", + " ('me', 'l'): 188,\n", + " ('el', 'i'): 537,\n", + " ('li', 'a'): 518,\n", + " ('.h', 'a'): 505,\n", + " ('ar', 'p'): 8,\n", + " ('rp', 'e'): 5,\n", + " 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" ('da', 'y'): 90,\n", + " ('ma', 'i'): 120,\n", + " ('is', 'i'): 35,\n", + " ('ay', 's'): 133,\n", + " ('ys', 'i'): 45,\n", + " ('ka', 'r'): 218,\n", + " ('ee', 'n'): 200,\n", + " ('ho', 'l'): 40,\n", + " ('.a', 'z'): 152,\n", + " ('az', 'a'): 123,\n", + " ...}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trigrams = {}\n", + "for w in words:\n", + " #chs = [''] + list(w) + ['']\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " trigram = (ch1+ch2, ch3)\n", + " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", + "\n", + "trigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(('ah', '.'), 1714),\n", + " (('na', '.'), 1673),\n", + " (('an', '.'), 1509),\n", + " (('on', '.'), 1503),\n", + " (('.m', 'a'), 1453),\n", + " (('.j', 'a'), 1255),\n", + " (('.k', 'a'), 1254),\n", + " (('en', 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(('.s', 'k'), 55),\n", + " (('by', '.'), 55),\n", + " (('ki', 'a'), 55),\n", + " (('wi', 'n'), 55),\n", + " (('ta', 'v'), 55),\n", + " (('jo', 'h'), 55),\n", + " (('la', 's'), 55),\n", + " (('ed', 'a'), 55),\n", + " (('fa', 'r'), 55),\n", + " (('ze', 'n'), 55),\n", + " (('ja', '.'), 55),\n", + " (('.a', 'e'), 55),\n", + " (('.h', 'i'), 55),\n", + " (('it', '.'), 55),\n", + " (('ay', 'm'), 55),\n", + " (('ta', 'i'), 55),\n", + " (('ia', 's'), 55),\n", + " (('li', 'v'), 54),\n", + " (('fi', 'n'), 54),\n", + " (('jo', 'r'), 54),\n", + " (('my', '.'), 54),\n", + " (('rn', 'e'), 54),\n", + " (('bl', 'a'), 54),\n", + " (('ia', 'r'), 54),\n", + " (('al', 'd'), 54),\n", + " (('am', 'r'), 54),\n", + " (('fr', 'a'), 54),\n", + " (('nt', 'o'), 54),\n", + " (('si', '.'), 54),\n", + " (('.e', 'i'), 54),\n", + " (('ns', 'h'), 54),\n", + " (('yr', 'e'), 54),\n", + " (('nd', 'y'), 53),\n", + " (('ha', 'v'), 53),\n", + " (('ka', 'h'), 53),\n", + " (('tt', 'a'), 53),\n", + " (('so', 'l'), 53),\n", + " (('ri', 'g'), 53),\n", + " (('ae', 'd'), 53),\n", + " (('no', 'v'), 52),\n", + " (('ck', 'e'), 52),\n", + " (('eg', 'a'), 52),\n", + " (('ia', 'm'), 52),\n", + " (('.p', 'h'), 52),\n", + " (('es', 'a'), 52),\n", + " (('.e', 'z'), 52),\n", + " (('ue', '.'), 52),\n", + " (('hy', 'l'), 52),\n", + " (('ak', 'y'), 52),\n", + " (('aw', 'n'), 52),\n", + " (('aj', 'a'), 52),\n", + " (('ik', 'o'), 52),\n", + " (('dd', 'i'), 51),\n", + " (('ga', 'r'), 51),\n", + " (('nd', 'o'), 51),\n", + " (('ei', 'r'), 51),\n", + " (('se', 'y'), 51),\n", + " (('ti', 'e'), 51),\n", + " (('am', 'b'), 51),\n", + " (('pr', 'i'), 51),\n", + " (('er', 't'), 51),\n", + " (('ri', 'd'), 51),\n", + " (('ki', 'e'), 51),\n", + " (('ja', 'e'), 51),\n", + " (('it', 't'), 51),\n", + " (('hi', 't'), 51),\n", + " (('ai', 'm'), 51),\n", + " (('wn', '.'), 51),\n", + " (('ra', 'd'), 51),\n", + " (('ay', 'o'), 51),\n", + " (('ea', 'l'), 51),\n", + " (('gi', 'a'), 50),\n", + " (('ep', 'h'), 50),\n", + " (('ai', 't'), 50),\n", + " (('ix', '.'), 50),\n", + " (('za', 'h'), 50),\n", + " (('ca', 'y'), 50),\n", + " (('an', 'u'), 50),\n", + " (('ek', '.'), 50),\n", + " (('ri', 'u'), 50),\n", + " (('au', 'd'), 49),\n", + " (('.i', 'v'), 49),\n", + " (('.i', 'r'), 49),\n", + " (('es', 'e'), 49),\n", + " (('my', 'l'), 49),\n", + " (('.f', 'e'), 49),\n", + " (('id', 'y'), 49),\n", + " (('ya', 'l'), 49),\n", + " (('re', 'd'), 49),\n", + " (('ha', 'e'), 49),\n", + " (('gr', 'e'), 49),\n", + " (('ky', 'r'), 49),\n", + " (('ba', 'r'), 49),\n", + " (('an', 'v'), 49),\n", + " (('er', 'm'), 49),\n", + " (('me', 's'), 49),\n", + " (('ja', 'r'), 49),\n", + " (('ar', 'v'), 49),\n", + " (('nt', 'i'), 48),\n", + " (('ur', 'e'), 48),\n", + " (('ke', '.'), 48),\n", + " (('.m', 'c'), 48),\n", + " (('ak', 'o'), 48),\n", + " (('wy', 'n'), 48),\n", + " (('ul', 'a'), 48),\n", + " (('me', 'i'), 48),\n", + " (('na', 'e'), 48),\n", + " (('om', 'e'), 48),\n", + " (('ma', 's'), 48),\n", + " (('ra', 's'), 48),\n", + " (('ja', 'v'), 48),\n", + " (('ui', 'n'), 47),\n", + " (('oe', 'l'), 47),\n", + " (('.z', 'u'), 47),\n", + " (('we', 'n'), 47),\n", + " (('no', 'n'), 47),\n", + " (('ed', 'i'), 47),\n", + " (('in', 'c'), 47),\n", + " (('aa', 'd'), 47),\n", + " (('ei', 's'), 47),\n", + " (('ce', 'n'), 47),\n", + " (('.o', 'r'), 47),\n", + " (('za', 'm'), 47),\n", + " (('za', 'k'), 47),\n", + " (('ir', 'o'), 47),\n", + " (('aa', 'l'), 46),\n", + " (('nz', 'i'), 46),\n", + " (('ee', 's'), 46),\n", + " (('la', 'u'), 46),\n", + " (('on', 'd'), 46),\n", + " (('ny', 'l'), 46),\n", + " (('se', 'a'), 46),\n", + " (('em', 'o'), 46),\n", + " (('.c', 'y'), 46),\n", + " (('si', 'm'), 46),\n", + " (('mi', 's'), 46),\n", + " (('av', 'y'), 46),\n", + " (('ba', '.'), 46),\n", + " (('um', 'a'), 46),\n", + " (('kh', 'y'), 46),\n", + " (('ru', 's'), 46),\n", + " (('ah', 'e'), 46),\n", + " (('rs', 'h'), 46),\n", + " (('ya', 'a'), 46),\n", + " (('pa', 'r'), 45),\n", + " (('he', 'a'), 45),\n", + " (('fe', 'r'), 45),\n", + " (('ys', 'i'), 45),\n", + " (('yd', 'a'), 45),\n", + " (('ma', 'x'), 45),\n", + " (('.g', 'u'), 45),\n", + " (('nv', 'i'), 45),\n", + " (('ze', '.'), 45),\n", + " (('ta', 'm'), 45),\n", + " (('at', 'o'), 45),\n", + " (('jo', 'n'), 45),\n", + " (('aj', '.'), 45),\n", + " (('av', '.'), 45),\n", + " (('rl', 'o'), 44),\n", + " (('sk', 'y'), 44),\n", + " (('nt', 'h'), 44),\n", + " (('it', 'y'), 44),\n", + " (('ja', 'd'), 44),\n", + " (('in', 'l'), 44),\n", + " (('ki', 'm'), 44),\n", + " (('os', 'i'), 44),\n", + " (('ge', '.'), 44),\n", + " (('fr', 'e'), 44),\n", + " (('kl', 'e'), 44),\n", + " (('it', 'z'), 44),\n", + " (('sa', 'h'), 44),\n", + " (('.c', 'i'), 44),\n", + " (('en', 'o'), 44),\n", + " (('de', 'a'), 44),\n", + " (('ay', 'r'), 44),\n", + " (('ev', 'o'), 44),\n", + " (('co', 'n'), 44),\n", + " (('sh', 'o'), 44),\n", + " (('hm', 'a'), 44),\n", + " (('ec', 'k'), 44),\n", + " (('ja', 'x'), 44),\n", + " (('ba', 's'), 44),\n", + " (('dh', 'a'), 44),\n", + " (('on', 'y'), 43),\n", + " (('si', 'd'), 43),\n", + " (('ye', '.'), 43),\n", + " (('ys', 't'), 43),\n", + " (('lo', 'u'), 43),\n", + " (('en', 'y'), 43),\n", + " (('yv', 'i'), 43),\n", + " (('sm', 'a'), 43),\n", + " (('uh', 'a'), 43),\n", + " (('ve', 'e'), 43),\n", + " (('mi', 't'), 43),\n", + " (('th', 'o'), 43),\n", + " (('ym', 'a'), 43),\n", + " (('di', 's'), 42),\n", + " ...]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(trigrams.items(), key = lambda item: -item[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n" + ] + } + ], + "source": [ + "group_chars = sorted(list(set(\"\".join(words))))\n", + "print(group_chars)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the dataset\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "num = xs.nelement()\n", + "\n", + "print(\"Number of examples: \", num)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb new file mode 100644 index 00000000..3be0be89 --- /dev/null +++ b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb @@ -0,0 +1,198920 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises:\n", + "E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model?\n", + "\n", + "E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n", + "\n", + "E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n", + "\n", + "E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of F.one_hot in favor of simply indexing into rows of W?\n", + "\n", + "E05: look up and use F.cross_entropy instead. You should achieve the same result. Can you think of why we'd prefer to use F.cross_entropy instead?\n", + "\n", + "E06: meta-exercise! Think of a fun/interesting exercise and complete it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "32033\n" + ] + } + ], + "source": [ + "print(len(words))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n", + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))\n", + "\n", + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('.e', 'm'): 288,\n", + " ('em', 'm'): 100,\n", + " ('mm', 'a'): 72,\n", + " ('ma', '.'): 174,\n", + " ('.o', 'l'): 104,\n", + " ('ol', 'i'): 69,\n", + " ('li', 'v'): 54,\n", + " ('iv', 'i'): 78,\n", + " ('vi', 'a'): 147,\n", + " ('ia', '.'): 903,\n", + " ('.a', 'v'): 243,\n", + " ('av', 'a'): 161,\n", + " ('va', '.'): 93,\n", + " ('.i', 's'): 124,\n", + " ('is', 'a'): 142,\n", + " ('sa', 'b'): 76,\n", + " ('ab', 'e'): 173,\n", + " ('be', 'l'): 201,\n", + " ('el', 'l'): 822,\n", + " ('ll', 'a'): 337,\n", + " ('la', '.'): 684,\n", + " ('.s', 'o'): 152,\n", + " ('so', 'p'): 21,\n", + " ('op', 'h'): 37,\n", + " ('ph', 'i'): 61,\n", + " ('hi', 'a'): 81,\n", + " ('.c', 'h'): 352,\n", + " ('ch', 'a'): 236,\n", + " ('ha', 'r'): 329,\n", + " ('ar', 'l'): 287,\n", + " ('rl', 'o'): 44,\n", + " ('lo', 't'): 14,\n", + " ('ot', 't'): 34,\n", + " ('tt', 'e'): 121,\n", + " ('te', '.'): 175,\n", + " ('.m', 'i'): 393,\n", + " ('mi', 'a'): 95,\n", + " ('.a', 'm'): 384,\n", + " ('am', 'e'): 226,\n", + " ('me', 'l'): 188,\n", + " ('el', 'i'): 537,\n", + " ('li', 'a'): 518,\n", + " ('.h', 'a'): 505,\n", + " ('ar', 'p'): 8,\n", + " ('rp', 'e'): 5,\n", + " ('pe', 'r'): 77,\n", + " ('er', '.'): 683,\n", + " ('.e', 'v'): 154,\n", + " ('ev', 'e'): 142,\n", + " ('ve', 'l'): 76,\n", + " ('el', 'y'): 353,\n", + " ('ly', 'n'): 976,\n", + " ('yn', '.'): 953,\n", + " ('.a', 'b'): 190,\n", + " ('ab', 'i'): 76,\n", + " ('bi', 'g'): 15,\n", + " ('ig', 'a'): 35,\n", + " ('ga', 'i'): 18,\n", + " ('ai', 'l'): 259,\n", + " ('il', '.'): 119,\n", + " ('em', 'i'): 160,\n", + " ('mi', 'l'): 259,\n", + " ('il', 'y'): 203,\n", + " ('ly', '.'): 270,\n", + " ('.e', 'l'): 488,\n", + " ('li', 'z'): 81,\n", + " ('iz', 'a'): 93,\n", + " ('za', 'b'): 40,\n", + " ('be', 't'): 61,\n", + " ('et', 'h'): 114,\n", + " ('th', '.'): 178,\n", + " ('il', 'a'): 279,\n", + " ('av', 'e'): 166,\n", + " ('ve', 'r'): 160,\n", + " ('er', 'y'): 84,\n", + " ('ry', '.'): 201,\n", + " ('so', 'f'): 18,\n", + " ('of', 'i'): 19,\n", + " ('fi', 'a'): 20,\n", + " ('.c', 'a'): 628,\n", + " ('ca', 'm'): 92,\n", + " ('am', 'i'): 355,\n", + " ('.a', 'r'): 482,\n", + " ('ar', 'i'): 950,\n", + " ('ri', 'a'): 499,\n", + " ('.s', 'c'): 27,\n", + " ('sc', 'a'): 14,\n", + " ('ca', 'r'): 193,\n", + " ('rl', 'e'): 146,\n", + " ('le', 't'): 91,\n", + " ('et', 't'): 203,\n", + " ('tt', '.'): 102,\n", + " ('.v', 'i'): 161,\n", + " ('vi', 'c'): 26,\n", + " ('ic', 't'): 21,\n", + " ('ct', 'o'): 22,\n", + " ('to', 'r'): 110,\n", + " ('or', 'i'): 220,\n", + " ('.m', 'a'): 1453,\n", + " ('ma', 'd'): 136,\n", + " ('ad', 'i'): 190,\n", + " ('di', 's'): 42,\n", + " ('is', 'o'): 68,\n", + " ('so', 'n'): 341,\n", + " ('on', '.'): 1503,\n", + " ('.l', 'u'): 162,\n", + " ('lu', 'n'): 25,\n", + " ('un', 'a'): 61,\n", + " ('na', '.'): 1673,\n", + " ('.g', 'r'): 165,\n", + " ('gr', 'a'): 120,\n", + " ('ra', 'c'): 103,\n", + " ('ac', 'e'): 129,\n", + " ('ce', '.'): 234,\n", + " ('ch', 'l'): 22,\n", + " ('hl', 'o'): 24,\n", + " ('lo', 'e'): 17,\n", + " ('oe', '.'): 28,\n", + " ('.p', 'e'): 92,\n", + " ('pe', 'n'): 31,\n", + " ('en', 'e'): 151,\n", + " ('ne', 'l'): 145,\n", + " ('el', 'o'): 83,\n", + " ('lo', 'p'): 11,\n", + " ('op', 'e'): 33,\n", + " ('pe', '.'): 26,\n", + " ('.l', 'a'): 463,\n", + " ('la', 'y'): 234,\n", + " ('ay', 'l'): 483,\n", + " ('yl', 'a'): 381,\n", + " ('.r', 'i'): 201,\n", + " ('ri', 'l'): 62,\n", + " ('il', 'e'): 197,\n", + " ('le', 'y'): 443,\n", + " ('ey', '.'): 602,\n", + " ('.z', 'o'): 72,\n", + " ('zo', 'e'): 19,\n", + " ('oe', 'y'): 14,\n", + " ('.n', 'o'): 156,\n", + " ('no', 'r'): 101,\n", + " ('or', 'a'): 202,\n", + " ('ra', '.'): 627,\n", + " ('.l', 'i'): 269,\n", + " ('li', 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't'): 102,\n", + " ('rt', 'e'): 28,\n", + " ('an', 'y'): 175,\n", + " ('ca', 'd'): 19,\n", + " ('en', 'c'): 58,\n", + " ('ma', 't'): 98,\n", + " ('ti', 'l'): 33,\n", + " ('il', 'd'): 17,\n", + " ('sy', 'l'): 18,\n", + " ('yl', 'v'): 10,\n", + " ('lv', 'i'): 37,\n", + " ('my', 'r'): 36,\n", + " ('yr', 'a'): 78,\n", + " ('er', 'n'): 41,\n", + " ('rn', 'a'): 35,\n", + " ('na', 'n'): 104,\n", + " ('ha', 't'): 20,\n", + " ('at', 't'): 73,\n", + " ('tt', 'i'): 38,\n", + " ('da', 'y'): 90,\n", + " ('ma', 'i'): 120,\n", + " ('is', 'i'): 35,\n", + " ('ay', 's'): 133,\n", + " ('ys', 'i'): 45,\n", + " ('ka', 'r'): 218,\n", + " ('ee', 'n'): 200,\n", + " ('ho', 'l'): 40,\n", + " ('.a', 'z'): 152,\n", + " ('az', 'a'): 123,\n", + " ...}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trigrams = {}\n", + "for w in words:\n", + " #chs = [''] + list(w) + ['']\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " trigram = (ch1+ch2, ch3)\n", + " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", + "\n", + "trigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(('ah', '.'), 1714),\n", + " (('na', '.'), 1673),\n", + " (('an', '.'), 1509),\n", + " (('on', '.'), 1503),\n", + " (('.m', 'a'), 1453),\n", + " (('.j', 'a'), 1255),\n", + " (('.k', 'a'), 1254),\n", + " (('en', '.'), 1217),\n", + " (('ly', 'n'), 976),\n", + " (('yn', '.'), 953),\n", + " (('ar', 'i'), 950),\n", + " (('ia', '.'), 903),\n", + " (('ie', '.'), 858),\n", + " (('an', 'n'), 825),\n", + " (('el', 'l'), 822),\n", + " (('an', 'a'), 804),\n", + " (('ia', 'n'), 790),\n", + " (('ma', 'r'), 776),\n", + " (('in', '.'), 766),\n", + " (('el', '.'), 727),\n", + " (('ya', '.'), 716),\n", + " (('an', 'i'), 703),\n", + " (('.d', 'a'), 700),\n", + " (('la', '.'), 684),\n", + " (('er', '.'), 683),\n", + " (('iy', 'a'), 669),\n", + " 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(('an', 'v'), 49),\n", + " (('er', 'm'), 49),\n", + " (('me', 's'), 49),\n", + " (('ja', 'r'), 49),\n", + " (('ar', 'v'), 49),\n", + " (('nt', 'i'), 48),\n", + " (('ur', 'e'), 48),\n", + " (('ke', '.'), 48),\n", + " (('.m', 'c'), 48),\n", + " (('ak', 'o'), 48),\n", + " (('wy', 'n'), 48),\n", + " (('ul', 'a'), 48),\n", + " (('me', 'i'), 48),\n", + " (('na', 'e'), 48),\n", + " (('om', 'e'), 48),\n", + " (('ma', 's'), 48),\n", + " (('ra', 's'), 48),\n", + " (('ja', 'v'), 48),\n", + " (('ui', 'n'), 47),\n", + " (('oe', 'l'), 47),\n", + " (('.z', 'u'), 47),\n", + " (('we', 'n'), 47),\n", + " (('no', 'n'), 47),\n", + " (('ed', 'i'), 47),\n", + " (('in', 'c'), 47),\n", + " (('aa', 'd'), 47),\n", + " (('ei', 's'), 47),\n", + " (('ce', 'n'), 47),\n", + " (('.o', 'r'), 47),\n", + " (('za', 'm'), 47),\n", + " (('za', 'k'), 47),\n", + " (('ir', 'o'), 47),\n", + " (('aa', 'l'), 46),\n", + " (('nz', 'i'), 46),\n", + " (('ee', 's'), 46),\n", + " (('la', 'u'), 46),\n", + " (('on', 'd'), 46),\n", + " (('ny', 'l'), 46),\n", + " (('se', 'a'), 46),\n", + " (('em', 'o'), 46),\n", + " (('.c', 'y'), 46),\n", + " (('si', 'm'), 46),\n", + " (('mi', 's'), 46),\n", + " (('av', 'y'), 46),\n", + " (('ba', '.'), 46),\n", + " (('um', 'a'), 46),\n", + " (('kh', 'y'), 46),\n", + " (('ru', 's'), 46),\n", + " (('ah', 'e'), 46),\n", + " (('rs', 'h'), 46),\n", + " (('ya', 'a'), 46),\n", + " (('pa', 'r'), 45),\n", + " (('he', 'a'), 45),\n", + " (('fe', 'r'), 45),\n", + " (('ys', 'i'), 45),\n", + " (('yd', 'a'), 45),\n", + " (('ma', 'x'), 45),\n", + " (('.g', 'u'), 45),\n", + " (('nv', 'i'), 45),\n", + " (('ze', '.'), 45),\n", + " (('ta', 'm'), 45),\n", + " (('at', 'o'), 45),\n", + " (('jo', 'n'), 45),\n", + " (('aj', '.'), 45),\n", + " (('av', '.'), 45),\n", + " (('rl', 'o'), 44),\n", + " (('sk', 'y'), 44),\n", + " (('nt', 'h'), 44),\n", + " (('it', 'y'), 44),\n", + " (('ja', 'd'), 44),\n", + " (('in', 'l'), 44),\n", + " (('ki', 'm'), 44),\n", + " (('os', 'i'), 44),\n", + " (('ge', '.'), 44),\n", + " (('fr', 'e'), 44),\n", + " (('kl', 'e'), 44),\n", + " (('it', 'z'), 44),\n", + " (('sa', 'h'), 44),\n", + " (('.c', 'i'), 44),\n", + " (('en', 'o'), 44),\n", + " (('de', 'a'), 44),\n", + " (('ay', 'r'), 44),\n", + " (('ev', 'o'), 44),\n", + " (('co', 'n'), 44),\n", + " (('sh', 'o'), 44),\n", + " (('hm', 'a'), 44),\n", + " (('ec', 'k'), 44),\n", + " (('ja', 'x'), 44),\n", + " (('ba', 's'), 44),\n", + " (('dh', 'a'), 44),\n", + " (('on', 'y'), 43),\n", + " (('si', 'd'), 43),\n", + " (('ye', '.'), 43),\n", + " (('ys', 't'), 43),\n", + " (('lo', 'u'), 43),\n", + " (('en', 'y'), 43),\n", + " (('yv', 'i'), 43),\n", + " (('sm', 'a'), 43),\n", + " (('uh', 'a'), 43),\n", + " (('ve', 'e'), 43),\n", + " (('mi', 't'), 43),\n", + " (('th', 'o'), 43),\n", + " (('ym', 'a'), 43),\n", + " (('di', 's'), 42),\n", + " ...]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_trigrams = sorted(trigrams.items(), key = lambda item: -item[1])\n", + "sorted_trigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "628\n" + ] + } + ], + "source": [ + "elements = []\n", + "\n", + "for trigram in sorted_trigrams:\n", + " elements.append(trigram[0][0])\n", + " elements.append(trigram[0][1])\n", + "\n", + "\n", + "elements = list(set(elements))\n", + "\n", + "print(len(elements))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "eltoi = {el: i for i, el in enumerate(elements)}\n", + "#print(eltoi)\n", + "eltoi[\".\"] = 999" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# int to string mapping\n", + "itoel = {i:el for i, el in enumerate(elements)}\n", + "#print(itoel)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".e m: 20 40\n", + "em m: 210 40\n", + "mm a: 393 177\n", + "ma .: 201 999\n", + ".o l: 144 285\n", + "ol i: 162 492\n", + "li v: 620 572\n", + "iv i: 93 492\n", + "vi a: 108 177\n", + "ia .: 95 999\n", + ".a v: 477 572\n", + "av a: 609 177\n", + "va .: 43 999\n", + ".i s: 599 236\n", + "is a: 136 177\n", + "sa b: 514 182\n", + "ab e: 493 135\n", + "be l: 454 285\n", + "el l: 597 285\n", + "ll a: 414 177\n", + "la .: 416 999\n", + ".s o: 271 384\n", + "so p: 1 537\n", + "op h: 120 259\n", + "ph i: 435 492\n", + "hi a: 424 177\n", + "ia .: 95 999\n", + ".c h: 401 259\n", + "ch a: 146 177\n", + "ha r: 114 38\n", + "ar l: 368 285\n", + "rl o: 612 384\n", + "lo t: 515 433\n", + "ot t: 505 433\n", + "tt e: 603 135\n", + "te .: 30 999\n", + ".m i: 11 492\n", + "mi a: 444 177\n", + "ia .: 95 999\n", + ".a m: 477 40\n", + "am e: 288 135\n", + "me l: 618 285\n", + "el i: 597 492\n", + "li a: 620 177\n", + "ia .: 95 999\n", + ".h a: 327 177\n", + "ha r: 114 38\n", + "ar p: 368 537\n", + "rp e: 533 135\n", + "pe r: 56 38\n", + "er .: 518 999\n", + ".e v: 20 572\n", + "ev e: 82 135\n", + "ve l: 528 285\n", + "el y: 597 115\n", + "ly n: 348 420\n", + "yn .: 45 999\n", + ".a b: 477 182\n", + "ab i: 493 492\n", + "bi g: 484 389\n", + "ig a: 12 177\n", + "ga i: 497 492\n", + "ai l: 499 285\n", + "il .: 193 999\n", + ".e m: 20 40\n", + "em i: 210 492\n", + "mi l: 444 285\n", + "il y: 193 115\n", + "ly .: 348 999\n", + ".e l: 20 285\n", + "el i: 597 492\n", + "li z: 620 340\n", + "iz a: 25 177\n", + "za b: 237 182\n", + "ab e: 493 135\n", + "be t: 454 433\n", + "et h: 574 259\n", + "th .: 520 999\n", + ".m i: 11 492\n", + "mi l: 444 285\n", + "il a: 193 177\n", + "la .: 416 999\n", + ".e l: 20 285\n", + "el l: 597 285\n", + "ll a: 414 177\n", + "la .: 416 999\n", + ".a v: 477 572\n", + "av e: 609 135\n", + "ve r: 528 38\n", + "er y: 518 115\n", + "ry .: 471 999\n", + ".s o: 271 384\n", + "so f: 1 124\n", + "of i: 575 492\n", + "fi a: 171 177\n", + "ia .: 95 999\n", + ".c a: 401 177\n", + "ca m: 357 40\n", + "am i: 288 492\n", + "mi l: 444 285\n", + "il a: 193 177\n", + "la .: 416 999\n", + ".a r: 477 38\n", + "ar i: 368 492\n", + "ri a: 318 177\n", + "ia .: 95 999\n", + ".s c: 271 39\n", + "sc a: 36 177\n", + "ca r: 357 38\n", + "ar l: 368 285\n", + "rl e: 612 135\n", + "le t: 222 433\n", + "et t: 574 433\n", + "tt .: 603 999\n", + ".v i: 111 492\n", + "vi c: 108 39\n", + "ic t: 510 433\n", + "ct o: 359 384\n", + "to r: 325 38\n", + "or i: 215 492\n", + "ri a: 318 177\n", + "ia .: 95 999\n", + ".m a: 11 177\n", + "ma d: 201 551\n", + "ad i: 521 492\n", + "di s: 27 236\n", + "is o: 136 384\n", + "so n: 1 420\n", + "on .: 57 999\n", + ".l u: 289 465\n", + "lu n: 462 420\n", + "un a: 595 177\n", + "na .: 296 999\n", + ".g r: 395 38\n", + "gr a: 294 177\n", + "ra c: 142 39\n", + "ac e: 126 135\n", + "ce .: 173 999\n", + ".c h: 401 259\n", + "ch l: 146 285\n", + "hl o: 178 384\n", + "lo e: 515 135\n", + "oe .: 602 999\n", + ".p e: 71 135\n", + "pe n: 56 420\n", + "en e: 482 135\n", + "ne l: 60 285\n", + "el o: 597 384\n", + "lo p: 515 537\n", + "op e: 120 135\n", + "pe .: 56 999\n", + ".l a: 289 177\n", + "la y: 416 115\n", + "ay l: 141 285\n", + "yl a: 118 177\n", + "la .: 416 999\n", + ".r i: 132 492\n", + "ri l: 318 285\n", + "il e: 193 135\n", + "le y: 222 115\n", + "ey .: 228 999\n", + ".z o: 379 384\n", + "zo e: 41 135\n", + "oe y: 602 115\n", + "ey .: 228 999\n", + ".n o: 254 384\n", + "no r: 99 38\n", + "or a: 215 177\n", + "ra .: 142 999\n", + ".l i: 289 492\n", + "li l: 620 285\n", + "il y: 193 115\n", + "ly .: 348 999\n", + ".e l: 20 285\n", + "el e: 597 135\n", + "le a: 222 177\n", + "ea n: 225 420\n", + "an o: 68 384\n", + "no r: 99 38\n", + "or .: 215 999\n", + ".h a: 327 177\n", + "ha n: 114 420\n", + "an n: 68 420\n", + "nn a: 567 177\n", + "na h: 296 259\n", + "ah .: 589 999\n", + ".l i: 289 492\n", + "li l: 620 285\n", + "il l: 193 285\n", + "ll i: 414 492\n", + "li a: 620 177\n", + "ia n: 95 420\n", + "an .: 68 999\n", + ".a d: 477 551\n", + "ad d: 521 551\n", + "dd i: 346 492\n", + "di s: 27 236\n", + "is o: 136 384\n", + "so n: 1 420\n", + "on .: 57 999\n", + ".a u: 477 465\n", + "au b: 131 182\n", + "ub r: 212 38\n", + "br e: 169 135\n", + "re y: 608 115\n", + "ey .: 228 999\n", + ".e l: 20 285\n", + "el l: 597 285\n", + "ll i: 414 492\n", + "li e: 620 135\n", + "ie .: 15 999\n", + ".s t: 271 433\n", + "st e: 404 135\n", + "te l: 30 285\n", + "el l: 597 285\n", + "ll a: 414 177\n", + "la .: 416 999\n", + ".n a: 254 177\n", + "na t: 296 433\n", + "at a: 579 177\n", + "ta l: 467 285\n", + "al i: 247 492\n", + "li e: 620 135\n", + "ie .: 15 999\n", + ".z o: 379 384\n", + "zo e: 41 135\n", + "oe .: 602 999\n", + ".l e: 289 135\n", + "le a: 222 177\n", + "ea h: 225 259\n", + "ah .: 589 999\n", + ".h a: 327 177\n", + "ha z: 114 340\n", + "az e: 441 135\n", + "ze l: 127 285\n", + "el .: 597 999\n", + ".v i: 111 492\n", + "vi o: 108 384\n", + "io l: 578 285\n", + "ol e: 162 135\n", + "le t: 222 433\n", + "et .: 574 999\n", + ".a u: 477 465\n", + "au r: 131 38\n", + "ur o: 290 384\n", + "ro r: 623 38\n", + "or a: 215 177\n", + "ra .: 142 999\n", + ".s a: 271 177\n", + "sa v: 514 572\n", + "av a: 609 177\n", + "va n: 43 420\n", + "an n: 68 420\n", + "nn a: 567 177\n", + "na h: 296 259\n", + "ah .: 589 999\n", + ".a u: 477 465\n", + "au d: 131 551\n", + "ud r: 2 38\n", + "dr e: 88 135\n", + "re y: 608 115\n", + "ey .: 228 999\n", + ".b r: 598 38\n", + "br o: 169 384\n", + "ro o: 623 384\n", + "oo k: 102 4\n", + "ok l: 313 285\n", + "kl y: 627 115\n", + "ly n: 348 420\n", + "yn .: 45 999\n", + ".b e: 598 135\n", + "be l: 454 285\n", + "el l: 597 285\n", + "ll a: 414 177\n", + "la .: 416 999\n", + ".c l: 401 285\n", + "cl a: 406 177\n", + "la i: 416 492\n", + "ai r: 499 38\n", + "ir e: 543 135\n", + "re .: 608 999\n", + ".s k: 271 4\n", + "sk y: 92 115\n", + "ky l: 287 285\n", + "yl a: 118 177\n", + "la r: 416 38\n", + "ar .: 368 999\n", + ".l u: 289 465\n", + "lu c: 462 39\n", + "uc y: 109 115\n", + "cy .: 611 999\n", + ".p a: 71 177\n", + "pa i: 291 492\n", + "ai s: 499 236\n", + "is l: 136 285\n", + "sl e: 72 135\n", + "le y: 222 115\n", + "ey .: 228 999\n", + ".e v: 20 572\n", + "ev e: 82 135\n", + "ve r: 528 38\n", + "er l: 518 285\n", + "rl y: 612 115\n", + "ly .: 348 999\n", + ".a n: 477 420\n", + "an n: 68 420\n", + "nn a: 567 177\n", + "na .: 296 999\n", + ".c a: 401 177\n", + "ca r: 357 38\n", + "ar o: 368 384\n", + "ro l: 623 285\n", + "ol i: 162 492\n", + "li n: 620 420\n", + "in e: 617 135\n", + "ne .: 60 999\n", + ".n o: 254 384\n", + "no v: 99 572\n", + "ov a: 256 177\n", + "va .: 43 999\n", + ".g e: 395 135\n", + "ge n: 198 420\n", + "en e: 482 135\n", + "ne s: 60 236\n", + "es i: 298 492\n", + "si s: 240 236\n", + "is .: 136 999\n", + ".e m: 20 40\n", + "em i: 210 492\n", + "mi l: 444 285\n", + "il i: 193 492\n", + "li a: 620 177\n", + "ia .: 95 999\n", + ".k e: 412 135\n", + "ke n: 397 420\n", + "en n: 482 420\n", + "nn e: 567 135\n", + "ne d: 60 551\n", + "ed y: 583 115\n", + "dy .: 234 999\n", + ".s a: 271 177\n", + "sa m: 514 40\n", + "am a: 288 177\n", + "ma n: 201 420\n", + "an t: 68 433\n", + "nt h: 112 259\n", + "th a: 520 177\n", + "ha .: 114 999\n", + ".m a: 11 177\n", + "ma y: 201 115\n", + "ay a: 141 177\n", + "ya .: 437 999\n", + ".w i: 172 492\n", + "wi l: 418 285\n", + "il l: 193 285\n", + "ll o: 414 384\n", + "lo w: 515 590\n", + "ow .: 604 999\n", + ".k i: 412 492\n", + "ki n: 378 420\n", + "in s: 617 236\n", + "ns l: 343 285\n", + "sl e: 72 135\n", + "le y: 222 115\n", + "ey .: 228 999\n", + ".n a: 254 177\n", + "na o: 296 384\n", + "ao m: 450 40\n", + "om i: 527 492\n", + "mi .: 444 999\n", + ".a a: 477 177\n", + "aa l: 87 285\n", + "al i: 247 492\n", + "li y: 620 115\n", + "iy a: 13 177\n", + "ya h: 437 259\n", + "ah .: 589 999\n", + ".e l: 20 285\n", + "el e: 597 135\n", + "le n: 222 420\n", + "en a: 482 177\n", + "na .: 296 999\n", + ".s a: 271 177\n", + "sa r: 514 38\n", + "ar a: 368 177\n", + "ra h: 142 259\n", + "ah .: 589 999\n", + ".a r: 477 38\n", + "ar i: 368 492\n", + "ri a: 318 177\n", + "ia n: 95 420\n", + "an a: 68 177\n", + "na .: 296 999\n", + ".a l: 477 285\n", + "al l: 247 285\n", + "ll i: 414 492\n", + "li s: 620 236\n", + "is o: 136 384\n", + "so n: 1 420\n", + "on .: 57 999\n", + ".g a: 395 177\n", + "ga b: 497 182\n", + "ab r: 493 38\n", + "br i: 169 492\n", + "ri e: 318 135\n", + "ie l: 15 285\n", + "el l: 597 285\n", + "ll a: 414 177\n", + "la .: 416 999\n", + ".a l: 477 285\n", + "al i: 247 492\n", + "li c: 620 39\n", + "ic e: 510 135\n", + "ce .: 173 999\n", + ".m a: 11 177\n", + "ma d: 201 551\n", + "ad e: 521 135\n", + "de l: 189 285\n", + "el y: 597 115\n", + "ly n: 348 420\n", + "yn .: 45 999\n", + ".c o: 401 384\n", + "co r: 191 38\n", + "or a: 215 177\n", + "ra .: 142 999\n", + ".r u: 132 465\n", + "ru b: 464 182\n", + "ub y: 212 115\n", + "by .: 217 999\n", + ".e v: 20 572\n", + "ev a: 82 177\n", + "va .: 43 999\n", + ".s e: 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"im .: 337 999\n", + ".z i: 379 492\n", + "zi n: 34 420\n", + "in .: 617 999\n", + ".z i: 379 492\n", + "zi s: 34 236\n", + "is h: 136 259\n", + "sh e: 205 135\n", + "he .: 498 999\n", + ".z m: 379 40\n", + "zm a: 409 177\n", + "ma r: 201 38\n", + "ar i: 368 492\n", + "ri .: 318 999\n", + ".z o: 379 384\n", + "zo e: 41 135\n", + "oe l: 602 285\n", + "el .: 597 999\n", + ".z o: 379 384\n", + "zo l: 41 285\n", + "ol a: 162 177\n", + "la .: 416 999\n", + ".z u: 379 465\n", + "zu b: 134 182\n", + "ub e: 212 135\n", + "be r: 454 38\n", + "er .: 518 999\n", + ".z u: 379 465\n", + "zu b: 134 182\n", + "ub e: 212 135\n", + "be y: 454 115\n", + "ey r: 228 38\n", + "yr .: 156 999\n", + ".z y: 379 115\n", + "zy e: 181 135\n", + "ye l: 166 285\n", + "el l: 597 285\n", + "ll .: 414 999\n", + ".z y: 379 115\n", + "zy h: 181 259\n", + "yh e: 104 135\n", + "he e: 498 135\n", + "ee m: 269 40\n", + "em .: 210 999\n", + ".z y: 379 115\n", + "zy k: 181 4\n", + "yk e: 246 135\n", + "ke e: 397 135\n", + "ee m: 269 40\n", + "em .: 210 999\n", + ".z y: 379 115\n", + "zy l: 181 285\n", + "yl a: 118 177\n", + "la s: 416 236\n", + "as .: 581 999\n", + ".z y: 379 115\n", + "zy r: 181 38\n", + "yr a: 156 177\n", + "ra n: 142 420\n", + "an .: 68 999\n", + ".z y: 379 115\n", + "zy r: 181 38\n", + "yr i: 156 492\n", + "ri e: 318 135\n", + "ie .: 15 999\n", + ".z y: 379 115\n", + "zy r: 181 38\n", + "yr o: 156 384\n", + "ro n: 623 420\n", + "on .: 57 999\n", + ".z z: 379 340\n", + "zz y: 138 115\n", + "zy z: 181 340\n", + "yz x: 544 555\n", + "zx .: 168 999\n", + "tensor([ 20, 210, 393, ..., 181, 544, 168])\n", + "tensor([ 40, 40, 177, ..., 340, 555, 999])\n" + ] + } + ], + "source": [ + "# First let's create a training set of bigrams (x, y)\n", + "\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " el1 = ch1+ch2\n", + " el2 = ch3\n", + "\n", + " ix1 = eltoi[el1]\n", + " ix2 = eltoi[el2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " print(f\"{el1} {el2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "\n", + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples: 196113\n" + ] + } + ], + "source": [ + "# Create the dataset\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " el1 = ch1+ch2\n", + " el2 = ch3\n", + "\n", + " ix1 = eltoi[el1]\n", + " ix2 = eltoi[el2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "num = xs.nelement()\n", + "\n", + "print(\"Number of examples: \", num)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "W = torch.randn((628, 628), generator=g, requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 0%| | 0/1000 [00:00= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", + "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [10,0,0], thread: [11,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", + 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failed.\n", + "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [52,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", + "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [57,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", + "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [62,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", + "Epoch 0: 0%| | 0/1000 [00:01\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[39m# Backward pass\u001b[39;00m\n\u001b[1;32m 32\u001b[0m W\u001b[39m.\u001b[39mgrad \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m# set gradient to zero\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[1;32m 35\u001b[0m \u001b[39m# update\u001b[39;00m\n\u001b[1;32m 36\u001b[0m W\u001b[39m.\u001b[39mdata \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m-\u001b[39mlr \u001b[39m*\u001b[39m W\u001b[39m.\u001b[39mgrad \u001b[39m# gradient descent\u001b[39;00m\n", + "File \u001b[0;32m~/venvs/deeply/lib/python3.10/site-packages/torch/_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 389\u001b[0m Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[1;32m 390\u001b[0m (\u001b[39mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 394\u001b[0m create_graph\u001b[39m=\u001b[39mcreate_graph,\n\u001b[1;32m 395\u001b[0m inputs\u001b[39m=\u001b[39minputs)\n\u001b[0;32m--> 396\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs)\n", + "File \u001b[0;32m~/venvs/deeply/lib/python3.10/site-packages/torch/autograd/__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 168\u001b[0m retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[1;32m 170\u001b[0m \u001b[39m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward( \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 174\u001b[0m tensors, grad_tensors_, retain_graph, create_graph, inputs,\n\u001b[1;32m 175\u001b[0m allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1." + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 75\n", + "alpha = 0.01\n", + "\n", + "t_start = time.time()\n", + "\n", + "# Gpu accelerated now\n", + "#W.to(device)\n", + "xs.to(device)\n", + "ys.to(device)\n", + "\n", + "with tqdm(range(1000), unit=\"Epoch\") as tepoch:\n", + " for epoch in tepoch:\n", + " tepoch.set_description(f\"Epoch {epoch}\")\n", + " \n", + "\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=628).float().to(device)\n", + "\n", + " logits = xenc @ W.to(device) # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = (counts / counts.sum(1, keepdim=True)) \n", + " \n", + " # print(probs.shape)\n", + " # print(ys.shape)\n", + "\n", + " # # regularised loss\n", + " loss = -probs[torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", + "\n", + " # Backward pass\n", + " W.grad = None # set gradient to zero\n", + " loss.backward()\n", + "\n", + " # update\n", + " W.data += -lr * W.grad # gradient descent\n", + "\n", + " tepoch.set_postfix(loss=loss.item(), time=time.time() - t_start)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# finally, sample from the 'neural net' model\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "\n", + "for i in range(5):\n", + " \n", + " out = []\n", + " ix = 0\n", + " while True:\n", + " \n", + " # ----------\n", + " # BEFORE:\n", + " #p = P[ix]\n", + " # ----------\n", + " # NOW:\n", + " xenc = F.one_hot(torch.tensor([ix]), num_classes=628).float()\n", + " logits = xenc @ W # predict log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n", + " # ----------\n", + " \n", + " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", + " out.append(itoel[ix])\n", + " if ix == 999:\n", + " break\n", + " print(''.join(out))\n", + " print()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9ca886e588401d99f4718f075ec6e6c6ca749666 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Fri, 7 Oct 2022 11:26:04 +0200 Subject: [PATCH 14/20] makemore 2 pending --- .../makemore/1_bigram_torch_exercise.ipynb | 4323 +- .../1_bigram_torch_exercise_approach_2.ipynb | 392657 +++++++-------- ...ch.ipynb => 1_makemore_bigram_torch.ipynb} | 0 my_code/makemore/2_makemore_mlp.ipynb | 19 + 4 files changed, 198465 insertions(+), 198534 deletions(-) rename my_code/makemore/{1_bigram_torch.ipynb => 1_makemore_bigram_torch.ipynb} (100%) create mode 100644 my_code/makemore/2_makemore_mlp.ipynb diff --git a/my_code/makemore/1_bigram_torch_exercise.ipynb b/my_code/makemore/1_bigram_torch_exercise.ipynb index 17dfc448..2ee03127 100644 --- a/my_code/makemore/1_bigram_torch_exercise.ipynb +++ b/my_code/makemore/1_bigram_torch_exercise.ipynb @@ -20,7 +20,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -53,1016 +65,1016 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{('.e', 'm'): 288,\n", - " ('em', 'm'): 100,\n", - " ('mm', 'a'): 72,\n", - " ('ma', '.'): 174,\n", - " ('.o', 'l'): 104,\n", - " ('ol', 'i'): 69,\n", - " ('li', 'v'): 54,\n", - " ('iv', 'i'): 78,\n", - " ('vi', 'a'): 147,\n", - " ('ia', '.'): 903,\n", - " ('.a', 'v'): 243,\n", - " ('av', 'a'): 161,\n", - " ('va', 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'a'): 78,\n", + " ('e', 'r', 'n'): 41,\n", + " ('r', 'n', 'a'): 35,\n", + " ('n', 'a', 'n'): 104,\n", + " ('h', 'a', 't'): 20,\n", + " ('a', 't', 't'): 73,\n", + " ('t', 't', 'i'): 38,\n", + " ('d', 'a', 'y'): 90,\n", + " ('m', 'a', 'i'): 120,\n", + " ('i', 's', 'i'): 35,\n", + " ('a', 'y', 's'): 133,\n", + " ('y', 's', 'i'): 45,\n", + " ('k', 'a', 'r'): 218,\n", + " ('e', 'e', 'n'): 200,\n", + " ('h', 'o', 'l'): 40,\n", + " ('.', 'a', 'z'): 152,\n", + " ('a', 'z', 'a'): 123,\n", " ...}" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1073,7 +1085,7 @@ " #chs = [''] + list(w) + ['']\n", " chs = ['.'] + list(w) + ['.']\n", " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " trigram = (ch1+ch2, ch3)\n", + " trigram = (ch1, ch2, ch3)\n", " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", "\n", "trigrams" @@ -1081,1016 +1093,1016 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": 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+ " (('u', 'h', 'a'), 43),\n", + " (('v', 'e', 'e'), 43),\n", + " (('m', 'i', 't'), 43),\n", + " (('t', 'h', 'o'), 43),\n", + " (('y', 'm', 'a'), 43),\n", + " (('d', 'i', 's'), 42),\n", " ...]" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2101,7 +2113,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2113,26 +2125,158 @@ } ], "source": [ - "group_chars = sorted(list(set(\"\".join(words))))\n", - "print(group_chars)" + "chars = sorted(list(set(\"\".join(words))))\n", + "print(chars)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}\n" + ] + } + ], + "source": [ + "# string to int mapping\n", + "stoi = {}\n", + "for i, char in enumerate(chars):\n", + " stoi[char] = i+1\n", + "\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n" + ] + } + ], + "source": [ + "stoi = {char:i+1 for i, char in enumerate(chars)}\n", + "stoi['.'] = 0\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], + "source": [ + "# int to string mapping\n", + "itos = {i+1:char for i, char in enumerate(chars)}\n", + "itos[0] = \".\"\n", + "print(itos)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".em: 0 5 13\n", + "emm: 5 13 13\n", + "mma: 13 13 1\n", + "ma.: 13 1 0\n", + "tensor([[ 0, 5],\n", + " [ 5, 13],\n", + " [13, 13],\n", + " [13, 1]])\n", + "tensor([13, 13, 1, 0])\n" + ] + } + ], + "source": [ + "# First let's create a training set of bigrams (x, y)\n", + "\n", + "xs, ys = [], []\n", + "\n", + "for w in words[:1]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " ix3 = stoi[ch3]\n", + " xs.append((ix1, ix2))\n", + " ys.append(ix3)\n", + " print(f\"{ch1}{ch2}{ch3}: {ix1} {ix2} {ix3}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "\n", + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([4, 2, 27])\n" + ] + } + ], + "source": [ + "xenc = F.one_hot(xs, num_classes=27).float() # we dont want an int we want a float\n", + "print(xenc.shape)\n", + "# plt.imshow(xenc, cmap='Blues')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples: 8\n" + ] + } + ], "source": [ "# Create the dataset\n", "xs, ys = [], []\n", "\n", - "for w in words:\n", + "for w in words[:1]:\n", " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", " ix1 = stoi[ch1]\n", " ix2 = stoi[ch2]\n", - " xs.append(ix1)\n", - " ys.append(ix2)\n", + " ix3 = stoi[ch3]\n", + " xs.append((ix1, ix2))\n", + " ys.append(ix3)\n", " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", "\n", "xs = torch.tensor(xs)\n", @@ -2141,6 +2285,153 @@ "\n", "print(\"Number of examples: \", num)" ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", + "W = torch.zeros((2, 27, 27), requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2, 4, 27])" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xenc = F.one_hot(xs, num_classes=27).float().permute(1, 0, 2)\n", + "xenc.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 4, 27])\n", + "torch.Size([2, 27, 27])\n" + ] + } + ], + "source": [ + "xenc @ W\n", + "\n", + "print(xenc.shape)\n", + "print(W.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([4, 2, 27])\n", + "torch.Size([4])\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 50\n", + "alpha = 0.01\n", + "\n", + "for k in range(1):\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=27).float().permute(1, 0, 2) # input is one-hot encoded\n", + "\n", + " logits = xenc @ W # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = (counts / counts.sum(1, keepdim=True)).permute(1, 0, 2) # probabilities for next character \n", + " \n", + " print(probs.shape)\n", + " print(ys.shape)\n", + "\n", + " # # regularised loss\n", + " # loss = -probs[torch.arange(num), torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", + " # if k % 25 == 0:\n", + " # print('loss:', loss.item())\n", + "\n", + " # # Backward pass\n", + " # W.grad = None # set gradient to zero\n", + " # loss.backward()\n", + "\n", + " # # update\n", + " # W.data += -lr * W.grad # gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([27])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor([0, 1, 2, 3, 4, 5, 6, 7]),\n", + " tensor([0, 1, 2, 3, 4, 5, 6, 7]),\n", + " tensor([13, 13, 1, 0])]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[torch.arange(num), torch.arange(num), ys]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb index 3be0be89..24fda497 100644 --- a/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb +++ b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb @@ -2165,7 +2165,7 @@ "source": [ "eltoi = {el: i for i, el in enumerate(elements)}\n", "#print(eltoi)\n", - "eltoi[\".\"] = 999" + "break_int = eltoi[\".\"]" ] }, { @@ -2188,196121 +2188,196121 @@ "name": "stdout", "output_type": "stream", "text": [ - ".e m: 20 40\n", - "em m: 210 40\n", - "mm a: 393 177\n", - "ma .: 201 999\n", - ".o l: 144 285\n", - "ol i: 162 492\n", - "li v: 620 572\n", - "iv i: 93 492\n", - "vi a: 108 177\n", - "ia .: 95 999\n", - ".a v: 477 572\n", - "av a: 609 177\n", - "va .: 43 999\n", - ".i s: 599 236\n", - "is a: 136 177\n", - "sa b: 514 182\n", - "ab e: 493 135\n", - "be l: 454 285\n", - "el l: 597 285\n", - "ll a: 414 177\n", - "la .: 416 999\n", - ".s o: 271 384\n", - "so p: 1 537\n", - "op h: 120 259\n", - "ph i: 435 492\n", - "hi a: 424 177\n", - "ia .: 95 999\n", - ".c h: 401 259\n", - "ch a: 146 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512\n", + "ri e: 413 348\n", + "ie .: 376 217\n", + ".z y: 431 224\n", + "zy r: 225 596\n", + "yr o: 445 325\n", + "ro n: 18 613\n", + "on .: 256 217\n", + ".z z: 431 565\n", + "zz y: 110 224\n", + "zy z: 225 565\n", + "yz x: 155 405\n", + "zx .: 0 217\n", + "tensor([415, 263, 343, ..., 225, 155, 0])\n", + "tensor([ 15, 15, 91, ..., 565, 405, 217])\n" ] } ], @@ -198407,410 +198407,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 0%| | 0/1000 [00:00= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [10,0,0], thread: [11,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [10,0,0], thread: [16,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [10,0,0], 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failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [125,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [33,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [40,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [47,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [52,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [57,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [62,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && \"index out of bounds\"` failed.\n", - "Epoch 0: 0%| | 0/1000 [00:01\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[39m# Backward pass\u001b[39;00m\n\u001b[1;32m 32\u001b[0m W\u001b[39m.\u001b[39mgrad \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m# set gradient to zero\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m loss\u001b[39m.\u001b[39;49mbackward()\n\u001b[1;32m 35\u001b[0m \u001b[39m# update\u001b[39;00m\n\u001b[1;32m 36\u001b[0m W\u001b[39m.\u001b[39mdata \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m-\u001b[39mlr \u001b[39m*\u001b[39m W\u001b[39m.\u001b[39mgrad \u001b[39m# gradient descent\u001b[39;00m\n", - "File \u001b[0;32m~/venvs/deeply/lib/python3.10/site-packages/torch/_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 389\u001b[0m Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[1;32m 390\u001b[0m (\u001b[39mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 394\u001b[0m create_graph\u001b[39m=\u001b[39mcreate_graph,\n\u001b[1;32m 395\u001b[0m inputs\u001b[39m=\u001b[39minputs)\n\u001b[0;32m--> 396\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs)\n", - "File \u001b[0;32m~/venvs/deeply/lib/python3.10/site-packages/torch/autograd/__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 168\u001b[0m retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[1;32m 170\u001b[0m \u001b[39m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward( \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 174\u001b[0m tensors, grad_tensors_, retain_graph, create_graph, inputs,\n\u001b[1;32m 175\u001b[0m allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n", - "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1." + "Epoch 999: 100%|██████████| 1000/1000 [14:30<00:00, 1.15Epoch/s, loss=2.31, time=873]\n" ] } ], "source": [ "# gradient descent in a loop -> training\n", "\n", - "lr = 75\n", + "lr = 150\n", "alpha = 0.01\n", "\n", "t_start = time.time()\n", @@ -198820,7 +198424,7 @@ "xs.to(device)\n", "ys.to(device)\n", "\n", - "with tqdm(range(1000), unit=\"Epoch\") as tepoch:\n", + "with tqdm(range(500), unit=\"Epoch\") as tepoch:\n", " for epoch in tepoch:\n", " tepoch.set_description(f\"Epoch {epoch}\")\n", " \n", @@ -198850,9 +198454,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xlekeavlcg.kabtudirb.kecttzzwgy.\n", + "\n", + "ucaanijizbmdwmneerxdcujykshcdbssriwwdvidrenthujgwil.\n", + "\n", + "vhwnetvdldqanvibayeb.\n", + "\n", + "spwbrotwovonkylckigzrqiyuwaystmkxbgvlmaeanoelezemnuuebeux.\n", + "\n", + "ifezdhwwuymluwrsthwki.\n", + "\n" + ] + } + ], "source": [ "# finally, sample from the 'neural net' model\n", "g = torch.Generator().manual_seed(2147483647)\n", @@ -198876,7 +198497,7 @@ " \n", " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", " out.append(itoel[ix])\n", - " if ix == 999:\n", + " if ix == break_int:\n", " break\n", " print(''.join(out))\n", " print()\n" diff --git a/my_code/makemore/1_bigram_torch.ipynb b/my_code/makemore/1_makemore_bigram_torch.ipynb similarity index 100% rename from my_code/makemore/1_bigram_torch.ipynb rename to my_code/makemore/1_makemore_bigram_torch.ipynb diff --git a/my_code/makemore/2_makemore_mlp.ipynb b/my_code/makemore/2_makemore_mlp.ipynb new file mode 100644 index 00000000..a6100842 --- /dev/null +++ b/my_code/makemore/2_makemore_mlp.ipynb @@ -0,0 +1,19 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 8a7bfbd98b2bd87427998e3d4a6f97d5e0aecab4 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Fri, 7 Oct 2022 16:14:26 +0200 Subject: [PATCH 15/20] makemore2 working --- my_code/makemore/2_makemore_mlp.ipynb | 238 +++++++++++++++++++++++++- 1 file changed, 234 insertions(+), 4 deletions(-) diff --git a/my_code/makemore/2_makemore_mlp.ipynb b/my_code/makemore/2_makemore_mlp.ipynb index a6100842..952bd909 100644 --- a/my_code/makemore/2_makemore_mlp.ipynb +++ b/my_code/makemore/2_makemore_mlp.ipynb @@ -2,17 +2,247 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import torch\n", + "import torch.nn.functional\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()\n", + "\n", + "words[:8]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n", + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))\n", + "\n", + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stoi: {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n", + "Itos: {1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], + "source": [ + "# making vocabulary mappings\n", + "chars = sorted(list(set(\"\".join(words))))\n", + "stoi = {c: i+1 for i, c in enumerate(chars)}\n", + "stoi[\".\"] = 0\n", + "itos = {i: c for c, i in stoi.items()}\n", + "print(\"Stoi: \", stoi)\n", + "print(\"Itos: \", itos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### build the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "block_size = 3 # context length: how many characters do we take to predict the next one?\n", + "X, Y = [], []\n", + "for w in words[:5]:\n", + " \n", + " #print(w)\n", + " context = [0] * block_size\n", + " for ch in w + '.':\n", + " ix = stoi[ch]\n", + " X.append(context)\n", + " Y.append(ix)\n", + " #print(''.join(itos[i] for i in context), '--->', itos[ix])\n", + " context = context[1:] + [ix] # crop and append\n", + " \n", + "X = torch.tensor(X)\n", + "Y = torch.tensor(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([32, 3]), torch.Size([32]), torch.int64, torch.int64)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape, Y.shape, X.dtype, Y.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# In the paper it has 3 words as inputs\n", + "# And 17000 words with 30 dimensions\n", + "# We only have 27 chars here, let's start with small dimensions for look-up matrix C" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([27, 2])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C = torch.randn(27, 2)\n", + "C.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.9258, 0.9268])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let' embed a single character first\n", + "C[5]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([32, 3, 2])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's embed all\n", + "emb = C[X]\n", + "emb.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's make the hidden layer\n", + "W1 = torch.randn((6, 100)) # 3*2 inputs and 100 hidden units\n", + "b1 = torch.randn(100)" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 ('deeply')", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } }, "nbformat": 4, "nbformat_minor": 2 From 357bcdd02e3bb0668bb2df8749bc104a2832c5a3 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 8 Oct 2022 11:09:26 +0200 Subject: [PATCH 16/20] stat bug in makemore2 vid code --- lectures/makemore/loss_with_correct_step.png | Bin 0 -> 20785 bytes lectures/makemore/loss_with_wrong_step.png | Bin 0 -> 20705 bytes lectures/makemore/makemore_part2_mlp.ipynb | 10 ++++++---- 3 files changed, 6 insertions(+), 4 deletions(-) create mode 100644 lectures/makemore/loss_with_correct_step.png create mode 100644 lectures/makemore/loss_with_wrong_step.png diff --git a/lectures/makemore/loss_with_correct_step.png b/lectures/makemore/loss_with_correct_step.png new file mode 100644 index 0000000000000000000000000000000000000000..5ce63edc2c36249d17f9f6a21363ee0d9aa60b71 GIT binary patch literal 20785 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zS5&0*);JqJPz@z9jerCAQ}Vr|2Zg1jW^qlnRYSpnhDUyb`|0EaUl^KkRyeVnY_#Q$~3oOI*Gj@{FnzA-M`?z}uZUJhXBdC}!5-zz$s&EqpLfuZZFpBiQH@{m=$apiPa3O9VaS7RU_`{?_h6D2Qe*rvxn_d6_ literal 0 HcmV?d00001 diff --git a/lectures/makemore/makemore_part2_mlp.ipynb b/lectures/makemore/makemore_part2_mlp.ipynb index 75d6dc47..3a090370 100644 --- a/lectures/makemore/makemore_part2_mlp.ipynb +++ b/lectures/makemore/makemore_part2_mlp.ipynb @@ -341,7 +341,8 @@ "source": [ "lri = []\n", "lossi = []\n", - "stepi = []" + "stepi = []\n", + "step = 0" ] }, { @@ -375,7 +376,8 @@ "\n", " # track stats\n", " #lri.append(lre[i])\n", - " stepi.append(i)\n", + " step += 1\n", + " stepi.append(step)\n", " lossi.append(loss.log10().item())\n", "\n", "#print(loss.item())" @@ -398,7 +400,7 @@ }, { "data": { - "image/png": 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Ea0v++d+W+h+HJq5I/r16J12a1wu7LnAbgY/PuO+zUmVnrg6ejeLaf2Qy19uH4Gtmyso9xIzZG2hat0bEeN5Yto1JA9v5E+34Jxb712U8MBdrYdbaXbx2zSD/8rMemhdxe9E89XmW/3G4c9yR44UMfXg+T1x2Bj+eKOJXr62ga4t6TL+gF33bNWLT7kPUTEnmqc838eRlZ5CSrPpPdXX3R9+Sd+g4z/7PgESHUqGU4KuIr7JLj88/Xlhcrm09FofpGRZsDG6q8SV3gPdW5PDDwWM88umGmNu5/YO11EpNYu763dw2roe/iQbAd9ESrZb8zBdZPPLpBjbeP47CIsu+owW0blQ76j7zDnmuDAAWbsxj75ECmtStQVbuYfYdPcFv31nl3+fG3Yf5xQvLOK9bGl9syKN1w1rsPHCM34w8TPeWDWK+v3gqKCzm7cztXD7wNJKSKvae64LCYpKTDMkVvJ+q6tWlp8ZcVK5M8B2a1WVL/pFEh1FtXfpc9Dbn30XpTD52ooiNIfcb/PYdT/mP14YfFrp+10Hu+Sj8zBZ/+sRzEunibe4CuGN8D87s0CRiDE9/nsWiTfn+5/29Vyi+K41wJxRf53rgCSiecg8d48DRE3RpUT9imac+z+LJeZuonZrMJQPaVkgcPl3vnE1G+8a8++uzy72NomJLkjn5ZrBosnIPk960TpW9mso9eIxV2/czplfLRIcSVtU8apJQX28t/41Q3e/6hJ8+9WXsgiH+UYYa1QMfr+f/PV2yjw0/HGJxQEI/XljkH74aaI+D0UDl7ZstLrY8NX8T+4+G38fZD80vdR9GKF9fhNPhptFkbt3LxKe/jDqKKPN7Z59zVu7hUv0kxwuL6HT7x9z41qqwv7tQHtZa3li2zb+9rflHGPXYAh6ZE/tKMVF+9relTHlthf8emarGlQn+1LyolEjO/8tC/ufFZf7nby4Pfx+DE76TQGFR8B/srgOe+wICh48GWpyVz5/nbOSOD8NfiRSGJIAZs7+j252z/Qn4qpeX+6ewPnTsBFm5h8qd6A8fL+SS55ayevv+Ule6B4+dYGqM+wt8svMOk3/4OKMeW1Dq5OS7wWzm6p10v+sTrnnla77dcYDBD83jrgjHIJZPvv2B2z9Y629izPPeK7EiRoXj9g/WMuLRL/zPcw8e49xHPuf7PWW7yn9p8RbSp83ihIOruPRps7j2H5ls3eMZdFBVc5IrE3yVPZpSbdwzc13Qc9/IpUl//4r8w8fJ2Xc0aORTgbe/xJf4PvxmR9R5ip5bsJnjhcV0u/MTcg8dC7r/4s9zNjLqsYX08k6lEUlRseV4YRGvLd3K1oBE3jvgdaFXJK9/9T3/CbhDONpw0hGPLmDIDM/7Dr0xzYT8Ec77Lpef/HUxuw4c47Wvvqe42HLff/4btik1O+9wqWY8KLmzfI+3Cc3J1dSH3+zgjWXbyM474h96O3P1Tr7fc5R/LClbO7vvxBJ6RbL/aAEj/vwFv3xxGenTZvHFBs8vkgb2S72+zPOeo8k7dDxmmXhzZYJXfpeKtuL7ffzh3fB9DRn3z2Xow58HdTIvzvI0EfmGkd709irGP+m5r6EwpEYY+pvBvjugI7HWsm7nAfIPH2fDD4c4eOwEW/KP8H//XEG3Oz/hro/WcUmEfhNfkjx2IvxPWE5+5WuWbM6PWNsN7ej/aNUOz30YMf4Is/MP8+LiLf6puqfPXEfXOz39KCMeXcCYxxfy5LxNQU0bkTbpa+IPN2XGTW+v8j8OvIoDKCz2xL7vSAF3ffgtxwuL+POnG4Leq7XWf6XkO9ltzT9K7sFjjHj0C7bvPcr873LJzj/i79d5PcyPBd390Tr+s9Zz4rzk2SX+E+Oy7D18lb2HXQd+5MwH5vLk/E2lXluRXNnJOrxbczbnbUl0GFLNvZMZPfE+88Vm/jC2O1AyW+nG3YdLlVsd8AM04Ya7Pj0/q9Qyn+Jiy98WZvPwJyV3UXdKq8vmvOCE7Os4Dk3iy7fsoWfrBnS/6xMa1Eph8pAOQeu/2JDnv3rwDX8tLrY8/XnpmF5YlM39s9bTvmkdZk6NfhfyzNWeZGeB0++dw/4wk+s99tlG9h4pYNX2/fz6vE584R25Zb2DXD/03ij39dZ9LN28h0l//4oXrsigcd1U+p/WuFTnbnbeEdbvOkjOPk9N/tWl33PvxN5c9OwStuQf4ZN1P5B36Dizvy25gun3xzkcPFbIq1cP5Ij36uuCpxbzs4y2ZOcdYdifPufeib2C9hN67H2Oek8Uvr6NfUcK/PNWvXzVmQD8Ze4m/jJ3E9kPjq/wkVIAJl53/JVVRkaGzczMjF0wjMc+28iT8yr3TCgSSY9WDdiSf5hjJzw1xg+vH+LvBJ5947CYk8/Fy9YZE8i4f26p5pStMyY4uo9i64wJTHk1M+j3EyJZM30MfafPiVmuTaPa7Ai4a7lPm4as3XEgyiucuXRAWx65tF/Y9zWhTytmrY0+UV1FWTt9DH3CHJfOzeuRlVty8t/0wDhSyzkyyBizwlqb4aSsK2vwaqKRqiRwigYgaIRPZSV38Ny7EG4SN6ftvmc/NI+dDu8i/rODeyCAoOQOxCW5g2cqj10RYo3XqJ7yCJfcgaDkXplc2QafVIHjbkXc6sqXwk8z0fH2j8MuD+U0uUPVuFHI1+8Rat53uZUcSdk5+c2IeHBpgk90BCIi5TfwwfJNx1FWrkzwF1fwXX4iItWBKxN8rLlIRETEYYI3xow1xmwwxmQZY6ZFKXemMabIGHNJ/EIUEZHyiJngjTHJwNPAOKAnMMkY0zNCuYeB6LffiYhIpXBSgx8IZFlrs621BcBbwMQw5W4A3gOqfhe2iMgpwEmCbwMEzt6U413mZ4xpA1wIPBdtQ8aYKcaYTGNMZl5e+X8aTkREYnOS4MMNSgy9c+IvwK3W2qh3GFhrn7fWZlhrM9LS0hyGKCIi5eHkTtYcoF3A87bAzpAyGcBb3rkhmgHjjTGF1toP4xGkiIiUnZME/zXQxRjTAdgBXAb8IrCAtdY/g5Ex5hXgP0ruIiKJFbOJxlpbCEzFMzpmPfCOtXadMeY6Y8x1FR1gJNcM7RC7kIjIKczRZGPW2o+Bj0OWhe1QtdZedfJhxdanTcPK2I2IiGu58k5WKJkzWkREwnNvgld+FxGJSgleRKSacm+CT3QAIiJVnHsTvKrwIiJRuTbBi4hIdK5N8D1aNUh0CCIiVZprE3zvNg1ZM31MosMQEamyXJvgARrUSk10CCIiVZarE7yIiESmBC8iUk0pwYuIVFOuT/Bzbj7H//i6czslMBIRkarF9Qm+a4v6/sc3jeqSwEhERKoW1yf4QLVSkxMdgohIlVGtEjzA1hkTGNC+caLDEBFJuGqX4EVExOOUTPC9WmuaAxGp/qplgo81mmbWb4ax5aHxlRSNiEhiVMsEP7pnC765a3TQsn9dN7jM27licPug5y0a1IxavlNa3TLvQ0SkolTLBA/QuG4Nts6Y4H9+ZnqTiGVH92zBa9cM5IP/OztoeaPaqbx+zSAA2jWpzctXDWR8n5b+9ZcPOo1/XD3Q/3ze786LGpPvBNGmUW3H7yOclSEnL7d4/ZpB/OrcjokOQ+SU4SjBG2PGGmM2GGOyjDHTwqy/3BizxvtviTGmX/xDje7sTk1jlnnm8v5cGVIrB/j7FRkM65LGGaeVHn3TroknGRsMPVs34JnLB/jXPXBhH87tmhZ1n2/+71n+x/+5YRj3TuzF578/z79sSOemUbfxmxGdSy2rkeLsvFyvZkqpZYEnvZMx8fTWZX7N0C7NSEkycdm/iMQWM1MYY5KBp4FxQE9gkjGmZ0ixLcC51tq+wH3A8/EONJrND47317TD8Q2bHN+nFX+c2Btw+JuuxpT5t1+vHtKB4d1KEvbggBNPWv2aXDE4PShB105NCboK6NjM08zz8MV92DpjAr8d0y3sfpbdPpKlt42IGsvDF/clNdlQIzn4Y75tXPeIr/ks4M7gUJMGnuZ//MRlZ0TddyRXDE4v1+tEpOycVAUHAlnW2mxrbQHwFjAxsIC1dom1dp/36VdA2/iGGV1ykiEpQs3wy2kjeO2agWHXARiHFUqn5e6+oCcvT468v9KCzyDzf38eW2dM4OdnnlaqZOfm9QCokZxEiwa1aNWwpKnnvG7hrwLWTj+/1Lz5k4d08D/u06ZhqX089rN+/L+QGvrkIencNj74xDCkc+mrpuW3j/Q/viHM1UeLBrXCxulzzdAOEdeFu5qJ5d1y9L2IVBelr+FLawNsD3ieA0SuLsM1wOxwK4wxU4ApAKedVjqBVYRY7d1ntGsUdnmzejW5dEBbiopP/rdf59x8Dtl5R8Ku810hDO3cjDU5+6Nu57Obz6Gw2JIaUCNfMm0ERcWW6TPXhX1NuLt7a6Qk8ezl/Vm/6yBfZe8tFc9F/duyJb8k3tX3jKFh7dJz7/uagJ6cdAZFxcW0b1qX5gEJ3HdCmtCnFY/+zFmr3RWD23PXT3ry1PxN/HnORgDSm9Zh656jTOjbmifnZwHwv8M68PdFWyJuZ830MTSolcrxwiJH+xWpjpzU4MPVXcNmPWPMcDwJ/tZw6621z1trM6y1GWlp0duuK1pSkmHm1CG8cnXp2rYxkHnnKNo1qUOjOp7ENrpHi6AyGWW4W7Zri/qM7d0yaNmSaSPolFaX+y/0NBm9fu0g1kw/P+zrbx7V1RuXCUruAK0b1aZdkzr0DxOPDf8xATCuTyt+O6YbY3p53lf7pnV4ZfKZ/iuhywe1p1frBiy/fWTY5B6oRrLhwjPa0j+kD8N38kpOMkEnmkhXGwD1vT/iMnVEybxCxnv5lJJc8lW8Y0JJK+GKO0ex8JbhdEqrS01v81dqUtnHD1x4RhseuqhPmV9XWSb0bUX2g+N57ZqB/u+EhBfu6rIqmTl1SKXsx0kNPgdoF/C8LbAztJAxpi/wAjDOWrsnPuFVrL5tG5VaNm1c96BOz0Z1avD1HaNoUreGf9ma6WP8iSSShy7qQ9cW9SKub92odsxRNz43jurCjTEmUvv1uZ0Y17slIx5d4GibPo3qeN7XgPaNOa9bc//ylg1rMes3w0qV/93orpzZIfKIJIArB7dnTK+WHC3w1J4Djx1AUpT2rtCyUFLDiNQf0rReTZrW84xiOlpQyNb8o9Su4Tmh1ExJ5saRXRjbuyWFRZaPVu3g3ZU57D96Imgb9Wqm8Pvzu9GmUW1ue39txPiGdm7G4qz8iOsr0s2jupKUZBjWJY1hXdJ4fO7GMr3+rI5NePznp/PCoi28uDjy1Q94KjmRjvfF/dvy3soc+rRpyDOX9+eDb3bw2GdliyWSlCRDYZSr5rT6Nck7dDxo2YS+rZi1ZlfQslvO786XWV/GJaZ4+eNPe3HPzHXUTk0Om3sqgpNqztdAF2NMB2NMDeAyYGZgAWPMacD7wC+ttfH5pBPkunM7lfpB77T6NUkOaONvUCuVmiklNdJFfxjOoj8MD3rNpIGnMaB99EQYT0lJho5pwScUJx3EI7o3p3XDWvzqHGdTLd8wsgtndWzqfy1A5+b1g8r8cWJvhnRuxqgezXngwt7cOja47T7cSJplt48sNcLn6iEdmDq8c8A1ZPAb+nlGO+6b2CtoWZ0aKfQMuVP55tFd6dGqAX3aNuTOn/QMmmLa59s/nu9vzrv7J6FjCEq8fm1w62Rqcun38vJVZ0Z8fVlMHd6ZmVOHMOfmc9g6Y4K/ySvUa9cMZGjnZjG3d/mg9rRqWDvoSijUglvO47v7xvLilRn+Zf+8dhAT+rbyP/eNLBveLY12Terwm5EllY+/TjqDxbcG/y2UxR0TekRcd9/EXmG/0x2alr7/JNyyQMO6RD5esZp1f3lW6ZF4sXw5bQSXZlRq1yTgIMFbawuBqcCnwHrgHWvtOmPMdcaY67zF7gaaAs8YY1YZYzIrLOIqqF2TOrRrUifRYZRS28Hsmk3q1mDJbSPp1rJ+zLKhfpbRjrXTx0RMPMYYLh/U3l+b9rn/wt4M69KMRy8taZcP1/l69wU9+f353YJq8E9OOoMXrvAkn4cv6csvyzEqp3n9WtwxPnIiuXpoB/8+YhnUoSlbZ0xg0kDPRe643i0Z3r151JFK0cz6zVB+Meg0sh8cz+/P70bfto2CpsQOFDg66m+/HMAjl/QtVeafASekC/p5O86jnPjbN61LrdRkzutacjU3pHMz7rnAc9KbPCQdE7bV1qN+rRTaNi75W/jdaE9TktNhtaN7tgi7fOuMCd7P2hP8GwHvK9w04Q3rpDL/d+c63s87vyrpjF/0h+FRpx5vHXACWH3PGOp4v9/hBghsnTGBrTMm0KZRbWqlJNO7TQMe//npEbcdb06aaLDWfgx8HLLsuYDH1wLXxjc0KY/kJENRseWeC3oyskfJH+nr1wxic97huO7LGONvMy+L5vVr8Zp3WOvv/rXa0X58ftqv7OPvo2nbuDavhumHGRUh0QBsemAcxdYyfeZ/meod2TOiewveXL7d//xX53aiWb2ajt7fjSO7kHvoGH3aNKJX64Y8eKGzfoBBHZuwaFM+1kLdmilcmtGOdzK38/XWfQzr0oxFm/I5PcIggkCXDGhL03o1yNn7o39Z6Ki05vVrsfH+caQmG56c5+noDjxPnNM1jYUb80pt2/fRtW0cXCu+fngnnv58M+D5bq7cto8L+rWOOcoqvWld8g8X0Kt1Q3q3acCJQktKcvh6auAV7aSBp/Hm8m3+Jp7QK4GBAc2OSUmGm0Z1pXZqMg/N/i7iewJoWDuVBbcMZ/fBY3zwzQ7/8iXTRgSdCHzb/c8NpZs9K5KjBC/usfqeMRQV21Ido0O7NGNolMtSNzj58UwlfPcn/OmSvqWatsIZ1qWZv2/G19Ed2CE7umcLNj0wLqgT3JesBnVowrItwaOVAo3s0fyk2mSLw7RbTB3emX9MHkhSkuGTm4aRHJCVAq8IVt41Omy/Rzi++zfCdaF0SqvLwo15Qdvq17YhFw9oy79W5DBp4Gk0rlOD+2etp2Ozutxyfnd/gg/9bk4b150ZYRIrwAtXZvDNtv00rJMalCxfvDKDujVT2LbnKF3C9H09eGFv7p3YCwMUWcsby7aVKvPR9UNYsrmk+/BX53ZiVM8WjIzRr5VWvyZp9WuyNOC1To9pRVOCr2bC3b1alQ3vlhZUQw8nVidrefRu07BMd/XePr5Hqb6ZUKEjnHyjmJLD9DlMOacjzy/M9pQr5/sa36cVizbl07FZSUILbD7x1cK7twyO+6L+bWhWvyb7jhRETUS3jevOgR9PRFwfGPdt43owqkcL/4lq+e0jqV8rldo1kllwy3B/vPfPWh+xycnnunM7RUzwjerUYHj35qWWj/SOcvP1D4XyjEDzjsYCJp7ehndX5LBu50F/mX7tGtEv5IqnU8DJ33eVYoAnLjudXq2D7yG5emgHWjeqzfg+LWN+pyuLu7KBVDtObgrz/a1EG/ZZUVo0qMnbUwaT3qzsE8n5EmDgqKFOaXXZnHeEn2W05avsPazJOVDu2C47sx0XntEmaAjqTaO78KtXV9AjypTYxpiYU2yApwYb9vVhltVISWJIQEdv8zBNLa0b1eaNaweVSqLhvPfrs5m7fjd1UpPpEuOEUB5N6tZg1m+GkT5tluPXdGtRz98MNfH0NqXWJyeZoM7oqkAJXqo8X600njV4Jz696RxaNqhFwzpl72cIFFiZ69e2EZvzjlCvZirndEljTc4B0upHn6U08nZNqRvZzu7UjLV/DH8/Rbykeptqoo3GieRsB6N9wDNst6r+MlsVqZw7ogQvVd7gTk3ZsPuQ/6azylKekUWBfE0Rlwxoy6JNnrHzD17Uh6uGpNOyYS1uHt2V/zmrPS0bRu9YrGquOjudfUcKHA+tjeSRS/rGHI9/sjo0q8v/Dju5GUynnNORvm0bsmrb/vgEVYmU4KXKu2NCD64Y3D5o7h03aNmwlr+d/8a3VgGeqSN87dTJScZ1yR087+G2KMNMnbo0ox2XZrSLXfAkBM7cWl63e9/rWm9zWrQb9aoaJXip8lKTkxyNdBGpSNeP6Mzh44VcPqjsNzolihK8iJyy6tZI5ubRzub1aVArlQcc3qNQVSjBi1SScNMaSGKtu3dsokOoUErwIpXgzgk9GNYlsTOoyqlHCV6kElx7kiM5RMqj2v7otojIqU4JXkSkmlKCFxGpppTgRUSqKSV4EZFqSgleRKSaUoIXEammlOBFRKopYyt7km3fjo3JA74v58ubAflxDCdeqmpcUHVjU1xlo7jKpjrG1d5a6+i26IQl+JNhjMm01jr72ftKVFXjgqobm+IqG8VVNqd6XGqiERGpppTgRUSqKbcm+OcTHUAEVTUuqLqxKa6yUVxlc0rH5co2eBERic2tNXgREYlBCV5EpLqy1rrqHzAW2ABkAdMqYPvtgM+B9cA64Ebv8unADmCV99/4gNfc5o1nA3B+wPIBwFrvuicpaRKrCbztXb4MSHcY21bv9lYBmd5lTYDPgE3e/xtXZlxAt4Bjsgo4CNyUqOMFvATkAt8GLKuUYwRc6d3HJuBKB3E9AnwHrAE+ABp5l6cDPwYcu+cqOa5K+ezKEdfbATFtBVZV5vEicm5I+Pcr4t9DPJNjRf8DkoHNQEegBrAa6BnnfbQC+nsf1wc2Aj29X/rfhynf0xtHTaCDN75k77rlwGDAALOBcd7l/+f7EgKXAW87jG0r0Cxk2Z/wnuiAacDDlR1XyOfzA9A+UccLOAfoT3BiqPBjhOePPNv7f2Pv48Yx4hoDpHgfPxwQV3pguZD3VxlxVfhnV564QmJ5FLi7Mo8XkXNDwr9fEf8eypMEE/XPe0A+DXh+G3BbBe/zI2B0lC99UAzAp944WwHfBSyfBPwtsIz3cQqeO9qMg1i2UjrBbwBaBXwBN1R2XAHbGgN86X2csONFyB98ZRyjwDLedX8DJkWLK2TdhcA/o5WrrLgq47M7mePlff12oEsijleY3FAlvl/h/rmtDb4Nng/WJ8e7rEIYY9KBM/BcKgFMNcasMca8ZIxpHCOmNt7H4WL1v8ZaWwgcAJo6CMkCc4wxK4wxU7zLWlhrd3m3tQtonoC4fC4D3gx4nujj5VMZx+hkv5tX46nJ+XQwxnxjjFlgjBkWsO/KiquiP7uTOV7DgN3W2k0Byyr1eIXkhir7/XJbgjdhltkK2ZEx9YD3gJustQeBZ4FOwOnALjyXiNFiihZred/HEGttf2AccL0x5pwoZSszLowxNYCfAv/yLqoKxyuWeMZyMsfuDqAQ+Kd30S7gNGvtGcBvgTeMMQ0qMa7K+OxO5jOdRHBFolKPV5jcEEnCj5fbEnwOno4On7bAznjvxBiTiucD/Ke19n0Aa+1ua22RtbYY+DswMEZMOd7H4WL1v8YYkwI0BPbGistau9P7fy6eTrmBwG5jTCvvtlrh6Ziq1Li8xgErrbW7vTEm/HgFqIxjVK7vpjHmSuAnwOXWe+1trT1urd3jfbwCT9tt18qKq5I+u/IerxTgIjwdkb54K+14hcsNVOHvV4W1XVfEPzxtUtl4Oix8nay94rwPA7wK/CVkeauAxzcDb3kf9yK4IyWbko6Ur4GzKOlIGe9dfj3BHSnvOIirLlA/4PESPCOKHiG4g+dPlRlXQHxvAZOrwvGidJtyhR8jPJ1fW/B0gDX2Pm4SI66xwH+BtJByaQFxdMQzoqVJJcZV4Z9deeIKOGYLEnG8iJwbqsT3K+zfwskkw0T8A8bj6b3eDNxRAdsfiufSZw0Bw8SA1/AMa1oDzAz5I7jDG88GvL3h3uUZwLfedU9RMhSqFp6mjCw8vekdHcTV0ftlWY1niNYd3uVNgXl4hk7NC/zQKyMu7+vqAHuAhgHLEnK88Fy67wJO4Kn1XFNZxwhPO3qW999kB3Fl4WlX9X3PfH/YF3s/49XASuCCSo6rUj67ssblXf4KcF1I2Uo5XkTODQn/fkX6p6kKRESqKbe1wYuIiENK8CIi1ZQSvIhINaUELyJSTSnBi4h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Ea0v++d+W+h+HJq5I/r16J12a1wu7LnAbgY/PuO+zUmVnrg6ejeLaf2Qy19uH4Gtmyso9xIzZG2hat0bEeN5Yto1JA9v5E+34Jxb712U8MBdrYdbaXbx2zSD/8rMemhdxe9E89XmW/3G4c9yR44UMfXg+T1x2Bj+eKOJXr62ga4t6TL+gF33bNWLT7kPUTEnmqc838eRlZ5CSrPpPdXX3R9+Sd+g4z/7PgESHUqGU4KuIr7JLj88/Xlhcrm09FofpGRZsDG6q8SV3gPdW5PDDwWM88umGmNu5/YO11EpNYu763dw2roe/iQbAd9ESrZb8zBdZPPLpBjbeP47CIsu+owW0blQ76j7zDnmuDAAWbsxj75ECmtStQVbuYfYdPcFv31nl3+fG3Yf5xQvLOK9bGl9syKN1w1rsPHCM34w8TPeWDWK+v3gqKCzm7cztXD7wNJKSKvae64LCYpKTDMkVvJ+q6tWlp8ZcVK5M8B2a1WVL/pFEh1FtXfpc9Dbn30XpTD52ooiNIfcb/PYdT/mP14YfFrp+10Hu+Sj8zBZ/+sRzEunibe4CuGN8D87s0CRiDE9/nsWiTfn+5/29Vyi+K41wJxRf53rgCSiecg8d48DRE3RpUT9imac+z+LJeZuonZrMJQPaVkgcPl3vnE1G+8a8++uzy72NomJLkjn5ZrBosnIPk960TpW9mso9eIxV2/czplfLRIcSVtU8apJQX28t/41Q3e/6hJ8+9WXsgiH+UYYa1QMfr+f/PV2yjw0/HGJxQEI/XljkH74aaI+D0UDl7ZstLrY8NX8T+4+G38fZD80vdR9GKF9fhNPhptFkbt3LxKe/jDqKKPN7Z59zVu7hUv0kxwuL6HT7x9z41qqwv7tQHtZa3li2zb+9rflHGPXYAh6ZE/tKMVF+9relTHlthf8emarGlQn+1LyolEjO/8tC/ufFZf7nby4Pfx+DE76TQGFR8B/srgOe+wICh48GWpyVz5/nbOSOD8NfiRSGJIAZs7+j252z/Qn4qpeX+6ewPnTsBFm5h8qd6A8fL+SS55ayevv+Ule6B4+dYGqM+wt8svMOk3/4OKMeW1Dq5OS7wWzm6p10v+sTrnnla77dcYDBD83jrgjHIJZPvv2B2z9Y629izPPeK7EiRoXj9g/WMuLRL/zPcw8e49xHPuf7PWW7yn9p8RbSp83ihIOruPRps7j2H5ls3eMZdFBVc5IrE3yVPZpSbdwzc13Qc9/IpUl//4r8w8fJ2Xc0aORTgbe/xJf4PvxmR9R5ip5bsJnjhcV0u/MTcg8dC7r/4s9zNjLqsYX08k6lEUlRseV4YRGvLd3K1oBE3jvgdaFXJK9/9T3/CbhDONpw0hGPLmDIDM/7Dr0xzYT8Ec77Lpef/HUxuw4c47Wvvqe42HLff/4btik1O+9wqWY8KLmzfI+3Cc3J1dSH3+zgjWXbyM474h96O3P1Tr7fc5R/LClbO7vvxBJ6RbL/aAEj/vwFv3xxGenTZvHFBs8vkgb2S72+zPOeo8k7dDxmmXhzZYJXfpeKtuL7ffzh3fB9DRn3z2Xow58HdTIvzvI0EfmGkd709irGP+m5r6EwpEYY+pvBvjugI7HWsm7nAfIPH2fDD4c4eOwEW/KP8H//XEG3Oz/hro/WcUmEfhNfkjx2IvxPWE5+5WuWbM6PWNsN7ej/aNUOz30YMf4Is/MP8+LiLf6puqfPXEfXOz39KCMeXcCYxxfy5LxNQU0bkTbpa+IPN2XGTW+v8j8OvIoDKCz2xL7vSAF3ffgtxwuL+POnG4Leq7XWf6XkO9ltzT9K7sFjjHj0C7bvPcr873LJzj/i79d5PcyPBd390Tr+s9Zz4rzk2SX+E+Oy7D18lb2HXQd+5MwH5vLk/E2lXluRXNnJOrxbczbnbUl0GFLNvZMZPfE+88Vm/jC2O1AyW+nG3YdLlVsd8AM04Ya7Pj0/q9Qyn+Jiy98WZvPwJyV3UXdKq8vmvOCE7Os4Dk3iy7fsoWfrBnS/6xMa1Eph8pAOQeu/2JDnv3rwDX8tLrY8/XnpmF5YlM39s9bTvmkdZk6NfhfyzNWeZGeB0++dw/4wk+s99tlG9h4pYNX2/fz6vE584R25Zb2DXD/03ij39dZ9LN28h0l//4oXrsigcd1U+p/WuFTnbnbeEdbvOkjOPk9N/tWl33PvxN5c9OwStuQf4ZN1P5B36Dizvy25gun3xzkcPFbIq1cP5Ij36uuCpxbzs4y2ZOcdYdifPufeib2C9hN67H2Oek8Uvr6NfUcK/PNWvXzVmQD8Ze4m/jJ3E9kPjq/wkVIAJl53/JVVRkaGzczMjF0wjMc+28iT8yr3TCgSSY9WDdiSf5hjJzw1xg+vH+LvBJ5947CYk8/Fy9YZE8i4f26p5pStMyY4uo9i64wJTHk1M+j3EyJZM30MfafPiVmuTaPa7Ai4a7lPm4as3XEgyiucuXRAWx65tF/Y9zWhTytmrY0+UV1FWTt9DH3CHJfOzeuRlVty8t/0wDhSyzkyyBizwlqb4aSsK2vwaqKRqiRwigYgaIRPZSV38Ny7EG4SN6ftvmc/NI+dDu8i/rODeyCAoOQOxCW5g2cqj10RYo3XqJ7yCJfcgaDkXplc2QafVIHjbkXc6sqXwk8z0fH2j8MuD+U0uUPVuFHI1+8Rat53uZUcSdk5+c2IeHBpgk90BCIi5TfwwfJNx1FWrkzwF1fwXX4iItWBKxN8rLlIRETEYYI3xow1xmwwxmQZY6ZFKXemMabIGHNJ/EIUEZHyiJngjTHJwNPAOKAnMMkY0zNCuYeB6LffiYhIpXBSgx8IZFlrs621BcBbwMQw5W4A3gOqfhe2iMgpwEmCbwMEzt6U413mZ4xpA1wIPBdtQ8aYKcaYTGNMZl5e+X8aTkREYnOS4MMNSgy9c+IvwK3W2qh3GFhrn7fWZlhrM9LS0hyGKCIi5eHkTtYcoF3A87bAzpAyGcBb3rkhmgHjjTGF1toP4xGkiIiUnZME/zXQxRjTAdgBXAb8IrCAtdY/g5Ex5hXgP0ruIiKJFbOJxlpbCEzFMzpmPfCOtXadMeY6Y8x1FR1gJNcM7RC7kIjIKczRZGPW2o+Bj0OWhe1QtdZedfJhxdanTcPK2I2IiGu58k5WKJkzWkREwnNvgld+FxGJSgleRKSacm+CT3QAIiJVnHsTvKrwIiJRuTbBi4hIdK5N8D1aNUh0CCIiVZprE3zvNg1ZM31MosMQEamyXJvgARrUSk10CCIiVZarE7yIiESmBC8iUk0pwYuIVFOuT/Bzbj7H//i6czslMBIRkarF9Qm+a4v6/sc3jeqSwEhERKoW1yf4QLVSkxMdgohIlVGtEjzA1hkTGNC+caLDEBFJuGqX4EVExOOUTPC9WmuaAxGp/qplgo81mmbWb4ax5aHxlRSNiEhiVMsEP7pnC765a3TQsn9dN7jM27licPug5y0a1IxavlNa3TLvQ0SkolTLBA/QuG4Nts6Y4H9+ZnqTiGVH92zBa9cM5IP/OztoeaPaqbx+zSAA2jWpzctXDWR8n5b+9ZcPOo1/XD3Q/3ze786LGpPvBNGmUW3H7yOclSEnL7d4/ZpB/OrcjokOQ+SU4SjBG2PGGmM2GGOyjDHTwqy/3BizxvtviTGmX/xDje7sTk1jlnnm8v5cGVIrB/j7FRkM65LGGaeVHn3TroknGRsMPVs34JnLB/jXPXBhH87tmhZ1n2/+71n+x/+5YRj3TuzF578/z79sSOemUbfxmxGdSy2rkeLsvFyvZkqpZYEnvZMx8fTWZX7N0C7NSEkycdm/iMQWM1MYY5KBp4FxQE9gkjGmZ0ixLcC51tq+wH3A8/EONJrND47317TD8Q2bHN+nFX+c2Btw+JuuxpT5t1+vHtKB4d1KEvbggBNPWv2aXDE4PShB105NCboK6NjM08zz8MV92DpjAr8d0y3sfpbdPpKlt42IGsvDF/clNdlQIzn4Y75tXPeIr/ks4M7gUJMGnuZ//MRlZ0TddyRXDE4v1+tEpOycVAUHAlnW2mxrbQHwFjAxsIC1dom1dp/36VdA2/iGGV1ykiEpQs3wy2kjeO2agWHXARiHFUqn5e6+oCcvT468v9KCzyDzf38eW2dM4OdnnlaqZOfm9QCokZxEiwa1aNWwpKnnvG7hrwLWTj+/1Lz5k4d08D/u06ZhqX089rN+/L+QGvrkIencNj74xDCkc+mrpuW3j/Q/viHM1UeLBrXCxulzzdAOEdeFu5qJ5d1y9L2IVBelr+FLawNsD3ieA0SuLsM1wOxwK4wxU4ApAKedVjqBVYRY7d1ntGsUdnmzejW5dEBbiopP/rdf59x8Dtl5R8Ku810hDO3cjDU5+6Nu57Obz6Gw2JIaUCNfMm0ERcWW6TPXhX1NuLt7a6Qk8ezl/Vm/6yBfZe8tFc9F/duyJb8k3tX3jKFh7dJz7/uagJ6cdAZFxcW0b1qX5gEJ3HdCmtCnFY/+zFmr3RWD23PXT3ry1PxN/HnORgDSm9Zh656jTOjbmifnZwHwv8M68PdFWyJuZ830MTSolcrxwiJH+xWpjpzU4MPVXcNmPWPMcDwJ/tZw6621z1trM6y1GWlp0duuK1pSkmHm1CG8cnXp2rYxkHnnKNo1qUOjOp7ENrpHi6AyGWW4W7Zri/qM7d0yaNmSaSPolFaX+y/0NBm9fu0g1kw/P+zrbx7V1RuXCUruAK0b1aZdkzr0DxOPDf8xATCuTyt+O6YbY3p53lf7pnV4ZfKZ/iuhywe1p1frBiy/fWTY5B6oRrLhwjPa0j+kD8N38kpOMkEnmkhXGwD1vT/iMnVEybxCxnv5lJJc8lW8Y0JJK+GKO0ex8JbhdEqrS01v81dqUtnHD1x4RhseuqhPmV9XWSb0bUX2g+N57ZqB/u+EhBfu6rIqmTl1SKXsx0kNPgdoF/C8LbAztJAxpi/wAjDOWrsnPuFVrL5tG5VaNm1c96BOz0Z1avD1HaNoUreGf9ma6WP8iSSShy7qQ9cW9SKub92odsxRNz43jurCjTEmUvv1uZ0Y17slIx5d4GibPo3qeN7XgPaNOa9bc//ylg1rMes3w0qV/93orpzZIfKIJIArB7dnTK+WHC3w1J4Djx1AUpT2rtCyUFLDiNQf0rReTZrW84xiOlpQyNb8o9Su4Tmh1ExJ5saRXRjbuyWFRZaPVu3g3ZU57D96Imgb9Wqm8Pvzu9GmUW1ue39txPiGdm7G4qz8iOsr0s2jupKUZBjWJY1hXdJ4fO7GMr3+rI5NePznp/PCoi28uDjy1Q94KjmRjvfF/dvy3soc+rRpyDOX9+eDb3bw2GdliyWSlCRDYZSr5rT6Nck7dDxo2YS+rZi1ZlfQslvO786XWV/GJaZ4+eNPe3HPzHXUTk0Om3sqgpNqztdAF2NMB2NMDeAyYGZgAWPMacD7wC+ttfH5pBPkunM7lfpB77T6NUkOaONvUCuVmiklNdJFfxjOoj8MD3rNpIGnMaB99EQYT0lJho5pwScUJx3EI7o3p3XDWvzqHGdTLd8wsgtndWzqfy1A5+b1g8r8cWJvhnRuxqgezXngwt7cOja47T7cSJplt48sNcLn6iEdmDq8c8A1ZPAb+nlGO+6b2CtoWZ0aKfQMuVP55tFd6dGqAX3aNuTOn/QMmmLa59s/nu9vzrv7J6FjCEq8fm1w62Rqcun38vJVZ0Z8fVlMHd6ZmVOHMOfmc9g6Y4K/ySvUa9cMZGjnZjG3d/mg9rRqWDvoSijUglvO47v7xvLilRn+Zf+8dhAT+rbyP/eNLBveLY12Terwm5EllY+/TjqDxbcG/y2UxR0TekRcd9/EXmG/0x2alr7/JNyyQMO6RD5esZp1f3lW6ZF4sXw5bQSXZlRq1yTgIMFbawuBqcCnwHrgHWvtOmPMdcaY67zF7gaaAs8YY1YZYzIrLOIqqF2TOrRrUifRYZRS28Hsmk3q1mDJbSPp1rJ+zLKhfpbRjrXTx0RMPMYYLh/U3l+b9rn/wt4M69KMRy8taZcP1/l69wU9+f353YJq8E9OOoMXrvAkn4cv6csvyzEqp3n9WtwxPnIiuXpoB/8+YhnUoSlbZ0xg0kDPRe643i0Z3r151JFK0cz6zVB+Meg0sh8cz+/P70bfto2CpsQOFDg66m+/HMAjl/QtVeafASekC/p5O86jnPjbN61LrdRkzutacjU3pHMz7rnAc9KbPCQdE7bV1qN+rRTaNi75W/jdaE9TktNhtaN7tgi7fOuMCd7P2hP8GwHvK9w04Q3rpDL/d+c63s87vyrpjF/0h+FRpx5vHXACWH3PGOp4v9/hBghsnTGBrTMm0KZRbWqlJNO7TQMe//npEbcdb06aaLDWfgx8HLLsuYDH1wLXxjc0KY/kJENRseWeC3oyskfJH+nr1wxic97huO7LGONvMy+L5vVr8Zp3WOvv/rXa0X58ftqv7OPvo2nbuDavhumHGRUh0QBsemAcxdYyfeZ/meod2TOiewveXL7d//xX53aiWb2ajt7fjSO7kHvoGH3aNKJX64Y8eKGzfoBBHZuwaFM+1kLdmilcmtGOdzK38/XWfQzr0oxFm/I5PcIggkCXDGhL03o1yNn7o39Z6Ki05vVrsfH+caQmG56c5+noDjxPnNM1jYUb80pt2/fRtW0cXCu+fngnnv58M+D5bq7cto8L+rWOOcoqvWld8g8X0Kt1Q3q3acCJQktKcvh6auAV7aSBp/Hm8m3+Jp7QK4GBAc2OSUmGm0Z1pXZqMg/N/i7iewJoWDuVBbcMZ/fBY3zwzQ7/8iXTRgSdCHzb/c8NpZs9K5KjBC/usfqeMRQV21Ido0O7NGNolMtSNzj58UwlfPcn/OmSvqWatsIZ1qWZv2/G19Ed2CE7umcLNj0wLqgT3JesBnVowrItwaOVAo3s0fyk2mSLw7RbTB3emX9MHkhSkuGTm4aRHJCVAq8IVt41Omy/Rzi++zfCdaF0SqvLwo15Qdvq17YhFw9oy79W5DBp4Gk0rlOD+2etp2Ozutxyfnd/gg/9bk4b150ZYRIrwAtXZvDNtv00rJMalCxfvDKDujVT2LbnKF3C9H09eGFv7p3YCwMUWcsby7aVKvPR9UNYsrmk+/BX53ZiVM8WjIzRr5VWvyZp9WuyNOC1To9pRVOCr2bC3b1alQ3vlhZUQw8nVidrefRu07BMd/XePr5Hqb6ZUKEjnHyjmJLD9DlMOacjzy/M9pQr5/sa36cVizbl07FZSUILbD7x1cK7twyO+6L+bWhWvyb7jhRETUS3jevOgR9PRFwfGPdt43owqkcL/4lq+e0jqV8rldo1kllwy3B/vPfPWh+xycnnunM7RUzwjerUYHj35qWWj/SOcvP1D4XyjEDzjsYCJp7ehndX5LBu50F/mX7tGtEv5IqnU8DJ33eVYoAnLjudXq2D7yG5emgHWjeqzfg+LWN+pyuLu7KBVDtObgrz/a1EG/ZZUVo0qMnbUwaT3qzsE8n5EmDgqKFOaXXZnHeEn2W05avsPazJOVDu2C47sx0XntEmaAjqTaO78KtXV9AjypTYxpiYU2yApwYb9vVhltVISWJIQEdv8zBNLa0b1eaNaweVSqLhvPfrs5m7fjd1UpPpEuOEUB5N6tZg1m+GkT5tluPXdGtRz98MNfH0NqXWJyeZoM7oqkAJXqo8X600njV4Jz696RxaNqhFwzpl72cIFFiZ69e2EZvzjlCvZirndEljTc4B0upHn6U08nZNqRvZzu7UjLV/DH8/Rbykeptqoo3GieRsB6N9wDNst6r+MlsVqZw7ogQvVd7gTk3ZsPuQ/6azylKekUWBfE0Rlwxoy6JNnrHzD17Uh6uGpNOyYS1uHt2V/zmrPS0bRu9YrGquOjudfUcKHA+tjeSRS/rGHI9/sjo0q8v/Dju5GUynnNORvm0bsmrb/vgEVYmU4KXKu2NCD64Y3D5o7h03aNmwlr+d/8a3VgGeqSN87dTJScZ1yR087+G2KMNMnbo0ox2XZrSLXfAkBM7cWl63e9/rWm9zWrQb9aoaJXip8lKTkxyNdBGpSNeP6Mzh44VcPqjsNzolihK8iJyy6tZI5ubRzub1aVArlQcc3qNQVSjBi1SScNMaSGKtu3dsokOoUErwIpXgzgk9GNYlsTOoyqlHCV6kElx7kiM5RMqj2v7otojIqU4JXkSkmlKCFxGpppTgRUSqKSV4EZFqSgleRKSaUoIXEammlOBFRKopYyt7km3fjo3JA74v58ubAflxDCdeqmpcUHVjU1xlo7jKpjrG1d5a6+i26IQl+JNhjMm01jr72ftKVFXjgqobm+IqG8VVNqd6XGqiERGpppTgRUSqKbcm+OcTHUAEVTUuqLqxKa6yUVxlc0rH5co2eBERic2tNXgREYlBCV5EpLqy1rrqHzAW2ABkAdMqYPvtgM+B9cA64Ebv8unADmCV99/4gNfc5o1nA3B+wPIBwFrvuicpaRKrCbztXb4MSHcY21bv9lYBmd5lTYDPgE3e/xtXZlxAt4Bjsgo4CNyUqOMFvATkAt8GLKuUYwRc6d3HJuBKB3E9AnwHrAE+ABp5l6cDPwYcu+cqOa5K+ezKEdfbATFtBVZV5vEicm5I+Pcr4t9DPJNjRf8DkoHNQEegBrAa6BnnfbQC+nsf1wc2Aj29X/rfhynf0xtHTaCDN75k77rlwGDAALOBcd7l/+f7EgKXAW87jG0r0Cxk2Z/wnuiAacDDlR1XyOfzA9A+UccLOAfoT3BiqPBjhOePPNv7f2Pv48Yx4hoDpHgfPxwQV3pguZD3VxlxVfhnV564QmJ5FLi7Mo8XkXNDwr9fEf8eypMEE/XPe0A+DXh+G3BbBe/zI2B0lC99UAzAp944WwHfBSyfBPwtsIz3cQqeO9qMg1i2UjrBbwBaBXwBN1R2XAHbGgN86X2csONFyB98ZRyjwDLedX8DJkWLK2TdhcA/o5WrrLgq47M7mePlff12oEsijleY3FAlvl/h/rmtDb4Nng/WJ8e7rEIYY9KBM/BcKgFMNcasMca8ZIxpHCOmNt7H4WL1v8ZaWwgcAJo6CMkCc4wxK4wxU7zLWlhrd3m3tQtonoC4fC4D3gx4nujj5VMZx+hkv5tX46nJ+XQwxnxjjFlgjBkWsO/KiquiP7uTOV7DgN3W2k0Byyr1eIXkhir7/XJbgjdhltkK2ZEx9YD3gJustQeBZ4FOwOnALjyXiNFiihZred/HEGttf2AccL0x5pwoZSszLowxNYCfAv/yLqoKxyuWeMZyMsfuDqAQ+Kd30S7gNGvtGcBvgTeMMQ0qMa7K+OxO5jOdRHBFolKPV5jcEEnCj5fbEnwOno4On7bAznjvxBiTiucD/Ke19n0Aa+1ua22RtbYY+DswMEZMOd7H4WL1v8YYkwI0BPbGistau9P7fy6eTrmBwG5jTCvvtlrh6Ziq1Li8xgErrbW7vTEm/HgFqIxjVK7vpjHmSuAnwOXWe+1trT1urd3jfbwCT9tt18qKq5I+u/IerxTgIjwdkb54K+14hcsNVOHvV4W1XVfEPzxtUtl4Oix8nay94rwPA7wK/CVkeauAxzcDb3kf9yK4IyWbko6Ur4GzKOlIGe9dfj3BHSnvOIirLlA/4PESPCOKHiG4g+dPlRlXQHxvAZOrwvGidJtyhR8jPJ1fW/B0gDX2Pm4SI66xwH+BtJByaQFxdMQzoqVJJcZV4Z9deeIKOGYLEnG8iJwbqsT3K+zfwskkw0T8A8bj6b3eDNxRAdsfiufSZw0Bw8SA1/AMa1oDzAz5I7jDG88GvL3h3uUZwLfedU9RMhSqFp6mjCw8vekdHcTV0ftlWY1niNYd3uVNgXl4hk7NC/zQKyMu7+vqAHuAhgHLEnK88Fy67wJO4Kn1XFNZxwhPO3qW999kB3Fl4WlX9X3PfH/YF3s/49XASuCCSo6rUj67ssblXf4KcF1I2Uo5XkTODQn/fkX6p6kKRESqKbe1wYuIiENK8CIi1ZQSvIhINaUELyJSTSnBi4hUU0rwIiLVlBK8iEg19f8B+0l3dDcvrEIAAAAASUVORK5CYII=", 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" ] @@ -468,7 +470,7 @@ "outputs": [ { "data": { - "image/png": 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f69Drnb3qica149UzQ7ZlgCH8jD9F4UmloaCWzq3W0rnViiesunpjipSE+LD2iYa6Gq3ZuK+/st3zR9/Q7NVb+pf//td+NWQbBhjC7zjDhucFA0aTJ4QJax9hgCEwFIENwHVGujd7MAYYohgQ2ABciQGGQDquYcP1YvGE3ozGuUZdhFIDDLe0nFJj8xEdOt2pUMAolrC6sXKSGupqtKR2umfPrDm2MRoENlzJStr40nE1Nh/V4bQP6XKtqqtRfW2VZz+kMTp+G2DYE4trc0sbxzZGjS5xuM7u1g693HZeazbu06H2TlmbnPvYWulge6fWbGRKxWLl9QGGu1s7tGDtVo5tjAmBDVfZ09qhB9Zt6zuTcp55iSkV4UWpY7ujO8qxjTEhsOEaPbHkFInd0eymSGRKRXgFxzZygcCGa2xuaVM0nhh5xQGYUhFe4HRsf+nem/WHC6/tf/zwe2brj+66rv8xxzYGI7DhGo3NRx27Cj98W7W2PHSXtjx0l/7XH8xJW8aUivACp2P7X/ae1AffVtX/+P21Vdq8t63/Mcc2BmOUOFwhnrA6PGjmJUmaXVmuzy66QR9tfF5n34zqirLwkHVSUyp6dSAS/C3Tsb3/5HldWV6qykmlurK8ROe6ozp57mLaOhzbGIjAhit09cYUChhF4+mzNN1xw1Xa0nJKZ9+MSpLOdUeHbJuaUnHyhKFhDhRapmNbSnaV19dW6epJpfqXAWfXKRzbGIgucbiC05SKkmQkWQ19fiCmVISbZTq2Jelf9pzUB+dcoyW3TtfmlqGBzbGNgQhsuILTlIqS9Osjr+v9tVWqmJg8w3DqEmdKRbhZpmNbkg6f7lSkNKj28z363YWeIcs5tjEQgQ3XaKirGVIz+vDpTj3+yyP6x5Xv1JaH7tJffuCtacuZUhFe4HRsp9z76LN64IltQ57n2MZg9LXANeprq/TIpgOS0kfT/ujFE/rRiycct2FKRXhBpmN7OBzbGIwzbLgGUyrCrzi2kQsENlwlNaViMGCYUhG+wnShuFx0icN15sys0JmqyVp782xfTqmI4uX36UIxvghsuJKRfDWlIpDit+lCkT8ENlwvNaUi4Dcc2xgNrmEDAOABBDYAAB5AYAMA4AEENgAAHkBgAwDgAQQ2AAAeQGADBRSLJ3T+YlTxDNMvAkAK92EDedYTi2tzS5teP92pT63ZMqDSVblW1dWovraKSlcAhuAMG8ij3a0dWrB2q9Zs3KeL0bislaJxK2ulg+2dWrNxnxas3ao9rR2F3lUALkNgA3myp7VDD6zbpo7uqLp645oUKdNTD9+dtk5Xb1wd3VEtW7eN0AaQhsAG8qAnFtfy9TvUHc1uPuTuaHL9nlj28ycD8DcCG8iDzS1tisYTGZfPnFqmn33+Tr1txhX9z0XjCW1pOZWP3QPgAQQ2kAeNzUfV1et8tnz9VRF98w9v15/9017tPX6u//mu3rgam4/kaxcBuByjxIFxFk9YHT7d6bhsaqRET3xynlZ9f5fjOodOdyqesEy9CIAzbGC8dfXGFMoQuBcuRnXyXLfmzZriuDwUMOrqjY3n7gHwCAIbGGeRkpBiGQqjRONWK7+7Sx++bYY+NOeaIctjCavSYIDiKgDoEgfGWzBgNLuyXIfanbvFu6Nxffo7O/W9Ty9QdzSuXxxo718WDgR085f/leIqADjDBvKhoa5GkZL0gL3Q1a17Hn1GknT+Ykz3Pf7rtLCWpN54guIqACQR2EBe1NdWKRy8vF+3h98zW5+563qKqwBFisBGUSj0JBuloaA2rJivsnDuurEprgIUF65hw7dSk2w0Nh/V4dOdBb8OPGdmhZpWLtTy9Tv6iqiMPPr7s4tu0Idvq1Zbx0Wd6epRy4nzactTxVWWzq0ep70G4BacYcOXBk6ycai90zXXgefMrND21Yu19v5aTQgHZYwUDhoZI5UM6jK/tXqyPjinSu9/7Fmt+v4uvW1GxZCfR3EVoHgQ2PCdwZNsTJ4Q0h8uvDZtnUJeBy4NBbV0brVmV5bryNp67frL9+rgV5comkgvXTp/1lQ9tb9dF6MJdfbE9PTL7Y4/L1VcBYC/EdjwFadJNiaXhfWJQYGdUujrwMGA0eQJYV2MxZ2Lq9iRg5jiKkBxILDhK06TbPyXe2/WtVdO1ObP36m/WHLzkG3cMMmGU3GV7a+e0ftuma7SUECRkqAWv3Wa47axhFWkhOEogN/xWw5fcZpk42//9Te6cdok1T/2nOM2qevAhRy45VRcZf/J83pyb5s2P3SXTpzt1s5jZxy3vbGynFrjQBEgsOEbw02yMRI3TLLRUFejNRv3pf3B8fgvj+jxX2YeVBYpCaqh7oZ87B6AAqNLHL4x3CQbI3HDdeCxFFcJBwNaUjt9nPYIgJsQ2PCNTJNsdPbEFCkd/n5rN1wHHm1xlbJwcn1qigPFgcCGb6SuAw/W8WZUu147q6cevttx0JnknuvAqeIqFWXhIbXHUyIlQVWUhdW0cqHmzKzI7w4CKBiuYcNXnK4DS9JDTbszbuO268Cp4ipbWk6psfmIDqVVaZukhroaLamdzpk1UGQIbPhKfW2VHtl0QFL291W78TpwqrjK0rnViiesunpjipSEXNELAKAw6BKHr/jxOnCquAphDRQ3Ahu+4/XrwIWeWQyAO9ElDl/y2nVgt80sBsB9CGz4lleuA+9u7dCDfVNupgbLRePJs+vUzGKPbDqgDSvmu643AED+0CWOouDW68CDZxZzUsiZxQC4B4ENFIiVhswsNpxCzywGoLDoEgcK5Fx3NG1msY8veIs+vuAtkqRJE8I6frZbDzyxLW2b1MxihZyoBEBhcIYNFMjvLvSkdYP/v+2/Vf1jz+lD3/i12s5d1Leee2XINqmZxQAUHwIbKIB4wupihq7wr3zwFv370de19eXTjstTM4sBKC4ENlAAXb0xGTN0ANxHb5+h6illenTr4YzbumFmMQD5xzVsoAAiJSFZm36WfGv1ZH3mruv1sf/7vOwwJ9BumFkMQP7l5AzbGHOvMeagMeaIMeZLDsvrjDHnjDG7+76+nIvXRXHxUwWwYMBowqDyqcvfOUsVE8Nq+sxCbf78nfqbj9Q6buuWmcUA5Ndl/5lujAlKelzSeyUdl7TTGLPJWntg0KrPWms/cLmvh+Li5wpgV08qVaQk3j/w7M/+ee+I27htZjEA+ZOLfrX5ko5Ya1+RJGNMk6T7JA0ObGBU/F4B7IqysMJBK6/PLAYgP8zg62ij/gHGfFTSvdbaP+p7/AlJC6y1nxuwTp2kHyl5Bn5S0hettfsz/LyVklZK0rRp025vamoa9T51dnaqvLx81Nt5id/beP7CBbVesEpkcXwGjNH1V0eynqHLLTo7OxUsLdMrv+vyZTv9foxKtNEP3Na+RYsW7bLWznNaloszbKeLaYM/fV6UdK21ttMYUy/pJ5JmO/0wa+06Seskad68ebaurm7UO9Tc3KyxbOclfm5jTyyub//Tz/Q/9mYfTBVlcW1fXeep7vHUe7intUPLB/UkDBQpCSocDHiuJ8HPx2gKbfQ+L7UvF4POjkuaOeDxDCXPovtZa89bazv7vt8sKWyMuSoHrw0f2tzSNuwoaSepCmBelJpZbO39tbppWrmMkcJBI2Okm6ZN0tr7a7V99WJPhTWA3MvFGfZOSbONMddJOiFpmaT/OHAFY8x0Se3WWmuMma/kHwpv5OC14UONzUf1wcr0xC4LB/X4x29T1RUTFDBG/+ffDuvJvW39y1MVwLxastMrM4sBKJzLDmxrbcwY8zlJT0kKSlpvrd1vjFnVt/ybkj4qqcEYE5PULWmZvdyL5/CleMLq8OlOqTL9+XffdLXaz1/Uiu/slCRNKh166KYqgHk95FIziwHAQDmpvtDXzb150HPfHPD9NyR9IxevBX/r6o0p5BC4B09d0Or6t+pL996srb9p185jZ4esk6oARtgB8CNKk8JVIiUhxRwKo7z6epc+8H+e029OXdCf33uzPr946L3IVAAD4Gd8usFVggGj2ZXlkjrSnq+cVKpz3VH9ZPcJvdkb00dvnzFkWyqAIRZP6M1onOv/8CUCG67TUFejky+/mPbczdMn6S/q3yprraJxqzU/2Ze2nApgxcvP1fCAgQhsuE59bZW+/Zv05545/Lqe+fqzGbehAlhx8ns1PGAgrmHDdUpDQV13VfYVvcrCQW1YMZ+zqCKzp7VDD6zbpo7uaH9Y73/knrR1unrj6uiOatm6bdrT2lGAvQRyh8CGK5WFg2pauVAVZWFFSpyDOFISVEVZWE0rF3L2VGR6YnEtX79D3dHs6rB3R5Pr98Syr9sOuA2BDdeiAhgy2dzSpmg8MaptvFwND5C4hg2XowIYnDQ2H3Wsuz4cr1fDAzjDhmekKoC5Jaxj8YTOX4wq7nDfOMZPfzW8MUhVwwO8iDNsYBS4hajwUtXwUqPBR4NqePAyAhvIErcQuUOmanjZoBoevIwucSALTrcQDcYtRPlxqRre6FEND15GYAMj4BYi92moq3G83e+WrzyVcRuq4cHrCGxgBE63EL1txhXa8tBdKg0FVBYO6ud/crdunHbprI9biMZXfW2VwsHRfXxRDS/3GHiZX1zMAUbgdAvR3uPn9PTL7frT992kCeGAfvLSCR1qvzRymVuIxldpKFndbtm6bVn1fFANL3cYeFk4nGEDwxjuFqLHth7WXbOv0tuqr9A3f3V0yHJuIRpfc2ZWUA0vz3a3dmjB2q1as3GfDrV3ytrkwEtrLw28XLB2K2M4xgmBDQwjdQuRk4qyEk0sCSpSGnI8o0jdQoTxQzW8/GHgZeHRJQ4MY7hbiP7bh2v1tZ8f0sypE/WlJTfrK5v2py3nFqL8oBre+BvrwMvtqxfTPZ5DfJoAw0jdQjTw+rQkffi2ZDhs2nNSASP9uOEOvbPmSv370Tf61+EWovxLVcNDbg0ceDljSpk2fGq+dr52RnNnTtHLbef1T7ta9SfvuVFXlpfq4aaXtOf4uf6Bl4zjyB26xIERON1C9OMXT2jV93dJkhJWWvp3z6eFNbcQwU8GD7y89sqJ+vvnjunerz+jmqvLdd/bq/XRb/67/tvml/XZRcnjPjXwErlDYAMj8MItRNxeg/E0eOBl69luHWy/IGulQ6cv6NdHXpck/ebUec2YUta/HgMvc4sucWAEbr2FiNtrkA8Ja4fUbu+NXapLYK3tf2ytFAxc+uOW2u25xRk2kAW33ULE7TXIl4Ax1G53CQIbyJJbbiHi9hrkG7Xb3YE/fYBRKPQtRNxeg0JoqKvRmo371NUb1/Gz3brn0Wf6l33xn/b2fz9wGQMvc48zbGCMUrcQ5fMMwqmuuRMzYJeoa47L5YWBl8WAwAY8xKmuuZS8N/bpL7xbX73vVv3s83fqmisujdTl9hpcrtTAy7Jwdr001G4fHwQ24BHD1TWXpOuviujHLx7X+x97Tic6utOWcXsNLpfbBl4WI65hAx6Rqms+8PaagU50dOulDAPMuL0GuZAaeLml5ZQam4/oUNrthJPUUFejJbXTObMeJwQ24BHD1TWXpDczjBiXuL0GuVPogZfFjC5xwCNSdc3HgttrMB4KMfCymBHYgIc41TUfCbfXAP5AYAMekun2msH3xg7E7TWAPxDYgIdwew1QvAhswGO4vQYoTgwbBTyI22uA4kNgAx7F7TVAcSGwAR9I3V4DwL+4hg0AgAcQ2AAAeEDRBHYsntD5i1EmQAAAeJKvr2H3xOLa3NKmxuajOpw2irZcq+pqVF9bxShaAIAn+Dawd7d26MH1OxSNJ/rnD07NcnSwvVNrNu7TI5sOaMOK+dynCgBwPV92iXdH43pg3TZ1dEf7w3qwrt64OrqjWrZum/ZkmJIQAAC38F1g98TievX1LnVHM081OFB3NK7l63eoJ5bd+gAAFILvAntzS5vsKMeVReMJbWk5NT47BABADvgusBubjyoxKLHXfeJ2/cvn7tTP/+RuPTB/5pBtunrjamw+kq9dBABg1Hw16CyesDp8ulOqTH/+z/55r851R1UaCmjT5+7Uln2n1PFmNG2dQ6c7FU9YyjoCAFzJV4Hd1RtTyCFwP/WuWbrnluR8wFUVE3TdlRG99GZH2jqhgFFXb4zyjgAAV/JVYEdKQooNKoyy8PqpetcNV+n+v/u1LkYTalq5UKXhoVcCYgmrSImv/jsAAD7iq2vYwYDR7MrytOcmTQjrXHdUF6MJ1Vwd0dwM91zfWFlOdzgAwLV8FdiS1FBXo4C5FLy/Ovg7hQJGWx66S1947016yeGe60hJUA11N+RxLwEAGB3f9QHX11bp27+59Lg3ntCDf79z2G3CwYCW1E4f5z0DAGDsfHeGXRoK6rqrIioLZ1cjvCwc1IYV86kpDgBwNd8FtpQM4aaVC1VRFlakxDmIIyVBVZSF1bRyIbXEAQCu57su8ZQ5Myu0ffVibWk5pcbmIzqUNlvXJDXU1WhJ7XTOrAEAnuDbwJaS3eNL51Zr6dxqxRNWXb0xRUpCjAYHAHiOrwN7oGDAUBQFAOBZvryGDQCA3xDYAJBHsXhC5y9GFU+MclpBFL2i6RIHgELpicW1uaVNjc1HdThtAGy5VtXVqL62igGwGBGBDQDjaHdrhx5cv0PReEJdvXFJUjSePLs+2N6pNRv36ZFNB7RhxXxuMcWw6BIHgHGyp7VDD6zbpo7uqKZESvTUw3cPWaerN66O7qiWrdumPQ6lk4EUAhsAxkFPLK7l63eoOxrPav3uaHL9nlh266P4ENgAMA42t7QpGk+kPRcMGP31h2v18z+5W99dMV+lofSP4Gg8oS0tp/K5m/AQAhsAxkFj89H+a9Yps66cqO/9+2t63/9+RucvRrXk1qq05V29cTU2H8nnbsJDCGwAyLF4wurw6c4hz7ee7daBtvOSpH0nzmnG1LIh6xw63cktX3BEYANAjnX1xhRyKIHcG7vURR5PyHGdUMCoqzc2rvsHbyKwPYSCC4A3REpCio3x9zSWsIqUcMcthuKocLlMBRf+61yrsy8dp+AC4ELBgNHsynIdah/aLT6SGyvLmaAIjghsFxuu4MLFaJyCC4CLNdTVaM3Gff2/u8fPduueR5/pX/7Es68M2SZSElRD3Q1520d4C13iLjWw4MLgkaYpFFwA3Ku+tkrh4Og+YsPBgJbUTh+nPYLXEdguRMEFwPtKQ0FtWDFfZeHsLlmVhZPrc4kLmRDYLuRUcEGSPn3ndXrq4bv11MN3a85N16cto+AC4D5zZlaoaeVCVZSFFSlxDuJISVAVZWE1rVzIpS0Mi2vYLuRUcOHW6sn62LwZWvr4r2WM9OwX7tAt13Ro/8nkPZ2pggtL51YXYpcBZDBnZoW2r16sLS2n1Nh8RIfSZuuapIa6Gi2pnc6ZNUZEYLtMpoIL75g1VU/tb+/vJj/a2qZ3zJraH9jSpYILjDAF3KU0FNTSudVaOrda8YRVV29MkZIQv6sYFbrEXSZTwYVsfq0puAC4XzBgNHlCmLDGqBHYLpOp4ML2V8/ofb83TRPCAZWFg6qZWaWdx86krUPBBcB9KHiEXOHT3WUyFVzYf/K8/nnXcf30s3cmHx85ltYdLlFwAXCLTAWPbqws16q6GgoeYUwIbBcaXHAh5dvPvapvP/eqJOlPa2Ma+PZRcAFwh+EKHh1s76TgEcaMLnEXouAC4E0UPMJ4IrBdiIILgPcMV/DoRw13DHmOgkcYLQLbpSi4AHhLpoJHkvSRxucdn6fgEUaDwHaxVMGFtffX6qZp5TJGCgeNjJEmhINae3+ttq9eTFgDLuBU8Chl/yP3OD6fKngEZCMng86MMfdK+rqkoKRvWWv/ZtBy07e8XtKbkh601r6Yi9f2u0wFF5595leqo6rZmMTiCb0ZjVO4AjmTqeBRNih4hGxddmAbY4KSHpf0XknHJe00xmyy1h4YsNoSSbP7vhZIauz7F6OQKriA0eM2G4ynVMGj1Gjw0UgVPOJ3GyPJRZf4fElHrLWvWGt7JTVJum/QOvdJ+q5N2iapwhhTlYPXBka0u7VDC9Zu1ZqN+3SovVPWJm+zsfbSbTYL1m5lxG6B+KGwSKaCR9mg4JF35fvYzcVRUi2pdcDj4xp69uy0TrWkthy8PpBR6jab4aYqTV53jGvZum0M4MsTv/V4ZCp4lA0KHnlLIY9dY+3l/WVgjPmYpHustX/U9/gTkuZba/94wDo/k/TX1trn+h5vlfTn1tpdDj9vpaSVkjRt2rTbm5qaRr1PnZ2dKi8vH0tzPMPvbcxF+6ykl9vOj+qv32DA6K1Vk7Oq3X65ivU97I7G9errXbJWSjh8/gRMcmDldVdFsr61sVAGtrGjO6oTZ7sd2/Sf/uD9+r8//NmQ5wPGqHpKmSrK3NsdXqzHqZN8HLuLFi3aZa2d57QsF2fYxyXNHPB4hqSTY1hHkmStXSdpnSTNmzfP1tXVjXqHmpubNZbtvMTvbcxF+za+dFyP/9vQinHDiZQEtfbm2XmZprQY38M9rR1qWLdN3dGRP8zKwlE1rbzd1T0eA9vYE4trwdqt6uiOpq1TMTGs+zt79bWWoR+3FWVhbV/9+67uTSjG49SJG47dXFzD3ilptjHmOmNMiaRlkjYNWmeTpE+apIWSzllr6Q7HuBp8m80X3nujPvWuWf2Pv/i+m/TgHbPStuE2m/EzXGERJ14rLOJU8KhyUql+3HCHnnj21SHrU/DIO9xy7F52YFtrY5I+J+kpSS9L+qG1dr8xZpUxZlXfapslvSLpiKQnJP3ny31dYDhOt9n88IVWfeS2GZIkY6QPzqnST3afGLJt6jYb5NZwhUUy8VphkcEFj05f6NHvf+1X2vD8sf51KHjkPU7H7mcX3aCtf/puff/TC/TYsrfrM3ddn7Z8PI7dnAxNtNZuVjKUBz73zQHfW0mfzcVrAdlwus3m+NlunX2zV7dcM1lXlZdq/8nz6ngzOmRbbrMZH06FRWZMKdN3PjVfO4+d0e3XTtGpcxf1me++oJ5Y8sMx1eORj0sUuZIqeLSl5ZQam4/oUNrApElqqKvRktrpnFl7yOBj99bqyfrgnCq9/7FnFQoE9OQf36mWE+mzJ47Hscu9BPClTLfZ/OPOVn309hm6urxUP3yh1WFLbrMZD8MVFpl15UR9/gcv6S9+3KJv/Me5WnJres+HFwuLZCp45KU2IMnp2J0/a6qe2t+ui9GEpISefrndcdtcH7uUJoUvpW6zGeyp/ad0941X620zKvTMod85bsttNrmX6vFw0nq2Wwfakmcn+06c04ypZWnLUz0eXpUqeMQx5U0Zj90s7rDK9bFLYMO3GupqhkycEo1bbTv6hn7WclJOl6mZV3x8DFdYpDd26dpgPKEhH470eKCQnI7d7a+e0ftuma7SUECRkqAWv3Wa47a5PnYJbPiW07zixkhz31Khf9zp3B3OvOLjI1OPRzbo8UAhOR27+0+e15N727T5obvU+Ie3a+exM47b5vrYJbDhW4Nvs7mhsly/+uIi/froGzr2xptD1uc2m/Hl1OMxEno84AZOx+7jvzyixV/7lT65fodOdnQP2WY8jl0CG7428Dabto5u3f0/fqm1P3s5bR1us8kPpx6P42e7dc+jz/Q/fuLZV/To04f7H9PjATdwOnZHMh7HLoEN3xtuXvGbpk1iXvE8cSosMhx6POAWIx27jz59WE88+0r/4/E6dhnJgaLAbTbukOrxWL5+h6LxhGPZ2EhJUOFgQBtWzOePKLiGG45dAhtFh3nFC4vCIvCqQh+7BDaAvKPHA15VyGOXwAZQUPR4wKvyfewy6AwAAA8gsAEA8AACGwAADyCwAQDwAAIbAAAPILABAPAAAhsAAA8gsAEA8AACGwAADyCwAQDwAAI7S7F4QucvRhVP2ELvCgCgCFFLfBg9sbg2t7SpsfmoDqfNylKuVXU1qq+tYkYhAEBeENgZ7G7t0IOD5j2NxpNn1wfbO7Vm4z49sukAc/YCAPKCLnEHe1o79MC6berojjpOUi5JXb1xdXRHtWzdNu1p7cjvDgIAig6BPUhPLK7l63eoO+oc1IN1R5Pr98SyWx8AgLGgS3yQzS1tisYTac8tfXu1HnzXLJUEjXa3dmjNT/Zp4NizaDyhLS2ntHRudZ73FgBQLDjDHqSx+WhaN3jN1eX6wJwqfbTxedU/9pziCQ0J5q7euBqbj+R7VwEARYQz7AHiCavDpzvTnnvXDVeqtvoKbfrcuyRJpeGg3ujqGbLtodOdiiesggGTl30FABQXAnuArt6YQgHTPxpckowx+tGu4/rvTx0cdttQwKirN6bJE8LjvZsAgCJEl/gAkZKQYoMKo/z6yOtaUlulKyMlkqQrysKqrigbsm0sYRUp4e8fAMD4IGEGCAaMZleW61D7pW7xI6c79bWfH9T3Pj1fxhjF4lZf/uk+nejoTtv2xspyusMBAOOGwB6koa5GazbuSxt49uTeNj25ty3jNpGSoBrqbsjH7gEAihRd4oPU11YpHBzdf0s4GNCS2unjtEcAABDYQ5SGgtqwYr7KwtnVCC8LJ9enpjgAYDwR2A7mzKxQ08qFqigLK1LiHMSRkqAqysJqWrmQWuIAgHHHNewM5sys0PbVi7Wl5ZQam4/oUNpsXZPUUFejJbXTObMGAOQFgT2M0lBQS+dWa+ncasUTVl29MUVKQowGBwDkHYGdpWDAUBQFAFAwXMMGAMADCGwAADyAwAYAwAMIbAAAPIDABgDAAwhsAAA8gMAGAMADCGwAADyAwAYAwAMIbAAAPIDABgDAAwhsAAA8gMAGAMADCGwAADyAwAYAwAMIbAAAPIDABgDAAwhsAAA8gMAGAMADCGwAADyAwAYAwAMIbAAAPIDABgDAAwhsAAA8gMAGAMADCOwiEIsndP5iVPGELfSuAADGKFToHcD46InFtbmlTY3NR3X4dKdCAaNYwurGynKtqqtRfW2VSkPBQu8mACBLBLYP7W7t0IPrdygaT6irNy5JisaTZ9cH2zu1ZuM+PbLpgDasmK85MysKuKcAgGzRJe4ze1o79MC6berojvaHtST9qOGO/u+7euPq6I5q2bpt2tPaUYC9BACMFoHtIz2xuJav36HuaHzIso80Pj/kue5ocv2e2ND1AQDuQmD7yOaWNkXjCcdl+x+5x/H5aDyhLS2nxnO3AAA5QGD7SGPz0bRu8Gx09cbV2HxknPYIAJArBLZPxBNWh093jmnbQ6c7ueULAFyOwPaJrt6YQgEzpm1DAaOu3liO9wgAkEsEtk9ESkKKjfEsOZawipRwhx8AuBmB7RPBgNHsyvIxbXtjZbmCYzw7BwDkB4HtIw11NYqUOFcvu+UrTzk+HykJqqHuhvHcLSAvKMELv6Mf1Efqa6v0yKYDkrIfKR4OBrSkdvr47RQwjijBi2LCGbaPlIaC2rBivsrC2X1AlYWT6/OBBi/a3dqhBWu3as3GfTrU3ilrkyV4rb1UgnfB2q1U84NvENg+M2dmhZpWLlRFWThj93ikJKiKsrCaVi6kljg8KVMJ3oEowQu/IbB9aM7MCm1fvVhr76/VTdPKZYwUDhoZI900bZLW3l+r7asXE9bwpOFK8DqhBC/8gmvYPlUaCmrp3GotnVuteMKqqzemSEmI0eDwvOFK8GaSKsG7dG71OO0VMP44wy4CwYDR5Alhwhq+QAleFCsCG4BnUIIXxYzABuAZlOBFMbusa9jGmKmS/lHSLEnHJP2Btfasw3rHJF1Q8gbhmLV23uW8LoDiRAleFLPLPcP+kqSt1trZkrb2Pc5kkbX27YQ1gLEaqQTv3z/4DlVOKnVcRgleeN3lBvZ9kjb0fb9B0tLL/HkAMKzhSvB+6js7dfpCz5DnKcELP7jcwJ5mrW2TpL5/KzOsZyX93Bizyxiz8jJfE0ARq6+tUjg4uo8uSvDCD4y1w18PMsY8LcnpSF8taYO1tmLAumettVMcfsY11tqTxphKSb+Q9MfW2mcyvN5KSSsladq0abc3NTVl25Z+nZ2dKi8f28xVAyWsVcC4swstV210K7+3T/J/G8ezfd3RuF75XZcSI3x+SVLAGF1/dSTrkr2j4ff3UPJ/G93WvkWLFu3KdOl4xMAejjHmoKQ6a22bMaZKUrO19qYRtvkrSZ3W2v850s+fN2+efeGFF0a9X83Nzaqrqxv1dl6aSGCsbfQKv7dP8n8bx7t9e1o7tHz9DkXjCcf7siMlQYWDAW1YMX/cqvr5/T2U/N9Gt7XPGJMxsC+3S3yTpOV93y+X9FOHF48YYyalvpf0Pkn7LvN1c46JBABvoQQvis3l3uPwN5J+aIz5tKTfSvqYlOwCl/Qta229pGmSNppk13JI0j9Ya//1Ml83p1ITCQxXmzj5F3xcy9ZtY9IMwCUowYticlmBba19Q9Jih+dPSqrv+/4VSXMu53XG01gnEti+erFruscBXCrBC/hV0Vc6c5pIYMaUMj318N39jz9z1/V6+D2z+x+nJhIAACBfij6wmUgAAOAFRR3YTCQAAPCKog7sTBMJxOJWA58uDQ/9b2IiAQBAPhV1YGeaSOD1zh5dWV6qiolhlQQDWnzz0AJuTCQAAMinok6c1EQCh9rTu8VjCavHth7WT/7zu9R69k0d/d3QbnMmEgAA5FNRB7aUnEhgzcZ9Qwaefef5Y/rO88cct2EiAQBAvhV1l7jERAIAAG8o+sAuDQW1YcX8rCcGKAsn16doCgAgn4o+sKVkTeKmlQtVURbOOM9upCSoirIwZUkBAAVR9NewU1ITCWxpOaXG5iM6lDZb1yQ11NVoSe10zqwBAAVBYA/ARAIAALcisDNgIgEAgJtwDRsAAA8gsAEA8AACGwAADyCwAQDwAAIbAAAPILABAPAAAhsAAA8gsAEA8AACGwAADyCwAQDwAGOtLfQ+ZGSM+Z2k18aw6VWSXs/x7riN39vo9/ZJ/m+j39sn0UY/cFv7rrXWXu20wNWBPVbGmBestfMKvR/jye9t9Hv7JP+30e/tk2ijH3ipfXSJAwDgAQQ2AAAe4NfAXlfoHcgDv7fR7+2T/N9Gv7dPoo1+4Jn2+fIaNgAAfuPXM2wAAHzFs4FtjJlqjPmFMeZw379THNa5yRize8DXeWPMw33L/soYc2LAsvq8N2IE2bSxb71jxpiWvna8MNrtCyXL93CmMeaXxpiXjTH7jTEPDVjmyvfQGHOvMeagMeaIMeZLDsuNMeaxvuV7jTG3ZbutW2TRxo/3tW2vMeZ5Y8ycAcscj1c3yaJ9dcaYcwOOvS9nu61bZNHGPxvQvn3GmLgxZmrfMi+8h+uNMaeNMfsyLPfe76G11pNfkv67pC/1ff8lSX87wvpBSaeUvMdNkv5K0hcL3Y5ctFHSMUlXXe7/kRvbJ6lK0m1930+SdEjS77n1Pew7zo5Kul5SiaQ9qf0dsE69pC2SjKSFkrZnu60bvrJs4x2SpvR9vyTVxuGOV7d8Zdm+OklPjmVbN3yNdj8lfVDSv3nlPezbx7sl3SZpX4blnvs99OwZtqT7JG3o+36DpKUjrL9Y0lFr7VgKsRTKaNuY6+3H24j7Z61ts9a+2Pf9BUkvS6rO1w6OwXxJR6y1r1hreyU1KdnOge6T9F2btE1ShTGmKstt3WDE/bTWPm+tPdv3cJukGXnex8txOe+Db97DQR6Q9IO87FmOWGufkXRmmFU893vo5cCeZq1tk5If6pIqR1h/mYYecJ/r6wpZ77bu4j7ZttFK+rkxZpcxZuUYti+UUe2fMWaWpLmStg942m3vYbWk1gGPj2voHxiZ1slmWzcY7X5+WskzmZRMx6tbZNu+dxpj9hhjthhjbhnltoWW9X4aYyZKulfSjwY87fb3MBue+z0MFXoHhmOMeVrSdIdFq0f5c0okfUjSXwx4ulHSV5U88L4q6WuSVoxtT8cuR218l7X2pDGmUtIvjDG/6fvrsuBy+B6WK/mB8bC19nzf0654DwcxDs8NvhUj0zrZbOsGWe+nMWaRkoF954CnXXu89smmfS8qeXmts2/sxE8kzc5yWzcYzX5+UNKvrbUDz1bd/h5mw3O/h64ObGvtezItM8a0G2OqrLVtfd0Yp4f5UUskvWitbR/ws/u/N8Y8IenJXOzzaOWijdbak33/njbGbFSyS+cZSaP5PxoXuWifMSasZFj/P2vtjwf8bFe8h4MclzRzwOMZkk5muU5JFtu6QTZtlDHmbZK+JWmJtfaN1PPDHK9uMWL7BvzRKGvtZmPM3xljrspmW5cYzX4O6Z30wHuYDc/9Hnq5S3yTpOV93y+X9NNh1h1y/aUvIFLul+Q4krDARmyjMSZijJmU+l7S+3SpLaP5PyqEbNpnJH1b0svW2v81aJkb38OdkmYbY67r69lZpmQ7B9ok6ZN9o1QXSjrXd0kgm23dYMT9NMa8RdKPJX3CWntowPPDHa9ukU37pvcdmzLGzFfys/SNbLZ1iaz20xhzhaR3a8Dvpkfew2x47/ew0KPexvol6UpJWyUd7vt3at/z10jaPGC9iUr+Il0xaPvvSWqRtFfJN6Oq0G0aSxuVHMm4p+9rv6TVI23vlq8s23enkt1ReyXt7vuqd/N7qOTo00NKjjRd3ffcKkmr+r43kh7vW94iad5w27rxK4s2fkvS2QHv2QsjHa9u+sqifZ/r2/89Sg6qu8Nv72Hf4wclNQ3azivv4Q8ktUmKKnk2/Wmv/x5S6QwAAA/wcpc4AABFg8AGAMADCGwAADyAwAYAwAMIbAAAPIDABgDAAwhsAAA8gMAGAMAD/j+d5wZTiS2b2AAAAABJRU5ErkJggg==\n", 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", "text/plain": [ "
" ] From 435122529a856006a7991eee810df8a1db9a10cc Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 8 Oct 2022 11:10:06 +0200 Subject: [PATCH 17/20] stat bug makemore 2 corrected --- lectures/makemore/makemore_part2_mlp.ipynb | 229 +++++++++++++++------ 1 file changed, 168 insertions(+), 61 deletions(-) diff --git a/lectures/makemore/makemore_part2_mlp.ipynb b/lectures/makemore/makemore_part2_mlp.ipynb index 3a090370..3ce90f54 100644 --- a/lectures/makemore/makemore_part2_mlp.ipynb +++ b/lectures/makemore/makemore_part2_mlp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,9 +14,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# read in all the words\n", "words = open('names.txt', 'r').read().splitlines()\n", @@ -25,18 +36,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "32033" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "len(words)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], "source": [ "# build the vocabulary of characters and mappings to/from integers\n", "chars = sorted(list(set(''.join(words))))\n", @@ -48,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -73,25 +103,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([228146, 3]), torch.int64, torch.Size([228146]), torch.int64)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X.shape, X.dtype, Y.shape, Y.dtype" ] }, { "cell_type": "code", - "execution_count": 769, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([182441, 3]) torch.Size([182441])\n", - "torch.Size([22902, 3]) torch.Size([22902])\n", - "torch.Size([22803, 3]) torch.Size([22803])\n" + "torch.Size([182625, 3]) torch.Size([182625])\n", + "torch.Size([22655, 3]) torch.Size([22655])\n", + "torch.Size([22866, 3]) torch.Size([22866])\n" ] } ], @@ -130,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -139,9 +180,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([228146, 3, 2])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "emb = C[X]\n", "emb.shape" @@ -149,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -168,25 +220,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-0.1173, 0.9927, -0.3330, ..., 0.9685, -0.9261, -0.9764],\n", + " [-0.9989, 1.0000, -0.7699, ..., 0.9995, -0.9570, -1.0000],\n", + " [ 0.9983, 0.9999, -0.8472, ..., 0.9419, -0.4396, -0.9535],\n", + " ...,\n", + " [-0.9909, -0.8530, 0.8724, ..., 0.7324, 0.2845, 0.4413],\n", + " [ 0.4456, 0.8637, 0.2240, ..., 0.6836, 0.9979, 1.0000],\n", + " [ 0.8108, -1.0000, 0.9985, ..., -0.7292, -0.9997, 0.0684]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "h" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([228146, 100])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "h.shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -196,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -205,16 +285,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([228146, 27])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "logits.shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -232,18 +323,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([228146, 27])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "prob.shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "IndexError", + "evalue": "shape mismatch: indexing tensors could not be broadcast together with shapes [32], [228146]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/ibad/Desktop/nn-zero-to-hero/lectures/makemore/makemore_part2_mlp.ipynb Cell 20\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39m-\u001b[39mprob[torch\u001b[39m.\u001b[39;49marange(\u001b[39m32\u001b[39;49m), Y]\u001b[39m.\u001b[39mlog()\u001b[39m.\u001b[39mmean()\n\u001b[1;32m 2\u001b[0m loss\n", + "\u001b[0;31mIndexError\u001b[0m: shape mismatch: indexing tensors could not be broadcast together with shapes [32], [228146]" + ] + } + ], "source": [ "loss = -prob[torch.arange(32), Y].log().mean()\n", "loss" @@ -260,16 +374,16 @@ }, { "cell_type": "code", - "execution_count": 780, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(torch.Size([182441, 3]), torch.Size([182441]))" + "(torch.Size([182625, 3]), torch.Size([182625]))" ] }, - "execution_count": 780, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -280,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 790, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 791, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -304,7 +418,7 @@ "11897" ] }, - "execution_count": 791, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -315,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 792, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -325,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 793, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 794, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -347,11 +461,11 @@ }, { "cell_type": "code", - "execution_count": 795, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "for i in range(200000):\n", + "for i in range(50000):\n", " \n", " # minibatch construct\n", " ix = torch.randint(0, Xtr.shape[0], (32,))\n", @@ -385,29 +499,17 @@ }, { "cell_type": "code", - "execution_count": 796, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", 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" ] }, - "execution_count": 796, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, "output_type": "display_data" } ], @@ -587,7 +689,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.6 ('deeply')", "language": "python", "name": "python3" }, @@ -601,7 +703,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } } }, "nbformat": 4, From 70b3dec34fc6046697b11b2ef08caeeaaa56570b Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 8 Oct 2022 11:21:29 +0200 Subject: [PATCH 18/20] removed my_code --- .../makemore/1_bigram_torch_exercise.ipynb | 2464 - .../1_bigram_torch_exercise_approach_2.ipynb | 198541 --------------- .../makemore/1_makemore_bigram_torch.ipynb | 2653 - my_code/makemore/2_makemore_mlp.ipynb | 249 - my_code/makemore/names.txt | 32033 --- .../1_gradients_and_backpropagation.ipynb | 1963 - ..._activation_optmization_grad_descent.ipynb | 1610 - my_code/micrograd/micrograd_demo.ipynb | 310 - .../micrograd_ir/micrograd_ir/__init__.py | 0 .../__pycache__/__init__.cpython-310.pyc | Bin 173 -> 0 bytes .../__pycache__/engine.cpython-310.pyc | Bin 4445 -> 0 bytes .../micrograd_ir/micrograd_ir/engine.py | 130 - .../micrograd/micrograd_ir/micrograd_ir/nn.py | 58 - .../test_engine.cpython-310-pytest-7.1.3.pyc | Bin 4168 -> 0 bytes .../micrograd_ir/test/test_engine.py | 72 - my_code/micrograd/neuron_model.jpeg | Bin 37814 -> 0 bytes .../solved_micrograd_exercises.ipynb | 616 - 17 files changed, 240699 deletions(-) delete mode 100644 my_code/makemore/1_bigram_torch_exercise.ipynb delete mode 100644 my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb delete mode 100644 my_code/makemore/1_makemore_bigram_torch.ipynb delete mode 100644 my_code/makemore/2_makemore_mlp.ipynb delete mode 100644 my_code/makemore/names.txt delete mode 100644 my_code/micrograd/1_gradients_and_backpropagation.ipynb delete mode 100644 my_code/micrograd/2_activation_optmization_grad_descent.ipynb delete mode 100644 my_code/micrograd/micrograd_demo.ipynb delete mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/__init__.py delete mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/__pycache__/__init__.cpython-310.pyc delete mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/__pycache__/engine.cpython-310.pyc delete mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/engine.py delete mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/nn.py delete mode 100644 my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc delete mode 100644 my_code/micrograd/micrograd_ir/test/test_engine.py delete mode 100644 my_code/micrograd/neuron_model.jpeg delete mode 100644 my_code/micrograd/solved_micrograd_exercises.ipynb diff --git a/my_code/makemore/1_bigram_torch_exercise.ipynb b/my_code/makemore/1_bigram_torch_exercise.ipynb deleted file mode 100644 index 2ee03127..00000000 --- a/my_code/makemore/1_bigram_torch_exercise.ipynb +++ /dev/null @@ -1,2464 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises:\n", - "E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model?\n", - "\n", - "E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n", - "\n", - "E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n", - "\n", - "E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of F.one_hot in favor of simply indexing into rows of W?\n", - "\n", - "E05: look up and use F.cross_entropy instead. You should achieve the same result. Can you think of why we'd prefer to use F.cross_entropy instead?\n", - "\n", - "E06: meta-exercise! Think of a fun/interesting exercise and complete it." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn.functional as F\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"names.txt\", \"r\") as f:\n", - " words = f.read().splitlines()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total names: 32033\n", - "Minimum name length: 2\n", - "Maximum name length: 15\n" - ] - } - ], - "source": [ - "# total words\n", - "print(\"Total names: \", len(words))\n", - "\n", - "print(\"Minimum name length: \", min(len(w) for w in words))\n", - "print(\"Maximum name length: \", max(len(w) for w in words))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - 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" ('b', 'b', 'y'): 9,\n", - " ('.', 'a', 'p'): 17,\n", - " ('a', 'p', 'r'): 9,\n", - " ('p', 'r', 'i'): 51,\n", - " ('a', 'r', 't'): 102,\n", - " ('r', 't', 'e'): 28,\n", - " ('a', 'n', 'y'): 175,\n", - " ('c', 'a', 'd'): 19,\n", - " ('e', 'n', 'c'): 58,\n", - " ('m', 'a', 't'): 98,\n", - " ('t', 'i', 'l'): 33,\n", - " ('i', 'l', 'd'): 17,\n", - " ('s', 'y', 'l'): 18,\n", - " ('y', 'l', 'v'): 10,\n", - " ('l', 'v', 'i'): 37,\n", - " ('m', 'y', 'r'): 36,\n", - " ('y', 'r', 'a'): 78,\n", - " ('e', 'r', 'n'): 41,\n", - " ('r', 'n', 'a'): 35,\n", - " ('n', 'a', 'n'): 104,\n", - " ('h', 'a', 't'): 20,\n", - " ('a', 't', 't'): 73,\n", - " ('t', 't', 'i'): 38,\n", - " ('d', 'a', 'y'): 90,\n", - " ('m', 'a', 'i'): 120,\n", - " ('i', 's', 'i'): 35,\n", - " ('a', 'y', 's'): 133,\n", - " ('y', 's', 'i'): 45,\n", - " ('k', 'a', 'r'): 218,\n", - " ('e', 'e', 'n'): 200,\n", - " ('h', 'o', 'l'): 40,\n", - " ('.', 'a', 'z'): 152,\n", - " ('a', 'z', 'a'): 123,\n", - " ...}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trigrams = {}\n", - "for w in words:\n", - " #chs = [''] + list(w) + ['']\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " trigram = (ch1, ch2, ch3)\n", - " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", - "\n", - "trigrams" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(('a', 'h', '.'), 1714),\n", - " (('n', 'a', '.'), 1673),\n", - " (('a', 'n', '.'), 1509),\n", - " (('o', 'n', '.'), 1503),\n", - " (('.', 'm', 'a'), 1453),\n", - " (('.', 'j', 'a'), 1255),\n", - " (('.', 'k', 'a'), 1254),\n", - " (('e', 'n', '.'), 1217),\n", - " (('l', 'y', 'n'), 976),\n", - " (('y', 'n', '.'), 953),\n", - " (('a', 'r', 'i'), 950),\n", - " (('i', 'a', '.'), 903),\n", - " (('i', 'e', '.'), 858),\n", - " (('a', 'n', 'n'), 825),\n", - " (('e', 'l', 'l'), 822),\n", - " (('a', 'n', 'a'), 804),\n", - 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" (('a', 'j', '.'), 45),\n", - " (('a', 'v', '.'), 45),\n", - " (('r', 'l', 'o'), 44),\n", - " (('s', 'k', 'y'), 44),\n", - " (('n', 't', 'h'), 44),\n", - " (('i', 't', 'y'), 44),\n", - " (('j', 'a', 'd'), 44),\n", - " (('i', 'n', 'l'), 44),\n", - " (('k', 'i', 'm'), 44),\n", - " (('o', 's', 'i'), 44),\n", - " (('g', 'e', '.'), 44),\n", - " (('f', 'r', 'e'), 44),\n", - " (('k', 'l', 'e'), 44),\n", - " (('i', 't', 'z'), 44),\n", - " (('s', 'a', 'h'), 44),\n", - " (('.', 'c', 'i'), 44),\n", - " (('e', 'n', 'o'), 44),\n", - " (('d', 'e', 'a'), 44),\n", - " (('a', 'y', 'r'), 44),\n", - " (('e', 'v', 'o'), 44),\n", - " (('c', 'o', 'n'), 44),\n", - " (('s', 'h', 'o'), 44),\n", - " (('h', 'm', 'a'), 44),\n", - " (('e', 'c', 'k'), 44),\n", - " (('j', 'a', 'x'), 44),\n", - " (('b', 'a', 's'), 44),\n", - " (('d', 'h', 'a'), 44),\n", - " (('o', 'n', 'y'), 43),\n", - " (('s', 'i', 'd'), 43),\n", - " (('y', 'e', '.'), 43),\n", - " (('y', 's', 't'), 43),\n", - " (('l', 'o', 'u'), 43),\n", - " (('e', 'n', 'y'), 43),\n", - " (('y', 'v', 'i'), 43),\n", - " (('s', 'm', 'a'), 43),\n", - " (('u', 'h', 'a'), 43),\n", - " (('v', 'e', 'e'), 43),\n", - " (('m', 'i', 't'), 43),\n", - " (('t', 'h', 'o'), 43),\n", - " (('y', 'm', 'a'), 43),\n", - " (('d', 'i', 's'), 42),\n", - " ...]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(trigrams.items(), key = lambda item: -item[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n" - ] - } - ], - "source": [ - "chars = sorted(list(set(\"\".join(words))))\n", - "print(chars)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}\n" - ] - } - ], - "source": [ - "# string to int mapping\n", - "stoi = {}\n", - "for i, char in enumerate(chars):\n", - " stoi[char] = i+1\n", - "\n", - "print(stoi)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n" - ] - } - ], - "source": [ - "stoi = {char:i+1 for i, char in enumerate(chars)}\n", - "stoi['.'] = 0\n", - "print(stoi)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" - ] - } - ], - "source": [ - "# int to string mapping\n", - "itos = {i+1:char for i, char in enumerate(chars)}\n", - "itos[0] = \".\"\n", - "print(itos)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".em: 0 5 13\n", - "emm: 5 13 13\n", - "mma: 13 13 1\n", - "ma.: 13 1 0\n", - "tensor([[ 0, 5],\n", - " [ 5, 13],\n", - " [13, 13],\n", - " [13, 1]])\n", - "tensor([13, 13, 1, 0])\n" - ] - } - ], - "source": [ - "# First let's create a training set of bigrams (x, y)\n", - "\n", - "xs, ys = [], []\n", - "\n", - "for w in words[:1]:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " ix3 = stoi[ch3]\n", - " xs.append((ix1, ix2))\n", - " ys.append(ix3)\n", - " print(f\"{ch1}{ch2}{ch3}: {ix1} {ix2} {ix3}\")\n", - "\n", - "xs = torch.tensor(xs)\n", - "ys = torch.tensor(ys)\n", - "\n", - "print(xs)\n", - "print(ys)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([4, 2, 27])\n" - ] - } - ], - "source": [ - "xenc = F.one_hot(xs, num_classes=27).float() # we dont want an int we want a float\n", - "print(xenc.shape)\n", - "# plt.imshow(xenc, cmap='Blues')" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples: 8\n" - ] - } - ], - "source": [ - "# Create the dataset\n", - "xs, ys = [], []\n", - "\n", - "for w in words[:1]:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " ix3 = stoi[ch3]\n", - " xs.append((ix1, ix2))\n", - " ys.append(ix3)\n", - " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", - "\n", - "xs = torch.tensor(xs)\n", - "ys = torch.tensor(ys)\n", - "num = xs.nelement()\n", - "\n", - "print(\"Number of examples: \", num)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", - "W = torch.zeros((2, 27, 27), requires_grad=True) # We created 27 neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2, 4, 27])" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xenc = F.one_hot(xs, num_classes=27).float().permute(1, 0, 2)\n", - "xenc.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([2, 4, 27])\n", - "torch.Size([2, 27, 27])\n" - ] - } - ], - "source": [ - "xenc @ W\n", - "\n", - "print(xenc.shape)\n", - "print(W.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([4, 2, 27])\n", - "torch.Size([4])\n" - ] - } - ], - "source": [ - "# gradient descent in a loop -> training\n", - "\n", - "lr = 50\n", - "alpha = 0.01\n", - "\n", - "for k in range(1):\n", - " # Forwards pass\n", - " xenc = F.one_hot(xs, num_classes=27).float().permute(1, 0, 2) # input is one-hot encoded\n", - "\n", - " logits = xenc @ W # log-counts\n", - " counts = logits.exp() # counts, equivalent to N\n", - " probs = (counts / counts.sum(1, keepdim=True)).permute(1, 0, 2) # probabilities for next character \n", - " \n", - " print(probs.shape)\n", - " print(ys.shape)\n", - "\n", - " # # regularised loss\n", - " # loss = -probs[torch.arange(num), torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", - " # if k % 25 == 0:\n", - " # print('loss:', loss.item())\n", - "\n", - " # # Backward pass\n", - " # W.grad = None # set gradient to zero\n", - " # loss.backward()\n", - "\n", - " # # update\n", - " # W.data += -lr * W.grad # gradient descent" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([27])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[tensor([0, 1, 2, 3, 4, 5, 6, 7]),\n", - " tensor([0, 1, 2, 3, 4, 5, 6, 7]),\n", - " tensor([13, 13, 1, 0])]" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[torch.arange(num), torch.arange(num), ys]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb deleted file mode 100644 index 24fda497..00000000 --- a/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb +++ /dev/null @@ -1,198541 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises:\n", - "E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model?\n", - "\n", - "E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n", - "\n", - "E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n", - "\n", - "E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of F.one_hot in favor of simply indexing into rows of W?\n", - "\n", - "E05: look up and use F.cross_entropy instead. You should achieve the same result. Can you think of why we'd prefer to use F.cross_entropy instead?\n", - "\n", - "E06: meta-exercise! Think of a fun/interesting exercise and complete it." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn.functional as F\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"names.txt\", \"r\") as f:\n", - " words = f.read().splitlines()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "32033\n" - ] - } - ], - "source": [ - "print(len(words))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total names: 32033\n", - "Minimum name length: 2\n", - "Maximum name length: 15\n" - ] - } - ], - "source": [ - "# total words\n", - "print(\"Total names: \", len(words))\n", - "\n", - "print(\"Minimum name length: \", min(len(w) for w in words))\n", - "print(\"Maximum name length: \", max(len(w) for w in words))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{('.e', 'm'): 288,\n", - " ('em', 'm'): 100,\n", - " ('mm', 'a'): 72,\n", - " ('ma', '.'): 174,\n", - " ('.o', 'l'): 104,\n", - " ('ol', 'i'): 69,\n", - " ('li', 'v'): 54,\n", - " ('iv', 'i'): 78,\n", - " ('vi', 'a'): 147,\n", - " ('ia', '.'): 903,\n", - " ('.a', 'v'): 243,\n", - " ('av', 'a'): 161,\n", - " ('va', '.'): 93,\n", - " ('.i', 's'): 124,\n", - " ('is', 'a'): 142,\n", - " ('sa', 'b'): 76,\n", - " ('ab', 'e'): 173,\n", - " ('be', 'l'): 201,\n", - " ('el', 'l'): 822,\n", - " ('ll', 'a'): 337,\n", - " ('la', '.'): 684,\n", - " ('.s', 'o'): 152,\n", - " ('so', 'p'): 21,\n", - " ('op', 'h'): 37,\n", - " ('ph', 'i'): 61,\n", - " ('hi', 'a'): 81,\n", - " ('.c', 'h'): 352,\n", - " ('ch', 'a'): 236,\n", - " ('ha', 'r'): 329,\n", - " ('ar', 'l'): 287,\n", - " ('rl', 'o'): 44,\n", - " ('lo', 't'): 14,\n", - " ('ot', 't'): 34,\n", - " ('tt', 'e'): 121,\n", - " ('te', '.'): 175,\n", - " ('.m', 'i'): 393,\n", - " ('mi', 'a'): 95,\n", - " ('.a', 'm'): 384,\n", - " ('am', 'e'): 226,\n", - " ('me', 'l'): 188,\n", - " ('el', 'i'): 537,\n", - " ('li', 'a'): 518,\n", - " ('.h', 'a'): 505,\n", - " ('ar', 'p'): 8,\n", - " ('rp', 'e'): 5,\n", - " ('pe', 'r'): 77,\n", - " ('er', '.'): 683,\n", - " ('.e', 'v'): 154,\n", - " ('ev', 'e'): 142,\n", - " ('ve', 'l'): 76,\n", - " ('el', 'y'): 353,\n", - " ('ly', 'n'): 976,\n", - " ('yn', '.'): 953,\n", - " ('.a', 'b'): 190,\n", - " ('ab', 'i'): 76,\n", - " ('bi', 'g'): 15,\n", - " ('ig', 'a'): 35,\n", - " ('ga', 'i'): 18,\n", - " ('ai', 'l'): 259,\n", - " ('il', '.'): 119,\n", - " ('em', 'i'): 160,\n", - " ('mi', 'l'): 259,\n", - " ('il', 'y'): 203,\n", - " ('ly', '.'): 270,\n", - " ('.e', 'l'): 488,\n", - " ('li', 'z'): 81,\n", - " ('iz', 'a'): 93,\n", - " ('za', 'b'): 40,\n", - " ('be', 't'): 61,\n", - " ('et', 'h'): 114,\n", - " ('th', '.'): 178,\n", - " ('il', 'a'): 279,\n", - " ('av', 'e'): 166,\n", - " ('ve', 'r'): 160,\n", - " ('er', 'y'): 84,\n", - " ('ry', '.'): 201,\n", - " ('so', 'f'): 18,\n", - " ('of', 'i'): 19,\n", - " ('fi', 'a'): 20,\n", - " ('.c', 'a'): 628,\n", - " ('ca', 'm'): 92,\n", - " ('am', 'i'): 355,\n", - " ('.a', 'r'): 482,\n", - " ('ar', 'i'): 950,\n", - " ('ri', 'a'): 499,\n", - " ('.s', 'c'): 27,\n", - " ('sc', 'a'): 14,\n", - " ('ca', 'r'): 193,\n", - " ('rl', 'e'): 146,\n", - " ('le', 't'): 91,\n", - " ('et', 't'): 203,\n", - " ('tt', '.'): 102,\n", - " ('.v', 'i'): 161,\n", - " ('vi', 'c'): 26,\n", - " ('ic', 't'): 21,\n", - " ('ct', 'o'): 22,\n", - " ('to', 'r'): 110,\n", - " ('or', 'i'): 220,\n", - " ('.m', 'a'): 1453,\n", - " ('ma', 'd'): 136,\n", - " ('ad', 'i'): 190,\n", - " ('di', 's'): 42,\n", - " ('is', 'o'): 68,\n", - " ('so', 'n'): 341,\n", - " ('on', '.'): 1503,\n", - " ('.l', 'u'): 162,\n", - " ('lu', 'n'): 25,\n", - " ('un', 'a'): 61,\n", - " ('na', '.'): 1673,\n", - " ('.g', 'r'): 165,\n", - " ('gr', 'a'): 120,\n", - " ('ra', 'c'): 103,\n", - " ('ac', 'e'): 129,\n", - " ('ce', '.'): 234,\n", - " ('ch', 'l'): 22,\n", - " ('hl', 'o'): 24,\n", - " ('lo', 'e'): 17,\n", - " ('oe', '.'): 28,\n", - " ('.p', 'e'): 92,\n", - " ('pe', 'n'): 31,\n", - " ('en', 'e'): 151,\n", - " ('ne', 'l'): 145,\n", - " ('el', 'o'): 83,\n", - " ('lo', 'p'): 11,\n", - " ('op', 'e'): 33,\n", - " ('pe', '.'): 26,\n", - " ('.l', 'a'): 463,\n", - " ('la', 'y'): 234,\n", - " ('ay', 'l'): 483,\n", - " ('yl', 'a'): 381,\n", - " ('.r', 'i'): 201,\n", - " ('ri', 'l'): 62,\n", - " ('il', 'e'): 197,\n", - " ('le', 'y'): 443,\n", - " ('ey', '.'): 602,\n", - " ('.z', 'o'): 72,\n", - " ('zo', 'e'): 19,\n", - " ('oe', 'y'): 14,\n", - " ('.n', 'o'): 156,\n", - " ('no', 'r'): 101,\n", - " ('or', 'a'): 202,\n", - " ('ra', '.'): 627,\n", - " ('.l', 'i'): 269,\n", - " ('li', 'l'): 133,\n", - " ('el', 'e'): 219,\n", - " ('le', 'a'): 173,\n", - " ('ea', 'n'): 196,\n", - " ('an', 'o'): 98,\n", - " ('or', '.'): 148,\n", - " ('ha', 'n'): 469,\n", - " ('an', 'n'): 825,\n", - " ('nn', 'a'): 633,\n", - " ('na', 'h'): 185,\n", - " ('ah', '.'): 1714,\n", - " ('il', 'l'): 211,\n", - " ('ll', 'i'): 271,\n", - " ('ia', 'n'): 790,\n", - " ('an', '.'): 1509,\n", - " ('.a', 'd'): 366,\n", - " ('ad', 'd'): 94,\n", - " ('dd', 'i'): 51,\n", - " ('.a', 'u'): 152,\n", - " ('au', 'b'): 35,\n", - " ('ub', 'r'): 34,\n", - " ('br', 'e'): 189,\n", - " ('re', 'y'): 163,\n", - " ('li', 'e'): 285,\n", - " ('ie', '.'): 858,\n", - " ('.s', 't'): 135,\n", - " ('st', 'e'): 154,\n", - " ('te', 'l'): 63,\n", - " ('.n', 'a'): 470,\n", - " ('na', 't'): 101,\n", - " ('at', 'a'): 111,\n", - " ('ta', 'l'): 153,\n", - " ('al', 'i'): 575,\n", - " ('.l', 'e'): 366,\n", - " ('ea', 'h'): 92,\n", - " ('ha', 'z'): 42,\n", - " ('az', 'e'): 60,\n", - " ('ze', 'l'): 83,\n", - " ('el', '.'): 727,\n", - " ('vi', 'o'): 89,\n", - " ('io', 'l'): 21,\n", - " ('ol', 'e'): 83,\n", - " ('et', '.'): 106,\n", - " ('au', 'r'): 108,\n", - " ('ur', 'o'): 12,\n", - " ('ro', 'r'): 18,\n", - " ('.s', 'a'): 595,\n", - " ('sa', 'v'): 35,\n", - " ('va', 'n'): 237,\n", - " ('au', 'd'): 49,\n", - " ('ud', 'r'): 23,\n", - " ('dr', 'e'): 113,\n", - " ('.b', 'r'): 646,\n", - " ('br', 'o'): 69,\n", - " ('ro', 'o'): 38,\n", - " ('oo', 'k'): 27,\n", - " ('ok', 'l'): 11,\n", - " ('kl', 'y'): 41,\n", - " ('.b', 'e'): 253,\n", - " ('.c', 'l'): 68,\n", - " ('cl', 'a'): 60,\n", - " ('la', 'i'): 177,\n", - " ('ai', 'r'): 189,\n", - " ('ir', 'e'): 122,\n", - " ('re', '.'): 231,\n", - " ('.s', 'k'): 55,\n", - " ('sk', 'y'): 44,\n", - " ('ky', 'l'): 104,\n", - " ('la', 'r'): 153,\n", - " ('ar', '.'): 228,\n", - " ('lu', 'c'): 69,\n", - " ('uc', 'y'): 6,\n", - " ('cy', '.'): 38,\n", - " ('.p', 'a'): 175,\n", - " ('pa', 'i'): 31,\n", - " ('ai', 's'): 145,\n", - " ('is', 'l'): 63,\n", - " ('sl', 'e'): 138,\n", - " ('er', 'l'): 101,\n", - " ('rl', 'y'): 80,\n", - " ('.a', 'n'): 623,\n", - " ('ar', 'o'): 103,\n", - " ('ro', 'l'): 42,\n", - " ('li', 'n'): 505,\n", - " ('in', 'e'): 241,\n", - " ('ne', '.'): 607,\n", - " ('no', 'v'): 52,\n", - " ('ov', 'a'): 90,\n", - " ('.g', 'e'): 110,\n", - " ('ge', 'n'): 86,\n", - " ('ne', 's'): 93,\n", - " ('es', 'i'): 57,\n", - " ('si', 's'): 21,\n", - " ('is', '.'): 287,\n", - " ('il', 'i'): 180,\n", - " ('.k', 'e'): 601,\n", - " ('ke', 'n'): 252,\n", - " ('en', 'n'): 240,\n", - " ('nn', 'e'): 261,\n", - " ('ne', 'd'): 23,\n", - " ('ed', 'y'): 31,\n", - " ('dy', '.'): 128,\n", - " ('sa', 'm'): 136,\n", - " ('am', 'a'): 431,\n", - " ('ma', 'n'): 274,\n", - " ('an', 't'): 177,\n", - " ('nt', 'h'): 44,\n", - " ('th', 'a'): 168,\n", - " ('ha', '.'): 284,\n", - " ('ma', 'y'): 153,\n", - " ('ay', 'a'): 389,\n", - " ('ya', '.'): 716,\n", - " ('.w', 'i'): 91,\n", - " ('wi', 'l'): 56,\n", - " ('ll', 'o'): 58,\n", - " ('lo', 'w'): 27,\n", - " ('ow', '.'): 25,\n", - " ('.k', 'i'): 250,\n", - " ('ki', 'n'): 134,\n", - " ('in', 's'): 58,\n", - " ('ns', 'l'): 63,\n", - " ('na', 'o'): 10,\n", - " ('ao', 'm'): 6,\n", - " ('om', 'i'): 74,\n", - " ('mi', '.'): 124,\n", - " ('.a', 'a'): 207,\n", - " ('aa', 'l'): 46,\n", - " ('li', 'y'): 117,\n", - " ('iy', 'a'): 669,\n", - " ('ya', 'h'): 511,\n", - " ('le', 'n'): 383,\n", - " ('en', 'a'): 279,\n", - " ('sa', 'r'): 83,\n", - " ('ar', 'a'): 371,\n", - " ('ra', 'h'): 222,\n", - " ('an', 'a'): 804,\n", - " ('.a', 'l'): 632,\n", - " ('al', 'l'): 196,\n", - " ('li', 's'): 208,\n", - " ('.g', 'a'): 136,\n", - " ('ga', 'b'): 28,\n", - " ('ab', 'r'): 85,\n", - " ('br', 'i'): 254,\n", - " ('ri', 'e'): 394,\n", - " ('ie', 'l'): 395,\n", - " ('li', 'c'): 36,\n", - " ('ic', 'e'): 63,\n", - " ('ad', 'e'): 194,\n", - " ('de', 'l'): 217,\n", - " ('.c', 'o'): 255,\n", - " ('co', 'r'): 102,\n", - " ('.r', 'u'): 108,\n", - " ('ru', 'b'): 17,\n", - " ('ub', 'y'): 6,\n", - " ('by', '.'): 55,\n", - " ('ev', 'a'): 133,\n", - " ('.s', 'e'): 182,\n", - " ('se', 'r'): 67,\n", - " ('er', 'e'): 170,\n", - " ('re', 'n'): 281,\n", - " ('en', 'i'): 168,\n", - " ('ni', 't'): 58,\n", - " ('it', 'y'): 44,\n", - " ('ty', '.'): 88,\n", - " ('au', 't'): 13,\n", - " ('ut', 'u'): 6,\n", - " ('tu', 'm'): 13,\n", - " ('um', 'n'): 8,\n", - " ('mn', '.'): 4,\n", - " ('ha', 'i'): 119,\n", - " ('.g', 'i'): 128,\n", - " ('gi', 'a'): 50,\n", - " ('.v', 'a'): 121,\n", - " ('va', 'l'): 96,\n", - " ('al', 'e'): 601,\n", - " ('en', 't'): 95,\n", - " ('nt', 'i'): 48,\n", - " ('ti', 'n'): 142,\n", - " ('in', 'a'): 379,\n", - " ('sl', 'a'): 33,\n", - " ('.q', 'u'): 82,\n", - " ('qu', 'i'): 67,\n", - " ('ui', 'n'): 47,\n", - " ('in', 'n'): 103,\n", - " ('nn', '.'): 619,\n", - " ('.n', 'e'): 164,\n", - " ('ne', 'v'): 40,\n", - " ('va', 'e'): 16,\n", - " ('ae', 'h'): 13,\n", - " ('eh', '.'): 36,\n", - " ('.i', 'v'): 49,\n", - " ('iv', 'y'): 23,\n", - " ('vy', '.'): 28,\n", - " ('sa', 'd'): 32,\n", - " ('di', 'e'): 106,\n", - " ('.p', 'i'): 30,\n", - " ('pi', 'p'): 6,\n", - " ('ip', 'e'): 9,\n", - " ('.l', 'y'): 118,\n", - " ('ly', 'd'): 14,\n", - " ('yd', 'i'): 41,\n", - 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" ('ma', 'c'): 77,\n", - " ('ac', 'k'): 59,\n", - " ('ck', 'e'): 52,\n", - " ('en', 'z'): 68,\n", - " ('nz', 'i'): 46,\n", - " ('zi', 'e'): 78,\n", - " ('.r', 'e'): 268,\n", - " ('re', 'a'): 72,\n", - " ('ea', 'g'): 26,\n", - " ('ag', 'a'): 42,\n", - " ('ga', 'n'): 99,\n", - " ('ad', 'a'): 164,\n", - " ('da', 'l'): 203,\n", - " ('al', 'y'): 310,\n", - " ('yn', 'n'): 516,\n", - " ('re', 'e'): 205,\n", - " ('.j', 'a'): 1255,\n", - " ('ja', 'd'): 44,\n", - " ('de', '.'): 92,\n", - " ('ka', 't'): 93,\n", - " ('at', 'h'): 142,\n", - " ('th', 'e'): 106,\n", - " ('he', 'r'): 132,\n", - " ('er', 'i'): 268,\n", - " ('ri', 'n'): 234,\n", - " ('.r', 'a'): 472,\n", - " ('ra', 'e'): 127,\n", - " ('ae', 'l'): 287,\n", - " ('ma', 'r'): 776,\n", - " ('.a', 't'): 72,\n", - " ('he', 'n'): 106,\n", - " ('.x', 'i'): 28,\n", - " ('xi', 'm'): 21,\n", - " ('im', 'e'): 29,\n", - " ('me', 'n'): 60,\n", - " ('ar', 'y'): 195,\n", - " ('ry', 'a'): 118,\n", - " ('le', 'i'): 401,\n", - " ('ei', 'l'): 111,\n", - " ('ni', '.'): 625,\n", - 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" ('rn', 'a'): 35,\n", - " ('na', 'n'): 104,\n", - " ('ha', 't'): 20,\n", - " ('at', 't'): 73,\n", - " ('tt', 'i'): 38,\n", - " ('da', 'y'): 90,\n", - " ('ma', 'i'): 120,\n", - " ('is', 'i'): 35,\n", - " ('ay', 's'): 133,\n", - " ('ys', 'i'): 45,\n", - " ('ka', 'r'): 218,\n", - " ('ee', 'n'): 200,\n", - " ('ho', 'l'): 40,\n", - " ('.a', 'z'): 152,\n", - " ('az', 'a'): 123,\n", - " ...}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trigrams = {}\n", - "for w in words:\n", - " #chs = [''] + list(w) + ['']\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " trigram = (ch1+ch2, ch3)\n", - " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", - "\n", - "trigrams" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(('ah', '.'), 1714),\n", - " (('na', '.'), 1673),\n", - " (('an', '.'), 1509),\n", - 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" (('aa', 'l'), 46),\n", - " (('nz', 'i'), 46),\n", - " (('ee', 's'), 46),\n", - " (('la', 'u'), 46),\n", - " (('on', 'd'), 46),\n", - " (('ny', 'l'), 46),\n", - " (('se', 'a'), 46),\n", - " (('em', 'o'), 46),\n", - " (('.c', 'y'), 46),\n", - " (('si', 'm'), 46),\n", - " (('mi', 's'), 46),\n", - " (('av', 'y'), 46),\n", - " (('ba', '.'), 46),\n", - " (('um', 'a'), 46),\n", - " (('kh', 'y'), 46),\n", - " (('ru', 's'), 46),\n", - " (('ah', 'e'), 46),\n", - " (('rs', 'h'), 46),\n", - " (('ya', 'a'), 46),\n", - " (('pa', 'r'), 45),\n", - " (('he', 'a'), 45),\n", - " (('fe', 'r'), 45),\n", - " (('ys', 'i'), 45),\n", - " (('yd', 'a'), 45),\n", - " (('ma', 'x'), 45),\n", - " (('.g', 'u'), 45),\n", - " (('nv', 'i'), 45),\n", - " (('ze', '.'), 45),\n", - " (('ta', 'm'), 45),\n", - " (('at', 'o'), 45),\n", - " (('jo', 'n'), 45),\n", - " (('aj', '.'), 45),\n", - " (('av', '.'), 45),\n", - " (('rl', 'o'), 44),\n", - " (('sk', 'y'), 44),\n", - " (('nt', 'h'), 44),\n", - " (('it', 'y'), 44),\n", - " (('ja', 'd'), 44),\n", - " (('in', 'l'), 44),\n", - " (('ki', 'm'), 44),\n", - " (('os', 'i'), 44),\n", - " (('ge', '.'), 44),\n", - " (('fr', 'e'), 44),\n", - " (('kl', 'e'), 44),\n", - " (('it', 'z'), 44),\n", - " (('sa', 'h'), 44),\n", - " (('.c', 'i'), 44),\n", - " (('en', 'o'), 44),\n", - " (('de', 'a'), 44),\n", - " (('ay', 'r'), 44),\n", - " (('ev', 'o'), 44),\n", - " (('co', 'n'), 44),\n", - " (('sh', 'o'), 44),\n", - " (('hm', 'a'), 44),\n", - " (('ec', 'k'), 44),\n", - " (('ja', 'x'), 44),\n", - " (('ba', 's'), 44),\n", - " (('dh', 'a'), 44),\n", - " (('on', 'y'), 43),\n", - " (('si', 'd'), 43),\n", - " (('ye', '.'), 43),\n", - " (('ys', 't'), 43),\n", - " (('lo', 'u'), 43),\n", - " (('en', 'y'), 43),\n", - " (('yv', 'i'), 43),\n", - " (('sm', 'a'), 43),\n", - " (('uh', 'a'), 43),\n", - " (('ve', 'e'), 43),\n", - " (('mi', 't'), 43),\n", - " (('th', 'o'), 43),\n", - " (('ym', 'a'), 43),\n", - " (('di', 's'), 42),\n", - " ...]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted_trigrams = sorted(trigrams.items(), key = lambda item: -item[1])\n", - "sorted_trigrams" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "628\n" - ] - } - ], - "source": [ - "elements = []\n", - "\n", - "for trigram in sorted_trigrams:\n", - " elements.append(trigram[0][0])\n", - " elements.append(trigram[0][1])\n", - "\n", - "\n", - "elements = list(set(elements))\n", - "\n", - "print(len(elements))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "eltoi = {el: i for i, el in enumerate(elements)}\n", - "#print(eltoi)\n", - "break_int = eltoi[\".\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# int to string mapping\n", - "itoel = {i:el for i, el in enumerate(elements)}\n", - "#print(itoel)" - ] - }, - { - "cell_type": "code", - 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"as .: 150 217\n", - ".z y: 431 224\n", - "zy r: 225 596\n", - "yr a: 445 91\n", - "ra n: 141 613\n", - "an .: 330 217\n", - ".z y: 431 224\n", - "zy r: 225 596\n", - "yr i: 445 512\n", - "ri e: 413 348\n", - "ie .: 376 217\n", - ".z y: 431 224\n", - "zy r: 225 596\n", - "yr o: 445 325\n", - "ro n: 18 613\n", - "on .: 256 217\n", - ".z z: 431 565\n", - "zz y: 110 224\n", - "zy z: 225 565\n", - "yz x: 155 405\n", - "zx .: 0 217\n", - "tensor([415, 263, 343, ..., 225, 155, 0])\n", - "tensor([ 15, 15, 91, ..., 565, 405, 217])\n" - ] - } - ], - "source": [ - "# First let's create a training set of bigrams (x, y)\n", - "\n", - "xs, ys = [], []\n", - "\n", - "for w in words:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " el1 = ch1+ch2\n", - " el2 = ch3\n", - "\n", - " ix1 = eltoi[el1]\n", - " ix2 = eltoi[el2]\n", - " xs.append(ix1)\n", - " ys.append(ix2)\n", - " print(f\"{el1} {el2}: {ix1} {ix2}\")\n", - "\n", - "xs = torch.tensor(xs)\n", - "ys = torch.tensor(ys)\n", - "\n", - "print(xs)\n", - "print(ys)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples: 196113\n" - ] - } - ], - "source": [ - "# Create the dataset\n", - "xs, ys = [], []\n", - "\n", - "for w in words:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", - " el1 = ch1+ch2\n", - " el2 = ch3\n", - "\n", - " ix1 = eltoi[el1]\n", - " ix2 = eltoi[el2]\n", - " xs.append(ix1)\n", - " ys.append(ix2)\n", - " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", - "\n", - "xs = torch.tensor(xs)\n", - "ys = torch.tensor(ys)\n", - "num = xs.nelement()\n", - "\n", - "print(\"Number of examples: \", num)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "W = torch.randn((628, 628), generator=g, requires_grad=True) # We created 27 neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "device(type='cuda')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "device" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 999: 100%|██████████| 1000/1000 [14:30<00:00, 1.15Epoch/s, loss=2.31, time=873]\n" - ] - } - ], - "source": [ - "# gradient descent in a loop -> training\n", - "\n", - "lr = 150\n", - "alpha = 0.01\n", - "\n", - "t_start = time.time()\n", - "\n", - "# Gpu accelerated now\n", - "#W.to(device)\n", - "xs.to(device)\n", - "ys.to(device)\n", - "\n", - "with tqdm(range(500), unit=\"Epoch\") as tepoch:\n", - " for epoch in tepoch:\n", - " tepoch.set_description(f\"Epoch {epoch}\")\n", - " \n", - "\n", - " # Forwards pass\n", - " xenc = F.one_hot(xs, num_classes=628).float().to(device)\n", - "\n", - " logits = xenc @ W.to(device) # log-counts\n", - " counts = logits.exp() # counts, equivalent to N\n", - " probs = (counts / counts.sum(1, keepdim=True)) \n", - " \n", - " # print(probs.shape)\n", - " # print(ys.shape)\n", - "\n", - " # # regularised loss\n", - " loss = -probs[torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", - "\n", - " # Backward pass\n", - " W.grad = None # set gradient to zero\n", - " loss.backward()\n", - "\n", - " # update\n", - " W.data += -lr * W.grad # gradient descent\n", - "\n", - " tepoch.set_postfix(loss=loss.item(), time=time.time() - t_start)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "xlekeavlcg.kabtudirb.kecttzzwgy.\n", - "\n", - "ucaanijizbmdwmneerxdcujykshcdbssriwwdvidrenthujgwil.\n", - "\n", - "vhwnetvdldqanvibayeb.\n", - "\n", - "spwbrotwovonkylckigzrqiyuwaystmkxbgvlmaeanoelezemnuuebeux.\n", - "\n", - "ifezdhwwuymluwrsthwki.\n", - "\n" - ] - } - ], - "source": [ - "# finally, sample from the 'neural net' model\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "\n", - "for i in range(5):\n", - " \n", - " out = []\n", - " ix = 0\n", - " while True:\n", - " \n", - " # ----------\n", - " # BEFORE:\n", - " #p = P[ix]\n", - " # ----------\n", - " # NOW:\n", - " xenc = F.one_hot(torch.tensor([ix]), num_classes=628).float()\n", - " logits = xenc @ W # predict log-counts\n", - " counts = logits.exp() # counts, equivalent to N\n", - " p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n", - " # ----------\n", - " \n", - " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", - " out.append(itoel[ix])\n", - " if ix == break_int:\n", - " break\n", - " print(''.join(out))\n", - " print()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/my_code/makemore/1_makemore_bigram_torch.ipynb b/my_code/makemore/1_makemore_bigram_torch.ipynb deleted file mode 100644 index 64ac3720..00000000 --- a/my_code/makemore/1_makemore_bigram_torch.ipynb +++ /dev/null @@ -1,2653 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['emma',\n", - " 'olivia',\n", - " 'ava',\n", - " 'isabella',\n", - " 'sophia',\n", - " 'charlotte',\n", - " 'mia',\n", - " 'amelia',\n", - " 'harper',\n", - " 'evelyn']" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with open(\"names.txt\", \"r\") as f:\n", - " words = f.read().splitlines()\n", - "\n", - "words[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total names: 32033\n" - ] - } - ], - "source": [ - "# total words\n", - "print(\"Total names: \", len(words))" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Minimum name length: 2\n", - "Maximum name length: 15\n" - ] - } - ], - "source": [ - "print(\"Minimum name length: \", min(len(w) for w in words))\n", - "print(\"Maximum name length: \", max(len(w) for w in words))" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{('.', 'e'): 1531,\n", - " ('e', 'm'): 769,\n", - " ('m', 'm'): 168,\n", - " ('m', 'a'): 2590,\n", - " ('a', '.'): 6640,\n", - " ('.', 'o'): 394,\n", - " ('o', 'l'): 619,\n", - " ('l', 'i'): 2480,\n", - " ('i', 'v'): 269,\n", - " ('v', 'i'): 911,\n", - " ('i', 'a'): 2445,\n", - " ('.', 'a'): 4410,\n", - " ('a', 'v'): 834,\n", - " ('v', 'a'): 642,\n", - " ('.', 'i'): 591,\n", - " ('i', 's'): 1316,\n", - " ('s', 'a'): 1201,\n", - " ('a', 'b'): 541,\n", - " ('b', 'e'): 655,\n", - " ('e', 'l'): 3248,\n", - " ('l', 'l'): 1345,\n", - " ('l', 'a'): 2623,\n", - " ('.', 's'): 2055,\n", - " 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bigrams[bigram] = bigrams.get(bigram, 0) + 1\n", - "\n", - "bigrams" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(('n', '.'), 6763),\n", - " (('a', '.'), 6640),\n", - " (('a', 'n'), 5438),\n", - " (('.', 'a'), 4410),\n", - " (('e', '.'), 3983),\n", - " (('a', 'r'), 3264),\n", - " (('e', 'l'), 3248),\n", - " (('r', 'i'), 3033),\n", - " (('n', 'a'), 2977),\n", - " (('.', 'k'), 2963),\n", - " (('l', 'e'), 2921),\n", - " (('e', 'n'), 2675),\n", - " (('l', 'a'), 2623),\n", - " (('m', 'a'), 2590),\n", - " (('.', 'm'), 2538),\n", - " (('a', 'l'), 2528),\n", - " (('i', '.'), 2489),\n", - " (('l', 'i'), 2480),\n", - " (('i', 'a'), 2445),\n", - " (('.', 'j'), 2422),\n", - " (('o', 'n'), 2411),\n", - " (('h', '.'), 2409),\n", - " (('r', 'a'), 2356),\n", - " (('a', 'h'), 2332),\n", - " (('h', 'a'), 2244),\n", - " (('y', 'a'), 2143),\n", - " (('i', 'n'), 2126),\n", - " (('.', 's'), 2055),\n", - " (('a', 'y'), 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2),\n", - " (('f', 'z'), 2),\n", - " (('h', 'g'), 2),\n", - " (('w', 'w'), 2),\n", - " (('k', 'j'), 2),\n", - " (('j', 'k'), 2),\n", - " (('w', 'm'), 2),\n", - " (('z', 'c'), 2),\n", - " (('z', 'v'), 2),\n", - " (('w', 'f'), 2),\n", - " (('q', 'm'), 2),\n", - " (('k', 'z'), 2),\n", - " (('j', 'j'), 2),\n", - " (('z', 'p'), 2),\n", - " (('j', 't'), 2),\n", - " (('k', 'b'), 2),\n", - " (('m', 'w'), 2),\n", - " (('h', 'f'), 2),\n", - " (('c', 'g'), 2),\n", - " (('t', 'f'), 2),\n", - " (('h', 'c'), 2),\n", - " (('q', 'o'), 2),\n", - " (('k', 'd'), 2),\n", - " (('k', 'v'), 2),\n", - " (('s', 'g'), 2),\n", - " (('z', 'd'), 2),\n", - " (('q', 'r'), 1),\n", - " (('d', 'z'), 1),\n", - " (('p', 'j'), 1),\n", - " (('q', 'l'), 1),\n", - " (('p', 'f'), 1),\n", - " (('q', 'e'), 1),\n", - " (('b', 'c'), 1),\n", - " (('c', 'd'), 1),\n", - " (('m', 'f'), 1),\n", - " (('p', 'n'), 1),\n", - " (('w', 'b'), 1),\n", - " (('p', 'c'), 1),\n", - " (('h', 'p'), 1),\n", - " (('f', 'h'), 1),\n", - " (('b', 'j'), 1),\n", - " (('f', 'g'), 1),\n", - " (('z', 'g'), 1),\n", - " (('c', 'p'), 1),\n", - " (('p', 'k'), 1),\n", - " (('p', 'm'), 1),\n", - " (('x', 'n'), 1),\n", - " (('s', 'q'), 1),\n", - " (('k', 'f'), 1),\n", - " (('m', 'k'), 1),\n", - " (('x', 'h'), 1),\n", - " (('g', 'f'), 1),\n", - " (('v', 'b'), 1),\n", - " (('j', 'p'), 1),\n", - " (('g', 'z'), 1),\n", - " (('v', 'd'), 1),\n", - " (('d', 'b'), 1),\n", - " (('v', 'h'), 1),\n", - " (('h', 'h'), 1),\n", - " (('g', 'v'), 1),\n", - " (('d', 'q'), 1),\n", - " (('x', 'b'), 1),\n", - " (('w', 'z'), 1),\n", - " (('h', 'q'), 1),\n", - " (('j', 'b'), 1),\n", - " (('x', 'm'), 1),\n", - " (('w', 'g'), 1),\n", - " (('t', 'b'), 1),\n", - " (('z', 'x'), 1)]" - ] - }, - "execution_count": 157, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(bigrams.items(), key = lambda item: -item[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [], - "source": [ - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [], - "source": [ - "N = torch.zeros((27, 27), dtype=torch.int32)" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n" - ] - } - ], - "source": [ - "chars = sorted(list(set(\"\".join(words))))\n", - "print(chars)" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}\n" - ] - } - ], - "source": [ - "# string to int mapping\n", - "stoi = {}\n", - "for i, char in enumerate(chars):\n", - " stoi[char] = i+1\n", - "\n", - "print(stoi)" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n" - ] - } - ], - "source": [ - "stoi = {char:i+1 for i, char in enumerate(chars)}\n", - "stoi['.'] = 0\n", - "print(stoi)" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" - ] - } - ], - "source": [ - "itos = {i+1:char for i, char in enumerate(chars)}\n", - "itos[0] = \".\"\n", - "print(itos)" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# plt.figure(figsize=(16,16))\n", - "# plt.imshow(N, cmap='Blues')\n", - "# for i in range(27):\n", - "# for j in range(27):\n", - "# chstr = itos[i] + itos[j]\n", - "# plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n", - "# plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n", - "# plt.axis('off');" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's fill bigrams now into N\n", - "for w in words:\n", - " #chs = [''] + list(w) + ['']\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", - " itx1 = stoi[ch1]\n", - " itx2 = stoi[ch2]\n", - " N[itx1, itx2] += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(16,16))\n", - "plt.imshow(N, cmap='Blues')\n", - "for i in range(27):\n", - " for j in range(27):\n", - " chstr = itos[i] + itos[j]\n", - " plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n", - " plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n", - "plt.axis('off');" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([ 0, 4410, 1306, 1542, 1690, 1531, 417, 669, 874, 591, 2422, 2963,\n", - " 1572, 2538, 1146, 394, 515, 92, 1639, 2055, 1308, 78, 376, 307,\n", - " 134, 535, 929], dtype=torch.int32)" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now lets'make probability distributions row wise\n", - "#sum(N[0] / sum(N[0]))\n", - "N[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [], - "source": [ - "#P = N.float()\n", - "#P.sum(1).shape # we need keepdim=True otherwise calculations areincorrect" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [], - "source": [ - "P = (N+1).float()\n", - "P /= P.sum(1, keepdim=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mor.\n", - "axx.\n", - "minaymoryles.\n", - "kondlaisah.\n", - "anchshizarie.\n" - ] - } - ], - "source": [ - "g = torch.Generator().manual_seed(2147483647)\n", - "\n", - "for i in range(5):\n", - " out = []\n", - " ix = 0\n", - " while True:\n", - " p = P[ix]\n", - " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", - " out.append(itos[ix]) \n", - " if ix==0: # we get a \".\"\n", - " break\n", - " \n", - " print(\"\".join(out))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### We want to make a metric to measure (Quality) how good the model is. i.e we want a loss" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".e: 0.0478\n", - "em: 0.0377\n", - "mm: 0.0253\n", - "ma: 0.3885\n", - "a.: 0.1958\n", - ".o: 0.0123\n", - "ol: 0.0779\n", - "li: 0.1774\n", - "iv: 0.0152\n", - "vi: 0.3508\n", - "ia: 0.1380\n", - "a.: 0.1958\n", - ".a: 0.1376\n", - "av: 0.0246\n", - "va: 0.2473\n", - "a.: 0.1958\n" - ] - } - ], - "source": [ - "# Let's fill bigrams now into N\n", - "for w in words[:3]:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", - " print(f\"{ch1}{ch2}: {prob:.4f}\")\n", - "\n", - "# There are 27 characters. That means during initialization 1/27 should be probability of each.\n", - "# If the probability is higger than that for some that means the model has learned something\n", - "# Which it has actually\n", - "# These rpobability should be near to 1 atleast on the training set meaning the model is confident." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Likelihood\n", - "\n", - "The product of probability distributions should be high\n", - "\n", - "Log probability; which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", - "\n", - "Loss function has the semantic that lower is better but with log-liklihood lower is worse so we use **negative log likelihood**\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GOAL: maximize likelihood of the data w.r.t. model parameters (statistical modeling)\n", - "\n", - "equivalent to maximizing the log likelihood (because log is monotonic)\n", - "\n", - "equivalent to minimizing the negative log likelihood\n", - "\n", - "equivalent to minimizing the average negative log likelihood" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log_likelihood=tensor(-559951.5625)\n", - "nll/(n+1)=tensor(2.4543)\n" - ] - } - ], - "source": [ - "log_likelihood = 0.0\n", - "# Let's fill bigrams now into N\n", - "n = 0\n", - "for w in words:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", - " logprob = torch.log(prob) # log probability # which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", - " log_likelihood += logprob\n", - " n += 1\n", - " #print(f\"{ch1}{ch2}: {prob:.4f} {logprob: .4f}\")\n", - "\n", - "print(f\"{log_likelihood=}\") # Maximum it can go is 0 when all probs are 1\n", - "nll = -log_likelihood\n", - "print(f\"{nll/(n+1)=}\") # We want average # the lower it is the better we are" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can estimate probability for any word (name)\n", - "\n", - "But if we check probability for a name like **ibadqq** the model gives a loss of inf which means the probability is exactly zero.\n", - "This is highly undesirable. q followed by q occurs 0 times as you can see from the initial graph.\n", - "\n", - "To tackle this we can do something like model smoothning. We will add 1 to all occurances of bigrams. It will eliminate this\n", - "problem. We could even 1000 to the counts. Adding a larger number will make the model more smooth. But here we will just add 1." - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log_likelihood=tensor(-559951.5625)\n", - "nll/(n+1)=tensor(2.4543)\n" - ] - } - ], - "source": [ - "log_likelihood = 0.0\n", - "# Let's fill bigrams now into N\n", - "n = 0\n", - "for w in words:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", - " logprob = torch.log(prob) # log probability # which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", - " log_likelihood += logprob\n", - " n += 1\n", - " #print(f\"{ch1}{ch2}: {prob:.4f} {logprob: .4f}\")\n", - "\n", - "print(f\"{log_likelihood=}\") # Maximum it can go is 0 when all probs are 1\n", - "nll = -log_likelihood\n", - "print(f\"{nll/(n+1)=}\") # We want average # the lower it is the better we are" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we will do try to reach the same place but with a different approach; using neural net" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".e: 0 5\n", - "em: 5 13\n", - "mm: 13 13\n", - "ma: 13 1\n", - "a.: 1 0\n", - "tensor([ 0, 5, 13, 13, 1])\n", - "tensor([ 5, 13, 13, 1, 0])\n" - ] - } - ], - "source": [ - "# First let's create a training set of bigrams (x, y)\n", - "\n", - "xs, ys = [], []\n", - "\n", - "for w in words[:1]:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " xs.append(ix1)\n", - " ys.append(ix2)\n", - " print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", - "\n", - "xs = torch.tensor(xs)\n", - "ys = torch.tensor(ys)\n", - "\n", - "print(xs)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### one hot encoding" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([5, 27])\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import torch.nn.functional as F\n", - "xenc = F.one_hot(xs, num_classes=27).float() # we dont want an int we want a float\n", - "print(xenc.shape)\n", - "plt.imshow(xenc, cmap='Blues')" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.float32" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xenc.dtype" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's construct first neuron" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[-7.6148e-01, -3.1424e-01, 1.9481e-02, -2.2533e-01, 4.5341e-01,\n", - " 4.2244e-01, -6.9604e-01, -1.6960e+00, -3.3331e-01, 3.7474e-01,\n", - " -8.0611e-02, -1.1231e+00, -4.4109e-01, -1.2095e+00, 4.1020e-01,\n", - " -1.5015e+00, -8.7129e-01, 2.9803e-01, -2.3551e+00, 3.4084e-04,\n", - " -9.6937e-01, 6.0936e-01, -8.3752e-01, 3.3975e-01, 1.5214e+00,\n", - " -8.9153e-01, 1.4327e+00],\n", - " [ 9.3261e-01, -9.2786e-01, -6.2602e-01, -9.7203e-01, 7.9021e-01,\n", - " 4.0905e-01, -9.1781e-01, 1.3454e+00, -4.0126e-01, -6.0294e-01,\n", - " -5.3656e-01, 8.8367e-01, 5.4779e-01, -3.5715e-01, 1.8984e-01,\n", - " 4.1010e-01, 1.6740e+00, 1.7788e+00, -5.2731e-01, 1.9860e+00,\n", - " 2.4396e+00, -1.4672e+00, 1.3123e+00, -6.3350e-01, 3.4797e-01,\n", - " -8.2656e-01, 4.2729e-02],\n", - " [ 2.1002e-01, -8.5984e-01, -9.2525e-01, -8.6639e-01, -1.2768e+00,\n", - " 3.8859e-01, 8.8408e-01, 4.0335e-01, 4.6471e-01, -6.9420e-01,\n", - " -1.5782e+00, -1.1070e-01, 3.6890e-02, 7.1488e-01, 4.3240e-01,\n", - " -4.2933e-01, 1.3047e+00, -9.7873e-01, 9.5837e-02, 6.6297e-01,\n", - " 8.1560e-01, 1.0019e+00, 5.6406e-01, -9.2049e-01, 3.8910e-01,\n", - " 1.3193e+00, 1.3622e+00],\n", - " [ 2.1002e-01, -8.5984e-01, -9.2525e-01, -8.6639e-01, -1.2768e+00,\n", - " 3.8859e-01, 8.8408e-01, 4.0335e-01, 4.6471e-01, -6.9420e-01,\n", - " -1.5782e+00, -1.1070e-01, 3.6890e-02, 7.1488e-01, 4.3240e-01,\n", - " -4.2933e-01, 1.3047e+00, -9.7873e-01, 9.5837e-02, 6.6297e-01,\n", - " 8.1560e-01, 1.0019e+00, 5.6406e-01, -9.2049e-01, 3.8910e-01,\n", - " 1.3193e+00, 1.3622e+00],\n", - " [ 1.3709e+00, -1.9400e+00, -2.4378e-01, 3.2082e-01, -1.1422e+00,\n", - " -8.0290e-01, 1.4555e+00, 7.6867e-01, -4.1894e-01, 1.0218e+00,\n", - " -1.0205e+00, -5.4941e-03, -7.9474e-02, -5.8220e-01, 1.2662e+00,\n", - " 7.7145e-01, -1.6896e-01, 3.9349e-01, 5.0482e-01, -2.5368e+00,\n", - " 3.9033e-01, 8.8935e-01, 5.9883e-01, -3.6300e-01, -1.2209e-01,\n", - " 6.1462e-01, 4.4726e-01]])" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W = torch.randn((27, 27)) # We created 27 neurons \n", - "# (5, 27) @ (27, 27) = (5, 27) # all 5 inputs will be trained for all 27 possible outputs this way\n", - "xenc @ W\n", - "\n", - "# W will be our only NN layer, the simplest dumbest type lol.\n", - "\n", - "# For every input we want to generate a probability distibution over all possible outputs (27 here)\n", - "\n", - "## We want interpret these results now somehow.\n", - "# We dont want counts directly because they are integers. What we will do is we will get log of counts from neural net.\n", - "# We will then exponentiate them to get counts back (somewhat)\n", - "\n", - "# for exp(0) = 1\n", - "# for exp(>0) > 1\n", - "# for exp(<0) < 1 \n", - "\n", - "# these exponentiated values will be our counts they cant be below 0." - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.4670, 0.7303, 1.0197, 0.7983, 1.5737, 1.5257, 0.4986, 0.1834,\n", - " 0.7165, 1.4546, 0.9226, 0.3253, 0.6433, 0.2983, 1.5071, 0.2228,\n", - " 0.4184, 1.3472, 0.0949, 1.0003, 0.3793, 1.8393, 0.4328, 1.4046,\n", - " 4.5785, 0.4100, 4.1900],\n", - " [ 2.5411, 0.3954, 0.5347, 0.3783, 2.2039, 1.5054, 0.3994, 3.8397,\n", - " 0.6695, 0.5472, 0.5848, 2.4198, 1.7294, 0.6997, 1.2091, 1.5070,\n", - " 5.3336, 5.9228, 0.5902, 7.2860, 11.4683, 0.2306, 3.7148, 0.5307,\n", - " 1.4162, 0.4376, 1.0437],\n", - " [ 1.2337, 0.4232, 0.3964, 0.4205, 0.2789, 1.4749, 2.4208, 1.4968,\n", - " 1.5916, 0.4995, 0.2063, 0.8952, 1.0376, 2.0439, 1.5409, 0.6509,\n", - " 3.6867, 0.3758, 1.1006, 1.9405, 2.2605, 2.7235, 1.7578, 0.3983,\n", - " 1.4757, 3.7407, 3.9049],\n", - " [ 1.2337, 0.4232, 0.3964, 0.4205, 0.2789, 1.4749, 2.4208, 1.4968,\n", - " 1.5916, 0.4995, 0.2063, 0.8952, 1.0376, 2.0439, 1.5409, 0.6509,\n", - " 3.6867, 0.3758, 1.1006, 1.9405, 2.2605, 2.7235, 1.7578, 0.3983,\n", - " 1.4757, 3.7407, 3.9049],\n", - " [ 3.9389, 0.1437, 0.7837, 1.3783, 0.3191, 0.4480, 4.2868, 2.1569,\n", - " 0.6577, 2.7781, 0.3604, 0.9945, 0.9236, 0.5587, 3.5472, 2.1629,\n", - " 0.8445, 1.4821, 1.6567, 0.0791, 1.4775, 2.4335, 1.8200, 0.6956,\n", - " 0.8851, 1.8490, 1.5640]])" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(xenc @ W).exp()" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(24)" - ] - }, - "execution_count": 179, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logits = xenc @ W # log-counts\n", - "counts = logits.exp() # counts\n", - "probs = counts / counts.sum(1, keepdim=True) # probabilities # normalizse the rows of counts: sum of every row is 1\n", - "probs[0].argmax()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summarised" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([ 0, 5, 13, 13, 1])\n", - "tensor([ 5, 13, 13, 1, 0])\n" - ] - } - ], - "source": [ - "print(xs)\n", - "print(ys)" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [], - "source": [ - "xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", - "logits = xenc @ W # log-counts\n", - "counts = logits.exp() # counts\n", - "probs = counts / counts.sum(1, keepdim=True) # probabilities # normalizse the rows of counts: sum of every row is 1\n", - "# these two previous lines are the same as softmax\n" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([5, 27])" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "probs.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [], - "source": [ - "# the forward pass is back-propagateable" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--------\n", - "bigram example 1: .e (indexes 0,5)\n", - "input to the neural net: 0\n", - "output probabilities from the neural net: tensor([0.0607, 0.0100, 0.0123, 0.0042, 0.0168, 0.0123, 0.0027, 0.0232, 0.0137,\n", - " 0.0313, 0.0079, 0.0278, 0.0091, 0.0082, 0.0500, 0.2378, 0.0603, 0.0025,\n", - " 0.0249, 0.0055, 0.0339, 0.0109, 0.0029, 0.0198, 0.0118, 0.1537, 0.1459],\n", - " grad_fn=)\n", - "label (actual next character): 5\n", - "probability assigned by the net to the the correct character: 0.01228625513613224\n", - "log likelihood: -4.399273872375488\n", - "negative log likelihood: 4.399273872375488\n", - "--------\n", - "bigram example 2: em (indexes 5,13)\n", - "input to the neural net: 5\n", - "output probabilities from the neural net: tensor([0.0290, 0.0796, 0.0248, 0.0521, 0.1989, 0.0289, 0.0094, 0.0335, 0.0097,\n", - " 0.0301, 0.0702, 0.0228, 0.0115, 0.0181, 0.0108, 0.0315, 0.0291, 0.0045,\n", - " 0.0916, 0.0215, 0.0486, 0.0300, 0.0501, 0.0027, 0.0118, 0.0022, 0.0472],\n", - " grad_fn=)\n", - "label (actual next character): 13\n", - "probability assigned by the net to the the correct character: 0.018050700426101685\n", - "log likelihood: -4.014570713043213\n", - "negative log likelihood: 4.014570713043213\n", - "--------\n", - "bigram example 3: mm (indexes 13,13)\n", - "input to the neural net: 13\n", - "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n", - " 0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n", - " 0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126],\n", - " grad_fn=)\n", - "label (actual next character): 13\n", - "probability assigned by the net to the the correct character: 0.026691533625125885\n", - "log likelihood: -3.623408794403076\n", - "negative log likelihood: 3.623408794403076\n", - "--------\n", - "bigram example 4: ma (indexes 13,1)\n", - "input to the neural net: 13\n", - "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n", - " 0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n", - " 0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126],\n", - " grad_fn=)\n", - "label (actual next character): 1\n", - "probability assigned by the net to the the correct character: 0.07367686182260513\n", - "log likelihood: -2.6080665588378906\n", - "negative log likelihood: 2.6080665588378906\n", - "--------\n", - "bigram example 5: a. (indexes 1,0)\n", - "input to the neural net: 1\n", - "output probabilities from the neural net: tensor([0.0150, 0.0086, 0.0396, 0.0100, 0.0606, 0.0308, 0.1084, 0.0131, 0.0125,\n", - " 0.0048, 0.1024, 0.0086, 0.0988, 0.0112, 0.0232, 0.0207, 0.0408, 0.0078,\n", - " 0.0899, 0.0531, 0.0463, 0.0309, 0.0051, 0.0329, 0.0654, 0.0503, 0.0091],\n", - " grad_fn=)\n", - "label (actual next character): 0\n", - "probability assigned by the net to the the correct character: 0.014977526850998402\n", - "log likelihood: -4.201204299926758\n", - "negative log likelihood: 4.201204299926758\n", - "=========\n", - "average negative log likelihood, i.e. loss = 3.7693049907684326\n" - ] - } - ], - "source": [ - "nlls = torch.zeros(5)\n", - "for i in range(5):\n", - " # i-th bigram:\n", - " x = xs[i].item() # input character index\n", - " y = ys[i].item() # label character index\n", - " print('--------')\n", - " print(f'bigram example {i+1}: {itos[x]}{itos[y]} (indexes {x},{y})')\n", - " print('input to the neural net:', x)\n", - " print('output probabilities from the neural net:', probs[i])\n", - " print('label (actual next character):', y)\n", - " p = probs[i, y]\n", - " print('probability assigned by the net to the the correct character:', p.item())\n", - " logp = torch.log(p)\n", - " print('log likelihood:', logp.item())\n", - " nll = -logp\n", - " print('negative log likelihood:', nll.item())\n", - " nlls[i] = nll\n", - "\n", - "print('=========')\n", - "print('average negative log likelihood, i.e. loss =', nlls.mean().item())" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], - "source": [ - "# Forwards pass\n", - "xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", - "logits = xenc @ W # log-counts\n", - "counts = logits.exp() # counts, equivalent to N\n", - "probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", - "loss = -probs[torch.arange(5), ys].log().mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [], - "source": [ - "# Backwards pass\n", - "W.grad = None # set the gradient to zero\n", - "loss.backward() # compute the gradient" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.0121, 0.0020, 0.0025, 0.0008, 0.0034, -0.1975, 0.0005, 0.0046,\n", - " 0.0027, 0.0063, 0.0016, 0.0056, 0.0018, 0.0016, 0.0100, 0.0476,\n", - " 0.0121, 0.0005, 0.0050, 0.0011, 0.0068, 0.0022, 0.0006, 0.0040,\n", - " 0.0024, 0.0307, 0.0292],\n", - " [-0.1970, 0.0017, 0.0079, 0.0020, 0.0121, 0.0062, 0.0217, 0.0026,\n", - " 0.0025, 0.0010, 0.0205, 0.0017, 0.0198, 0.0022, 0.0046, 0.0041,\n", - " 0.0082, 0.0016, 0.0180, 0.0106, 0.0093, 0.0062, 0.0010, 0.0066,\n", - " 0.0131, 0.0101, 0.0018],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000],\n", - " [ 0.0058, 0.0159, 0.0050, 0.0104, 0.0398, 0.0058, 0.0019, 0.0067,\n", - " 0.0019, 0.0060, 0.0140, 0.0046, 0.0023, -0.1964, 0.0022, 0.0063,\n", - " 0.0058, 0.0009, 0.0183, 0.0043, 0.0097, 0.0060, 0.0100, 0.0005,\n", - " 0.0024, 0.0004, 0.0094],\n", - " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - 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" 0.0000, 0.0000, 0.0000]])" - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W.grad\n", - "\n", - "# W[0, 0] is positive. It means the loss will increse if this increases.\n", - "# We can use this gradient information to update the weights" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss before update: 3.7693049907684326\n", - "loss after update: 3.7693049907684326\n" - ] - } - ], - "source": [ - "W.data += -0.1 * W.grad\n", - "\n", - "# Loss should descrease after this update\n", - "\n", - "print('loss before update:', loss.item())\n", - "\n", - "loss = -probs[torch.arange(5), ys].log().mean()\n", - "\n", - "print('loss after update:', loss.item())\n", - "# It is slightly lower" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's train properly now" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples: 228146\n" - ] - } - ], - "source": [ - "# Create the dataset\n", - "xs, ys = [], []\n", - "\n", - "for w in words:\n", - " chs = ['.'] + list(w) + ['.']\n", - " for ch1, ch2 in zip(chs, chs[1:]):\n", - " ix1 = stoi[ch1]\n", - " ix2 = stoi[ch2]\n", - " xs.append(ix1)\n", - " ys.append(ix2)\n", - " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", - "\n", - "xs = torch.tensor(xs)\n", - "ys = torch.tensor(ys)\n", - "num = xs.nelement()\n", - "\n", - "print(\"Number of examples: \", num)" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [], - "source": [ - "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", - "W = torch.zeros((27, 27), requires_grad=True) # We created 27 neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss: 3.295837640762329\n", - "loss: 2.52099871635437\n", - "loss: 2.4878063201904297\n", - "loss: 2.4761619567871094\n", - "loss: 2.4702982902526855\n", - "loss: 2.466809034347534\n", - "loss: 2.4645133018493652\n", - "loss: 2.4628946781158447\n", - "loss: 2.4616944789886475\n", - "loss: 2.4607694149017334\n", - "loss: 2.4600346088409424\n", - "loss: 2.459437370300293\n" - ] - } - ], - "source": [ - "# gradient descent in a loop -> training\n", - "\n", - "lr = 50\n", - "\n", - "for k in range(300):\n", - " # Forwards pass\n", - " xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", - " logits = xenc @ W # log-counts\n", - " counts = logits.exp() # counts, equivalent to N\n", - " probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", - " loss = -probs[torch.arange(num), ys].log().mean()\n", - " if k % 25 == 0:\n", - " print('loss:', loss.item())\n", - "\n", - " # Backward pass\n", - " W.grad = None # set gradient to zero\n", - " loss.backward()\n", - "\n", - " # update\n", - " W.data += -lr * W.grad # gradient descent\n", - "\n", - "# The loss is same as calculated with probability distrubuted\n", - "# We did the same thing but it is way more flexible\n", - "# static approach is not scalable but NN one is" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Regularisation" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [], - "source": [ - "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", - "W = torch.zeros((27, 27), requires_grad=True) # We created 27 neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss: 3.295837640762329\n", - "loss: 2.529900074005127\n", - "loss: 2.500406265258789\n", - "loss: 2.491044044494629\n", - "loss: 2.4867959022521973\n", - "loss: 2.4845311641693115\n", - "loss: 2.4832026958465576\n", - "loss: 2.482370615005493\n", - "loss: 2.4818227291107178\n", - "loss: 2.4814486503601074\n", - "loss: 2.4811856746673584\n", - "loss: 2.4809961318969727\n" - ] - } - ], - "source": [ - "# gradient descent in a loop -> training\n", - "\n", - "lr = 50\n", - "alpha = 0.01\n", - "\n", - "for k in range(300):\n", - " # Forwards pass\n", - " xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", - " logits = xenc @ W # log-counts\n", - " counts = logits.exp() # counts, equivalent to N\n", - " probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", - " \n", - " # regularised loss\n", - " loss = -probs[torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", - " if k % 25 == 0:\n", - " print('loss:', loss.item())\n", - "\n", - " # Backward pass\n", - " W.grad = None # set gradient to zero\n", - " loss.backward()\n", - "\n", - " # update\n", - " W.data += -lr * W.grad # gradient descent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Effect of regularization" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "# regularisation here will want to make W elements smaller (0 best case for it)\n", - "# the regular loss will try to make the probility distribution according to training data\n", - "# regularisation is similar to smoothening that we did before\n", - "# Increasing counts: (N + 1) or even (N + 10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sample from neural net model" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mor.\n", - "axx.\n", - "minaymoryles.\n", - "kondlaisah.\n", - "anchthizarie.\n" - ] - } - ], - "source": [ - "# finally, sample from the 'neural net' model\n", - "g = torch.Generator().manual_seed(2147483647)\n", - "\n", - "for i in range(5):\n", - " \n", - " out = []\n", - " ix = 0\n", - " while True:\n", - " \n", - " # ----------\n", - " # BEFORE:\n", - " #p = P[ix]\n", - " # ----------\n", - " # NOW:\n", - " xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float()\n", - " logits = xenc @ W # predict log-counts\n", - " counts = logits.exp() # counts, equivalent to N\n", - " p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n", - " # ----------\n", - " \n", - " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", - " out.append(itos[ix])\n", - " if ix == 0:\n", - " break\n", - " print(''.join(out))\n", - "\n", - "# same output as before exactly.\n", - "# identical models and acheive the same loss\n", - "# mor.\n", - "# axx.\n", - "# minaymoryles.\n", - "# kondlaisah.\n", - "# anchshizarie." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/my_code/makemore/2_makemore_mlp.ipynb b/my_code/makemore/2_makemore_mlp.ipynb deleted file mode 100644 index 952bd909..00000000 --- a/my_code/makemore/2_makemore_mlp.ipynb +++ /dev/null @@ -1,249 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn.functional\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with open(\"names.txt\", \"r\") as f:\n", - " words = f.read().splitlines()\n", - "\n", - "words[:8]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total names: 32033\n", - "Minimum name length: 2\n", - "Maximum name length: 15\n" - ] - } - ], - "source": [ - "# total words\n", - "print(\"Total names: \", len(words))\n", - "\n", - "print(\"Minimum name length: \", min(len(w) for w in words))\n", - "print(\"Maximum name length: \", max(len(w) for w in words))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stoi: {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n", - "Itos: {1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" - ] - } - ], - "source": [ - "# making vocabulary mappings\n", - "chars = sorted(list(set(\"\".join(words))))\n", - "stoi = {c: i+1 for i, c in enumerate(chars)}\n", - "stoi[\".\"] = 0\n", - "itos = {i: c for c, i in stoi.items()}\n", - "print(\"Stoi: \", stoi)\n", - "print(\"Itos: \", itos)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### build the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "block_size = 3 # context length: how many characters do we take to predict the next one?\n", - "X, Y = [], []\n", - "for w in words[:5]:\n", - " \n", - " #print(w)\n", - " context = [0] * block_size\n", - " for ch in w + '.':\n", - " ix = stoi[ch]\n", - " X.append(context)\n", - " Y.append(ix)\n", - " #print(''.join(itos[i] for i in context), '--->', itos[ix])\n", - " context = context[1:] + [ix] # crop and append\n", - " \n", - "X = torch.tensor(X)\n", - "Y = torch.tensor(Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(torch.Size([32, 3]), torch.Size([32]), torch.int64, torch.int64)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.shape, Y.shape, X.dtype, Y.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# In the paper it has 3 words as inputs\n", - "# And 17000 words with 30 dimensions\n", - "# We only have 27 chars here, let's start with small dimensions for look-up matrix C" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([27, 2])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "C = torch.randn(27, 2)\n", - "C.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([0.9258, 0.9268])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let' embed a single character first\n", - "C[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([32, 3, 2])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's embed all\n", - "emb = C[X]\n", - "emb.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Now let's make the hidden layer\n", - "W1 = torch.randn((6, 100)) # 3*2 inputs and 100 hidden units\n", - "b1 = torch.randn(100)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/my_code/makemore/names.txt b/my_code/makemore/names.txt deleted file mode 100644 index 736d88e1..00000000 --- a/my_code/makemore/names.txt +++ /dev/null @@ -1,32033 +0,0 @@ -emma -olivia -ava -isabella -sophia -charlotte -mia -amelia -harper -evelyn -abigail -emily -elizabeth -mila -ella -avery -sofia -camila -aria -scarlett -victoria -madison -luna -grace -chloe -penelope -layla -riley -zoey -nora -lily -eleanor 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- { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def f(x):\n", - " return 3*x**2 - 4*x + 5" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f(3.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "xs = np.arange(-5, 5, 0.25)\n", - "ys = f(xs)\n", - "plt.plot(xs, ys)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.00000009255109" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now the derivative at a particular point\n", - "# from the definition of derivate\n", - "h = 0.00000001 # lim h->0\n", - "x = 3\n", - "\n", - "f(x+h) # it should be greater than f(x)\n", - "\n", - "# Now Slope\n", - "\n", - "slope = (f(x+h) - f(x)) / h\n", - "slope\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-22.00000039920269" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "h = 0.00000001 # lim h->0\n", - "x = -3\n", - "\n", - "f(x+h) # it should be greater than f(x)\n", - "\n", - "# Now Slope\n", - "\n", - "slope = (f(x+h) - f(x)) / h\n", - "slope # here it will be negative" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "h = 0.00000001 # lim h->0\n", - "x = 2/3\n", - "\n", - "f(x+h) # it should be greater than f(x)\n", - "\n", - "# Now Slope\n", - "\n", - "slope = (f(x+h) - f(x)) / h\n", - "slope # here it will be zero" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.0\n" - ] - } - ], - "source": [ - "# Now more complex function\n", - "a = 2.0\n", - "b = -3.0\n", - "c = 10.0\n", - "\n", - "d = a*b + c\n", - "print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "d1 4.0\n", - "d2 3.999699999999999\n", - "slope -3.000000000010772\n" - ] - } - ], - "source": [ - "h = 0.0001\n", - "\n", - "# inputs\n", - "a = 2.0\n", - "b = -3.0\n", - "c = 10.0\n", - "\n", - "d1 = a*b +c\n", - "\n", - "a += h # increment a by h\n", - "d2 = a*b + c\n", - "\n", - "print(\"d1\", d1)\n", - "print(\"d2\", d2)\n", - "print(\"slope\", (d2-d1)/h)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "d1 4.0\n", - "d2 4.0002\n", - "slope 2.0000000000042206\n" - ] - } - ], - "source": [ - "h = 0.0001\n", - "\n", - "# inputs\n", - "a = 2.0\n", - "b = -3.0\n", - "c = 10.0\n", - "\n", - "d1 = a*b +c\n", - "\n", - "b += h # increment b by h\n", - "d2 = a*b + c\n", - "\n", - "print(\"d1\", d1)\n", - "print(\"d2\", d2)\n", - "print(\"slope\", (d2-d1)/h)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "d1 4.0\n", - "d2 4.0001\n", - "slope 0.9999999999976694\n" - ] - } - ], - "source": [ - "h = 0.0001\n", - "\n", - "# inputs\n", - "a = 2.0\n", - "b = -3.0\n", - "c = 10.0\n", - "\n", - "d1 = a*b +c\n", - "\n", - "c += h # increment c by h\n", - "d2 = a*b + c\n", - "\n", - "print(\"d1\", d1)\n", - "print(\"d2\", d2)\n", - "print(\"slope\", (d2-d1)/h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Value() Object Building" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "class Value:\n", - "\n", - " def __init__(self, data):\n", - " self.data = data\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=2.0)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(2.0)\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# # Now we want to add 2 value objects\n", - "# a = Value(2.0)\n", - "# b = Value(-3.0)\n", - "# c = a + b # This operating is not defined yet\n", - "# c" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\"):\n", - " self.data = data\n", - " self._prev = set(_children)\n", - " self._op = _op\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " return Value(self.data + other.data, (self, other), \"+\")\n", - "\n", - " def __mul__(self, other):\n", - " return Value(self.data * other.data, (self, other), \"*\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=-1.0)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now we want to add 2 value objects\n", - "a = Value(2.0)\n", - "b = Value(-3.0)\n", - "c = a + b # This operating is not defined yet\n", - "c\n", - "\n", - "# a +b \n", - "# python will do\n", - "# a.__add__(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=4.0)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(2.0)\n", - "b = Value(-3.0)\n", - "c = Value(10.0)\n", - "\n", - "d = a*b +c # (a.__mul__(b)).__add__(c)\n", - "d" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{Value(data=-6.0), Value(data=10.0)}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We want to keep a track of relationship between a, b, c, d\n", - "d._prev # we know the children of the value" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'+'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now we also want to know what operations created them\n", - "d._op" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### These expressions will get very large, we would like to visualise them with this code" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install graphviz\n", - "from graphviz import Digraph\n", - "\n", - "def trace(root):\n", - " # builds a set of all nodes and edges in a graph\n", - " nodes, edges = set(), set()\n", - " def build(v):\n", - " if v not in nodes:\n", - " nodes.add(v)\n", - " for child in v._prev:\n", - " edges.add((child, v))\n", - " build(child)\n", - " build(root)\n", - " return nodes, edges\n", - "\n", - "def draw_dot(root):\n", - " dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n", - " \n", - " nodes, edges = trace(root)\n", - " for n in nodes:\n", - " uid = str(id(n))\n", - " # for any value in the graph, create a rectangular ('record') node for it\n", - " dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n", - " if n._op:\n", - " # if this value is a result of some operation, create an op node for it\n", - " dot.node(name = uid + n._op, label = n._op)\n", - " # and connect this node to it\n", - " dot.edge(uid + n._op, uid)\n", - "\n", - " for n1, n2 in edges:\n", - " # connect n1 to the op node of n2\n", - " dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n", - "\n", - " return dot" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " return Value(self.data + other.data, (self, other), \"+\")\n", - "\n", - " def __mul__(self, other):\n", - " return Value(self.data * other.data, (self, other), \"*\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=-8.0)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(2.0, label=\"a\")\n", - "b = Value(-3.0, label=\"b\")\n", - "c = Value(10.0, label=\"c\")\n", - "e = a*b; e.label=\"e\"\n", - "d = e+c; d.label=\"d\"\n", - "f = Value(-2.0, label=\"f\")\n", - "L = d*f; L.label=\"L\"\n", - "L" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backpropagation" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# using this we can visulize the operations\n", - "draw_dot(L)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Now let's calculate gradients" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-2.000000000000668\n" - ] - } - ], - "source": [ - "def lol():\n", - " h = 0.001\n", - " a = Value(2.0, label=\"a\")\n", - " b = Value(-3.0, label=\"b\")\n", - " c = Value(10.0, label=\"c\")\n", - " e = a*b; e.label=\"e\"\n", - " d = e+c; d.label=\"d\"\n", - " f = Value(-2.0, label=\"f\")\n", - " L = d*f; L.label=\"L\"\n", - " L1 = L.data\n", - "\n", - " a = Value(2.0, label=\"a\")\n", - " b = Value(-3.0, label=\"b\")\n", - " c = Value(10.0, label=\"c\")\n", - " e = a*b; e.label=\"e\"\n", - " d = e+c; d.label=\"d\"\n", - " d.data += h\n", - "\n", - " f = Value(-2.0, label=\"f\")\n", - " L = d*f; L.label=\"L\"\n", - "\n", - " L2 = L.data #+ h\n", - "\n", - " print((L2-L1)/h)\n", - " \n", - "lol()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "L.grad = 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# using this we can visulize the operations\n", - "draw_dot(L)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "L = d*f\n", - "\n", - "We want to know\n", - "\n", - "dL/dd = ? => f\n", - "\n", - "(f(x+h) - f(x)) / h\n", - "\n", - "proof:\n", - "(f(x+h) - f(x)) / h\n", - "\n", - "((d+h)*f - d*f) / h\n", - "\n", - "(d*f + h*f - d*f) / h\n", - "\n", - "h*f / h\n", - "\n", - "= f\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "f.grad = 4.0 # value of d\n", - "d.grad = -2.0 # value of f" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(L)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-3.9999999999995595\n" - ] - } - ], - "source": [ - "def lol():\n", - " \n", - " h = 0.001\n", - " \n", - " a = Value(2.0, label='a')\n", - " b = Value(-3.0, label='b')\n", - " c = Value(10.0, label='c')\n", - " e = a*b; e.label = 'e'\n", - " d = e + c; d.label = 'd'\n", - " f = Value(-2.0, label='f')\n", - " L = d * f; L.label = 'L'\n", - " \n", - " L1 = L.data\n", - " \n", - " a = Value(2.0, label='a')\n", - " b = Value(-3.0+h, label='b')\n", - " c = Value(10.0, label='c')\n", - " \n", - " e = a*b; e.label = 'e'\n", - " d = e + c; d.label = 'd'\n", - " f = Value(-2.0, label='f')\n", - " L = d * f; L.label = 'L'\n", - " \n", - " L2 = L.data\n", - " \n", - " print((L2 - L1)/h)\n", - " \n", - "lol()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we would want to find dL/dc\n", - "\n", - "We know d = c + e which when differenciated gives dd/dc = 1.0\n", - "\n", - "also, dd/de = 1.0 too\n", - "\n", - "now, dL/dc can calculated with chain rule\n", - "\n", - "The chain rule may also be expressed in Leibniz's notation. If a variable z depends on the variable y, which itself depends on the variable x (that is, y and z are dependent variables), then z depends on x as well, via the intermediate variable y. In this case, the chain rule is expressed as:\n", - "\n", - "\n", - "dz/dx = (dz/dy) * (dy/dx) # relative rate of change \n", - "\n", - "Now, we want:\n", - "dL/dc =\n", - "\n", - "We know:\n", - "dL/dd\n", - "dd/dc\n", - "\n", - "So, \n", - "dL/dc = (dL/dd) * (dd/dc)\n", - "\n", - "The + node just directly routes the rate of change." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Therefore:\n", - "c.grad = -2.0\n", - "e.grad = -2.0\n", - "draw_dot(L)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we want:\n", - "\n", - "dL/da = (dL/de) * (de/da) \n", - "\n", - "and\n", - "\n", - "dL/db = dL/de) * (de/db)\n", - "\n", - "e = a*b\n", - "\n", - "de/da = b\n", - "\n", - "de/db = a" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 6.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad -4.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.grad = (-2.0 * -3.0)\n", - "b.grad = (-2.0 * 2.0)\n", - "draw_dot(L)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is exactly what backpropagation is:\n", - "Backproagation is recursive application of chain rule throught the computation graph." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### My own little exercise to see if i really understood everything correctly" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=145.0)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(4.0, label=\"a\")\n", - "b = Value(-8.0, label=\"b\")\n", - "c = Value(3.0, label=\"c\")\n", - "e = a*b; e.label=\"e\"\n", - "d = e+c; d.label=\"d\"\n", - "f = Value(-5.0, label=\"f\")\n", - "L = d*f; L.label=\"L\"\n", - "L" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970779264\n\nd\n\ndata -29.0000\n\ngrad 0.0000\n\n\n\n140596970780128*\n\n*\n\n\n\n140596970779264->140596970780128*\n\n\n\n\n\n140596970779264+\n\n+\n\n\n\n140596970779264+->140596970779264\n\n\n\n\n\n140596970777296\n\na\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140596970775808*\n\n*\n\n\n\n140596970777296->140596970775808*\n\n\n\n\n\n140596970775808\n\ne\n\ndata -32.0000\n\ngrad 0.0000\n\n\n\n140596970775808->140596970779264+\n\n\n\n\n\n140596970775808*->140596970775808\n\n\n\n\n\n140596970775856\n\nc\n\ndata 3.0000\n\ngrad 0.0000\n\n\n\n140596970775856->140596970779264+\n\n\n\n\n\n140596970774944\n\nf\n\ndata -5.0000\n\ngrad 0.0000\n\n\n\n140596970774944->140596970780128*\n\n\n\n\n\n140596970780080\n\nb\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140596970780080->140596970775808*\n\n\n\n\n\n140596970780128\n\nL\n\ndata 145.0000\n\ngrad 0.0000\n\n\n\n140596970780128*->140596970780128\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(L)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "L.grad = 1.0\n", - "\n", - "f.grad = -29.0\n", - "d.grad = -5.0\n", - "\n", - "e.grad = -5 * 1\n", - "c.grad = -5 * 1\n", - "\n", - "b.grad = -5 * 4.0\n", - "a.grad = -5 * -8.0\n", - "\n", - "# All correct and verified\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "40.000000000020464\n" - ] - } - ], - "source": [ - "# verfiying the gradient\n", - "def lol():\n", - "\n", - " h = 0.001\n", - "\n", - " a = Value(4.0, label=\"a\")\n", - " b = Value(-8.0, label=\"b\")\n", - " c = Value(3.0, label=\"c\")\n", - " e = a*b; e.label=\"e\"\n", - " d = e+c; d.label=\"d\"\n", - " f = Value(-5.0, label=\"f\")\n", - " L = d*f; L.label=\"L\"\n", - "\n", - " L1 = L.data\n", - "\n", - " a = Value(4.0 + h, label=\"a\")\n", - " b = Value(-8.0, label=\"b\")\n", - " c = Value(3.0, label=\"c\")\n", - " e = a*b; e.label=\"e\"\n", - " \n", - " d = e+c; d.label=\"d\"\n", - " f = Value(-5.0, label=\"f\")\n", - " L = d*f; L.label=\"L\"\n", - " L2 = L.data\n", - "\n", - " print((L2 - L1)/h)\n", - " \n", - "lol()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Single Optimisation Step\n", - "\n", - "We will nudge our inputs to make L go up" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "a = Value(2.0, label=\"a\")\n", - "b = Value(-3.0, label=\"b\")\n", - "c = Value(10.0, label=\"c\")\n", - "e = a*b; e.label=\"e\"\n", - "d = e+c; d.label=\"d\"\n", - "f = Value(-2.0, label=\"f\")\n", - "L = d*f; L.label=\"L\"\n", - "\n", - "L.grad = 1.0\n", - "f.grad = 4.0 # value of d\n", - "d.grad = -2.0 # value of f\n", - "\n", - "c.grad = -2.0\n", - "e.grad = -2.0\n", - "\n", - "a.grad = (-2.0 * -3.0)\n", - "b.grad = (-2.0 * 2.0)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970522656\n\nb\n\ndata -3.0000\n\ngrad -4.0000\n\n\n\n140596970523424*\n\n*\n\n\n\n140596970522656->140596970523424*\n\n\n\n\n\n140596970523808\n\na\n\ndata 2.0000\n\ngrad 6.0000\n\n\n\n140596970523808->140596970523424*\n\n\n\n\n\n140596971724448\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596971721472*\n\n*\n\n\n\n140596971724448->140596971721472*\n\n\n\n\n\n140596971721472\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596971721472*->140596971721472\n\n\n\n\n\n140596971729680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596971729680->140596971721472*\n\n\n\n\n\n140596971729680+\n\n+\n\n\n\n140596971729680+->140596971729680\n\n\n\n\n\n140596970523424\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140596970523424->140596971729680+\n\n\n\n\n\n140596970523424*->140596970523424\n\n\n\n\n\n140596970522560\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140596970522560->140596971729680+\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(L)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-7.286496\n" - ] - } - ], - "source": [ - "step_size = 0.01\n", - "\n", - "# We are chaning the leaf nodes\n", - "a.data += step_size*a.grad\n", - "b.data += step_size*b.grad\n", - "c.data += step_size*c.grad\n", - "f.data += step_size*f.grad\n", - "\n", - "# Let's do the forward pass again\n", - "e = a * b\n", - "d = e + c\n", - "L = d * f\n", - "\n", - "# We expect a greater value of L here\n", - "print(L.data)\n", - "\n", - "# This can be used to optimise as we can control the value of L" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970523664\n\n \n\ndata -6.2624\n\ngrad 0.0000\n\n\n\n140596971724016+\n\n+\n\n\n\n140596970523664->140596971724016+\n\n\n\n\n\n140596970523664*\n\n*\n\n\n\n140596970523664*->140596970523664\n\n\n\n\n\n140596970522656\n\nb\n\ndata -3.0400\n\ngrad -4.0000\n\n\n\n140596970522656->140596970523664*\n\n\n\n\n\n140596971722336\n\n \n\ndata -7.2865\n\ngrad 0.0000\n\n\n\n140596971722336*\n\n*\n\n\n\n140596971722336*->140596971722336\n\n\n\n\n\n140596971724448\n\nf\n\ndata -1.9600\n\ngrad 4.0000\n\n\n\n140596971724448->140596971722336*\n\n\n\n\n\n140596970523808\n\na\n\ndata 2.0600\n\ngrad 6.0000\n\n\n\n140596970523808->140596970523664*\n\n\n\n\n\n140596971724016\n\n \n\ndata 3.7176\n\ngrad 0.0000\n\n\n\n140596971724016->140596971722336*\n\n\n\n\n\n140596971724016+->140596971724016\n\n\n\n\n\n140596970522560\n\nc\n\ndata 9.9800\n\ngrad -2.0000\n\n\n\n140596970522560->140596971724016+\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(L)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Manual Backproagation through a Neuron" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "tanh fucntion, activatiom" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(-5, 5, 0.2)\n", - "y = np.tanh(np.arange(-5, 5, 0.2))\n", - "plt.plot(x, y)\n", - "plt.grid()\n", - "\n", - "# It squashes the input" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "o = tanh(n)\n", - "\n", - "dervate of tanh = 1 - tanh²\n", - "\n", - "do/dn = 1 - o²\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " return Value(self.data + other.data, (self, other), \"+\")\n", - "\n", - " def __mul__(self, other):\n", - " return Value(self.data * other.data, (self, other), \"*\")\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# see neuron_model.jpeg\n", - "\n", - "# inputs\n", - "x1 = Value(2.0, label=\"x1\")\n", - "x2 = Value(0.0, label=\"x2\")\n", - "\n", - "# Weights\n", - "w1 = Value(-3.0, label=\"w1\")\n", - "w2 = Value(1.0, label=\"w2\")\n", - "\n", - "# Bias of a neuron\n", - "b = Value(6.88137358701954432, label=\"b\")\n", - "\n", - "# from figure we can see\n", - "# x1*w1 + x2*w2 + b\n", - "\n", - "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", - "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", - "\n", - "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", - "\n", - "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", - "\n", - "o = n.tanh(); o.label = \"output\"" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936027168\n\nb\n\ndata 6.8814\n\ngrad 0.0000\n\n\n\n140596936027696+\n\n+\n\n\n\n140596936027168->140596936027696+\n\n\n\n\n\n140596936027696\n\nn\n\ndata 0.8814\n\ngrad 0.0000\n\n\n\n140596936028560tanh\n\ntanh\n\n\n\n140596936027696->140596936028560tanh\n\n\n\n\n\n140596936027696+->140596936027696\n\n\n\n\n\n140596936021648\n\nx1\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596936021264*\n\n*\n\n\n\n140596936021648->140596936021264*\n\n\n\n\n\n140596936025296\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596936024816+\n\n+\n\n\n\n140596936025296->140596936024816+\n\n\n\n\n\n140596936025296*\n\n*\n\n\n\n140596936025296*->140596936025296\n\n\n\n\n\n140596936024816\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596936024816->140596936027696+\n\n\n\n\n\n140596936024816+->140596936024816\n\n\n\n\n\n140596936021264\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596936021264->140596936024816+\n\n\n\n\n\n140596936021264*->140596936021264\n\n\n\n\n\n140596936031536\n\nw1\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596936031536->140596936021264*\n\n\n\n\n\n140596936028560\n\noutput\n\ndata 0.7071\n\ngrad 0.0000\n\n\n\n140596936028560tanh->140596936028560\n\n\n\n\n\n140596936029664\n\nx2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596936029664->140596936025296*\n\n\n\n\n\n140596936025584\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936025584->140596936025296*\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(o)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we would care more about gradients wrt weights w1 and w2.\n", - "\n", - "\n", - "Now we calculate gradients" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936027168\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596936027696+\n\n+\n\n\n\n140596936027168->140596936027696+\n\n\n\n\n\n140596936027696\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596936028560tanh\n\ntanh\n\n\n\n140596936027696->140596936028560tanh\n\n\n\n\n\n140596936027696+->140596936027696\n\n\n\n\n\n140596936021648\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596936021264*\n\n*\n\n\n\n140596936021648->140596936021264*\n\n\n\n\n\n140596936025296\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936024816+\n\n+\n\n\n\n140596936025296->140596936024816+\n\n\n\n\n\n140596936025296*\n\n*\n\n\n\n140596936025296*->140596936025296\n\n\n\n\n\n140596936024816\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936024816->140596936027696+\n\n\n\n\n\n140596936024816+->140596936024816\n\n\n\n\n\n140596936021264\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936021264->140596936024816+\n\n\n\n\n\n140596936021264*->140596936021264\n\n\n\n\n\n140596936031536\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596936031536->140596936021264*\n\n\n\n\n\n140596936028560\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596936028560tanh->140596936028560\n\n\n\n\n\n140596936029664\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936029664->140596936025296*\n\n\n\n\n\n140596936025584\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936025584->140596936025296*\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o.grad = 1.0\n", - "n.grad = 1 - o.data**2\n", - "\n", - "b.grad = 0.5\n", - "x1w1x2w2.grad = 0.5\n", - "\n", - "x1w1.grad = 0.5\n", - "x2w2.grad = 0.5\n", - "\n", - "w1.grad = x1w1.grad * x1.data\n", - "x1.grad = x1w1.grad * w1.data\n", - "\n", - "w2.grad = x2w2.grad * x2.data\n", - "x2.grad = x2w2.grad * w2.data\n", - "\n", - "draw_dot(o)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Backpropagation function" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._backward = lambda: None\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " out = Value(self.data + other.data, (self, other), \"+\")\n", - " \n", - " def _backward():\n", - " self.grad = 1.0 * out.grad\n", - " other.grad = 1.0 * out.grad\n", - "\n", - " # this means the gradient is just copied as it is as \n", - " # we have seen it happening for addition \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def __mul__(self, other):\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad = other.data * out.grad\n", - " other.grad = self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - "\n", - " def _backward():\n", - " self.grad = (1 - t**2) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out " - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# inputs\n", - "x1 = Value(2.0, label=\"x1\")\n", - "x2 = Value(0.0, label=\"x2\")\n", - "\n", - "# Weights\n", - "w1 = Value(-3.0, label=\"w1\")\n", - "w2 = Value(1.0, label=\"w2\")\n", - "\n", - "# Bias of a neuron\n", - "b = Value(6.88137358701954432, label=\"b\")\n", - "\n", - "# from figure we can see\n", - "# x1*w1 + x2*w2 + b\n", - "\n", - "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", - "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", - "\n", - "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", - "\n", - "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", - "\n", - "o = n.tanh(); o.label = \"output\"" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970775568\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934156128*\n\n*\n\n\n\n140596970775568->140596934156128*\n\n\n\n\n\n140596970780752\n\nx1\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596934160736*\n\n*\n\n\n\n140596970780752->140596934160736*\n\n\n\n\n\n140596970777248\n\nb\n\ndata 6.8814\n\ngrad 0.0000\n\n\n\n140596934162752+\n\n+\n\n\n\n140596970777248->140596934162752+\n\n\n\n\n\n140596970782384\n\nw1\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970782384->140596934160736*\n\n\n\n\n\n140596934162752\n\nn\n\ndata 0.8814\n\ngrad 0.0000\n\n\n\n140596934158672tanh\n\ntanh\n\n\n\n140596934162752->140596934158672tanh\n\n\n\n\n\n140596934162752+->140596934162752\n\n\n\n\n\n140596934158672\n\noutput\n\ndata 0.7071\n\ngrad 0.0000\n\n\n\n140596934158672tanh->140596934158672\n\n\n\n\n\n140596934156128\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596934156224+\n\n+\n\n\n\n140596934156128->140596934156224+\n\n\n\n\n\n140596934156128*->140596934156128\n\n\n\n\n\n140596970775376\n\nx2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596970775376->140596934156128*\n\n\n\n\n\n140596934160736\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596934160736->140596934156224+\n\n\n\n\n\n140596934160736*->140596934160736\n\n\n\n\n\n140596934156224\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596934156224->140596934162752+\n\n\n\n\n\n140596934156224+->140596934156224\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(o)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We don't have to do it manually now. We just have to call backward in the right order" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# but we will have initialise o.grad first\n", - "o.grad = 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "o._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "n._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "b._backward() # nothing will happen. None" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "x1w1x2w2._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "x1w1._backward()\n", - "x2w2._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970775568\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934156128*\n\n*\n\n\n\n140596970775568->140596934156128*\n\n\n\n\n\n140596970780752\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596934160736*\n\n*\n\n\n\n140596970780752->140596934160736*\n\n\n\n\n\n140596970777248\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596934162752+\n\n+\n\n\n\n140596970777248->140596934162752+\n\n\n\n\n\n140596970782384\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596970782384->140596934160736*\n\n\n\n\n\n140596934162752\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596934158672tanh\n\ntanh\n\n\n\n140596934162752->140596934158672tanh\n\n\n\n\n\n140596934162752+->140596934162752\n\n\n\n\n\n140596934158672\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596934158672tanh->140596934158672\n\n\n\n\n\n140596934156128\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934156224+\n\n+\n\n\n\n140596934156128->140596934156224+\n\n\n\n\n\n140596934156128*->140596934156128\n\n\n\n\n\n140596970775376\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596970775376->140596934156128*\n\n\n\n\n\n140596934160736\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934160736->140596934156224+\n\n\n\n\n\n140596934160736*->140596934160736\n\n\n\n\n\n140596934156224\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934156224->140596934162752+\n\n\n\n\n\n140596934156224+->140596934156224\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(o)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Implementing the backward function for a whole expression graph\n", - "Now we want to eliminate calling backward pass manually\n", - "\n", - "We want to calculate backward for a node only when backward for all the forward nodes have been calculated.\n", - "\n", - "We calculate backward when backward for all dependencies has been calculated.\n", - "\n", - "This ordering of graphs can be achieved by something called **Toplogical Sort**" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# inputs\n", - "x1 = Value(2.0, label=\"x1\")\n", - "x2 = Value(0.0, label=\"x2\")\n", - "\n", - "# Weights\n", - "w1 = Value(-3.0, label=\"w1\")\n", - "w2 = Value(1.0, label=\"w2\")\n", - "\n", - "# Bias of a neuron\n", - "b = Value(6.88137358701954432, label=\"b\")\n", - "\n", - "# from figure we can see\n", - "# x1*w1 + x2*w2 + b\n", - "\n", - "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", - "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", - "\n", - "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", - "\n", - "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", - "\n", - "o = n.tanh(); o.label = \"output\"" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "topo = []\n", - "visited = set()\n", - "\n", - "def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "o.grad = 1.0\n", - "\n", - "build_topo(o)\n", - "\n", - "for node in reversed(topo):\n", - " node._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140598171427376\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934161888*\n\n*\n\n\n\n140598171427376->140596934161888*\n\n\n\n\n\n140596934158432\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934162080+\n\n+\n\n\n\n140596934158432->140596934162080+\n\n\n\n\n\n140596934158432*\n\n*\n\n\n\n140596934158432*->140596934158432\n\n\n\n\n\n140596934149216\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596934155696+\n\n+\n\n\n\n140596934149216->140596934155696+\n\n\n\n\n\n140596934162080\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934162080->140596934155696+\n\n\n\n\n\n140596934162080+->140596934162080\n\n\n\n\n\n140596970774272\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596970774272->140596934158432*\n\n\n\n\n\n140596934163424\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596934163424tanh\n\ntanh\n\n\n\n140596934163424tanh->140596934163424\n\n\n\n\n\n140596970779984\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596970779984->140596934158432*\n\n\n\n\n\n140596934155696\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596934155696->140596934163424tanh\n\n\n\n\n\n140596934155696+->140596934155696\n\n\n\n\n\n140596934149072\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934149072->140596934161888*\n\n\n\n\n\n140596934161888\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934161888->140596934162080+\n\n\n\n\n\n140596934161888*->140596934161888\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here we have it correctly\n", - "draw_dot(o)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# Now let's add this to value class\n", - "\n", - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._backward = lambda: None\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " out = Value(self.data + other.data, (self, other), \"+\")\n", - " \n", - " def _backward():\n", - " self.grad = 1.0 * out.grad\n", - " other.grad = 1.0 * out.grad\n", - "\n", - " # this means the gradient is just copied as it is as \n", - " # we have seen it happening for addition \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def __mul__(self, other):\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad = other.data * out.grad\n", - " other.grad = self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - "\n", - " def _backward():\n", - " self.grad = (1 - t**2) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out \n", - "\n", - " \n", - " def backward(self):\n", - " topo = []\n", - " visited = set()\n", - "\n", - " def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)\n", - " \n", - " build_topo(self)\n", - "\n", - " self.grad = 1.0\n", - " for node in reversed(topo):\n", - " node._backward()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's see if everything works\n", - "# inputs\n", - "x1 = Value(2.0, label=\"x1\")\n", - "x2 = Value(0.0, label=\"x2\")\n", - "\n", - "# Weights\n", - "w1 = Value(-3.0, label=\"w1\")\n", - "w2 = Value(1.0, label=\"w2\")\n", - "\n", - "# Bias of a neuron\n", - "b = Value(6.88137358701954432, label=\"b\")\n", - "\n", - "# from figure we can see\n", - "# x1*w1 + x2*w2 + b\n", - "\n", - "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", - "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", - "\n", - "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", - "\n", - "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", - "\n", - "o = n.tanh(); o.label = \"output\"" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936162848\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596936160880+\n\n+\n\n\n\n140596936162848->140596936160880+\n\n\n\n\n\n140596936157232\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936158288+\n\n+\n\n\n\n140596936157232->140596936158288+\n\n\n\n\n\n140596936157232*\n\n*\n\n\n\n140596936157232*->140596936157232\n\n\n\n\n\n140596936158288\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936158288->140596936160880+\n\n\n\n\n\n140596936158288+->140596936158288\n\n\n\n\n\n140596936160880\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596936156032tanh\n\ntanh\n\n\n\n140596936160880->140596936156032tanh\n\n\n\n\n\n140596936160880+->140596936160880\n\n\n\n\n\n140596936162944\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936162944->140596936157232*\n\n\n\n\n\n140596936162464\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936162464->140596936158288+\n\n\n\n\n\n140596936162464*\n\n*\n\n\n\n140596936162464*->140596936162464\n\n\n\n\n\n140596936162992\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596936162992->140596936162464*\n\n\n\n\n\n140596936159968\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596936159968->140596936162464*\n\n\n\n\n\n140596936156992\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936156992->140596936157232*\n\n\n\n\n\n140596936156032\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596936156032tanh->140596936156032\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o.backward()\n", - "\n", - "draw_dot(o)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now that's backprop for 1 Neuron" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is still a bug, we haven't surfaced yet" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936162704\n\na\n\ndata 3.0000\n\ngrad 1.0000\n\n\n\n140596936159344+\n\n+\n\n\n\n140596936162704->140596936159344+\n\n\n\n\n\n140596936159344\n\nb\n\ndata 6.0000\n\ngrad 1.0000\n\n\n\n140596936159344+->140596936159344\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(3.0, label=\"a\")\n", - "b = a + a; b.label=\"b\"\n", - "\n", - "b.backward()\n", - "\n", - "draw_dot(b)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596934151184\n\nf\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934151184*\n\n*\n\n\n\n140596934151184*->140596934151184\n\n\n\n\n\n140596936160784\n\na\n\ndata -2.0000\n\ngrad -6.0000\n\n\n\n140596934151424+\n\n+\n\n\n\n140596936160784->140596934151424+\n\n\n\n\n\n140596934159776*\n\n*\n\n\n\n140596936160784->140596934159776*\n\n\n\n\n\n140596936162416\n\nb\n\ndata 3.0000\n\ngrad -6.0000\n\n\n\n140596936162416->140596934151424+\n\n\n\n\n\n140596936162416->140596934159776*\n\n\n\n\n\n140596934151424\n\ne\n\ndata 1.0000\n\ngrad -6.0000\n\n\n\n140596934151424->140596934151184*\n\n\n\n\n\n140596934151424+->140596934151424\n\n\n\n\n\n140596934159776\n\nd\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934159776->140596934151184*\n\n\n\n\n\n140596934159776*->140596934159776\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Another example for this bug\n", - "a = Value(-2.0, label='a')\n", - "b = Value(3.0, label='b')\n", - "d = a * b ; d.label = 'd'\n", - "e = a + b ; e.label = 'e'\n", - "f = d * e ; f.label = 'f'\n", - "\n", - "f.backward()\n", - "\n", - "draw_dot(f)\n", - "\n", - "# There will be an issue when we are using same variable twice\n", - "# before we were using one variable once\n", - "\n", - "# If you see the multivariate chain rule you will see we need to accumulate the gradients\n", - "# That's what we will do now\n", - "# Any contrubution that flows backward will simply add" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# Now let's add this to value class\n", - "\n", - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._backward = lambda: None\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " out = Value(self.data + other.data, (self, other), \"+\")\n", - " \n", - " def _backward():\n", - " self.grad += 1.0 * out.grad\n", - " other.grad += 1.0 * out.grad\n", - "\n", - " # this means the gradient is just copied as it is as \n", - " # we have seen it happening for addition \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def __mul__(self, other):\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad += other.data * out.grad\n", - " other.grad += self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - "\n", - " def _backward():\n", - " self.grad += (1 - t**2) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out \n", - "\n", - " \n", - " def backward(self):\n", - " topo = []\n", - " visited = set()\n", - "\n", - " def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)\n", - " \n", - " build_topo(self)\n", - "\n", - " self.grad = 1.0\n", - " for node in reversed(topo):\n", - " node._backward()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970778256\n\na\n\ndata -2.0000\n\ngrad -3.0000\n\n\n\n140596934149792+\n\n+\n\n\n\n140596970778256->140596934149792+\n\n\n\n\n\n140596934148880*\n\n*\n\n\n\n140596970778256->140596934148880*\n\n\n\n\n\n140596934149792\n\ne\n\ndata 1.0000\n\ngrad -6.0000\n\n\n\n140596934162368*\n\n*\n\n\n\n140596934149792->140596934162368*\n\n\n\n\n\n140596934149792+->140596934149792\n\n\n\n\n\n140596934148880\n\nd\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934148880->140596934162368*\n\n\n\n\n\n140596934148880*->140596934148880\n\n\n\n\n\n140596970780560\n\nb\n\ndata 3.0000\n\ngrad -8.0000\n\n\n\n140596970780560->140596934149792+\n\n\n\n\n\n140596970780560->140596934148880*\n\n\n\n\n\n140596934162368\n\nf\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934162368*->140596934162368\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Another example for this bug\n", - "a = Value(-2.0, label='a')\n", - "b = Value(3.0, label='b')\n", - "d = a * b ; d.label = 'd'\n", - "e = a + b ; e.label = 'e'\n", - "f = d * e ; f.label = 'f'\n", - "\n", - "f.backward()\n", - "\n", - "draw_dot(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596934162848\n\na\n\ndata 3.0000\n\ngrad 2.0000\n\n\n\n140596934163808+\n\n+\n\n\n\n140596934162848->140596934163808+\n\n\n\n\n\n140596934163808\n\nb\n\ndata 6.0000\n\ngrad 1.0000\n\n\n\n140596934163808+->140596934163808\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(3.0, label=\"a\")\n", - "b = a + a; b.label=\"b\"\n", - "\n", - "b.backward()\n", - "\n", - "draw_dot(b)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now everything is correct" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/my_code/micrograd/2_activation_optmization_grad_descent.ipynb b/my_code/micrograd/2_activation_optmization_grad_descent.ipynb deleted file mode 100644 index 22dc4327..00000000 --- a/my_code/micrograd/2_activation_optmization_grad_descent.ipynb +++ /dev/null @@ -1,1610 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import random\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "random.seed(1993) # For consistency" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def f(x):\n", - " return 3*x**2 - 4*x + 5\n", - "\n", - "f(3.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "xs = np.arange(-5, 5, 0.25)\n", - "ys = f(xs)\n", - "plt.plot(xs, ys)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install graphviz\n", - "from graphviz import Digraph\n", - "\n", - "def trace(root):\n", - " # builds a set of all nodes and edges in a graph\n", - " nodes, edges = set(), set()\n", - " def build(v):\n", - " if v not in nodes:\n", - " nodes.add(v)\n", - " for child in v._prev:\n", - " edges.add((child, v))\n", - " build(child)\n", - " build(root)\n", - " return nodes, edges\n", - "\n", - "def draw_dot(root):\n", - " dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n", - " \n", - " nodes, edges = trace(root)\n", - " for n in nodes:\n", - " uid = str(id(n))\n", - " # for any value in the graph, create a rectangular ('record') node for it\n", - " dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n", - " if n._op:\n", - " # if this value is a result of some operation, create an op node for it\n", - " dot.node(name = uid + n._op, label = n._op)\n", - " # and connect this node to it\n", - " dot.edge(uid + n._op, uid)\n", - "\n", - " for n1, n2 in edges:\n", - " # connect n1 to the op node of n2\n", - " dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n", - "\n", - " return dot" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def trace(root):\n", - " # builds a set of all nodes and edges in a graph\n", - " nodes, edges = set(), set()\n", - " def build(v):\n", - " if v not in nodes:\n", - " nodes.add(v)\n", - " for child in v._prev:\n", - " edges.add((child, v))\n", - " build(child)\n", - " build(root)\n", - " return nodes, edges" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Now let's add this to value class\n", - "\n", - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._backward = lambda: None\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " def __add__(self, other):\n", - " out = Value(self.data + other.data, (self, other), \"+\")\n", - " \n", - " def _backward():\n", - " self.grad += 1.0 * out.grad\n", - " other.grad += 1.0 * out.grad\n", - "\n", - " # this means the gradient is just copied as it is as \n", - " # we have seen it happening for addition \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def __mul__(self, other):\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad += other.data * out.grad\n", - " other.grad += self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - "\n", - " def _backward():\n", - " self.grad += (1 - t**2) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out \n", - "\n", - " \n", - " def backward(self):\n", - " topo = []\n", - " visited = set()\n", - "\n", - " def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)\n", - "\n", - " self.grad = 1.0 \n", - " for node in reversed(topo):\n", - " node._backward()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Currently we cannot do integer addition directly, so, we will make change now\n", - "# We will do this for multiply\n", - "\n", - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._backward = lambda: None\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " \n", - " def __add__(self, other):\n", - " other = other if isinstance(other, Value) else Value(other) # now, we can add integer directly\n", - " out = Value(self.data + other.data, (self, other), \"+\")\n", - " \n", - " def _backward():\n", - " self.grad += 1.0 * out.grad\n", - " other.grad += 1.0 * out.grad\n", - "\n", - " # this means the gradient is just copied as it is as \n", - " # we have seen it happening for addition \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - " \n", - " def __radd__(self, other):\n", - " return self + other\n", - "\n", - " \n", - " def __mul__(self, other):\n", - " other = other if isinstance(other, Value) else Value(other)\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad += other.data * out.grad\n", - " other.grad += self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - "\n", - " def __pow__(self, other):\n", - " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", - " out = Value(self.data**other, (self,), f'**{other}')\n", - "\n", - " def _backward():\n", - " self.grad += (other*self.data**(other - 1)) * out.grad\n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - " \n", - "\n", - " def __rmul__(self, other): #other * self\n", - " return self * other\n", - "\n", - " \n", - " def __truediv__(self, other):\n", - " return self * (other**-1)\n", - "\n", - " \n", - " def __sub__(self, other):\n", - " return self + (-other) \n", - " \n", - " \n", - " def exp(self):\n", - " x = self.data\n", - " out = Value(math.exp(x), (self, ), \"exp\")\n", - "\n", - " def _backward():\n", - " self.grad += out.data * out.grad # exp(x) * out.grad\n", - " \n", - " out._backward = _backward\n", - " \n", - " return out\n", - "\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - "\n", - " def _backward():\n", - " self.grad += (1 - t**2) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out \n", - "\n", - " \n", - " def backward(self):\n", - " topo = []\n", - " visited = set()\n", - "\n", - " def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)\n", - " \n", - " build_topo(self)\n", - "\n", - " self.grad = 1.0\n", - " for node in reversed(topo):\n", - " node._backward()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Value(data=3.0)\n", - "Value(data=3.0)\n", - "Value(data=4.0)\n", - "Value(data=1.0)\n", - "Value(data=0.5)\n", - "Value(data=16.0)\n", - "Value(data=54.598150033144236)\n" - ] - } - ], - "source": [ - "a = Value(2.0)\n", - "b = Value(4.0)\n", - "\n", - "print(a+1)\n", - "print(1+a)\n", - "\n", - "print(a*2)\n", - "print(a/2)\n", - "print(a/b)\n", - "\n", - "print(b**2)\n", - "\n", - "print(b.exp())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Division**\n", - "\n", - "a / b\n", - "\n", - "a * (1/b)\n", - "\n", - "a * (b**-1)\n", - "\n", - "Let's make:\n", - "x**k\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=0.5)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = Value(2.0)\n", - "b = Value(4.0)\n", - "\n", - "a/b\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Breaking down tanh" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's see if everything works\n", - "# inputs\n", - "x1 = Value(2.0, label=\"x1\")\n", - "x2 = Value(0.0, label=\"x2\")\n", - "\n", - "# Weights\n", - "w1 = Value(-3.0, label=\"w1\")\n", - "w2 = Value(1.0, label=\"w2\")\n", - "\n", - "# Bias of a neuron\n", - "b = Value(6.88137358701954432, label=\"b\")\n", - "\n", - "# from figure we can see\n", - "# x1*w1 + x2*w2 + b\n", - "\n", - "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", - "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", - "\n", - "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", - "\n", - "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", - "\n", - "#o = n.tanh(); o.label = \"output\"\n", - "# ---------- tanh break-up ----------\n", - "e = (2*n).exp()\n", - "o = (e - 1) / (e + 1); o.label = \"output\"\n", - "\n", - "# ---------- tanh break-up ----------\n", - "\n", - "\n", - "o.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140679288691712\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140679288691712*\n\n*\n\n\n\n140679288691712*->140679288691712\n\n\n\n\n\n140679288689696\n\n \n\ndata 1.7627\n\ngrad 0.2500\n\n\n\n140679288689552exp\n\nexp\n\n\n\n140679288689696->140679288689552exp\n\n\n\n\n\n140679288689696*\n\n*\n\n\n\n140679288689696*->140679288689696\n\n\n\n\n\n140679288695840\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140679288695984+\n\n+\n\n\n\n140679288695840->140679288695984+\n\n\n\n\n\n140679288695840+\n\n+\n\n\n\n140679288695840+->140679288695840\n\n\n\n\n\n140679288687680\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140679288698144*\n\n*\n\n\n\n140679288687680->140679288698144*\n\n\n\n\n\n140679288689840\n\n \n\ndata 4.8284\n\ngrad 0.1464\n\n\n\n140679288689840->140679288691712*\n\n\n\n\n\n140679288689840+\n\n+\n\n\n\n140679288689840+->140679288689840\n\n\n\n\n\n140679288692912\n\n \n\ndata 2.0000\n\ngrad 0.2203\n\n\n\n140679288692912->140679288689696*\n\n\n\n\n\n140679288695984\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140679288695984->140679288689696*\n\n\n\n\n\n140679288695984+->140679288695984\n\n\n\n\n\n140679288687824\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140679288700160*\n\n*\n\n\n\n140679288687824->140679288700160*\n\n\n\n\n\n140679288688880\n\n \n\ndata 6.8284\n\ngrad -0.1036\n\n\n\n140679288691568**-1\n\n**-1\n\n\n\n140679288688880->140679288691568**-1\n\n\n\n\n\n140679288688880+\n\n+\n\n\n\n140679288688880+->140679288688880\n\n\n\n\n\n140679288700160\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140679288700160->140679288695840+\n\n\n\n\n\n140679288700160*->140679288700160\n\n\n\n\n\n140679288690944\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140679288690944->140679288698144*\n\n\n\n\n\n140679288698144\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140679288698144->140679288695840+\n\n\n\n\n\n140679288698144*->140679288698144\n\n\n\n\n\n140679288686384\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140679288686384->140679288700160*\n\n\n\n\n\n140679288686432\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140679288686432->140679288695984+\n\n\n\n\n\n140679288691568\n\n \n\ndata 0.1464\n\ngrad 4.8284\n\n\n\n140679288691568->140679288691712*\n\n\n\n\n\n140679288691568**-1->140679288691568\n\n\n\n\n\n140679288689552\n\n \n\ndata 5.8284\n\ngrad 0.0429\n\n\n\n140679288689552->140679288689840+\n\n\n\n\n\n140679288689552->140679288688880+\n\n\n\n\n\n140679288689552exp->140679288689552\n\n\n\n\n\n140679288689600\n\n \n\ndata -1.0000\n\ngrad 0.1464\n\n\n\n140679288689600->140679288689840+\n\n\n\n\n\n140679288689120\n\n \n\ndata 1.0000\n\ngrad -0.1036\n\n\n\n140679288689120->140679288688880+\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "draw_dot(o)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The outputs of this resembles exactly the output if we directly use tanh fucntion. So, it doesn't matter how we implement operations. \n", - "\n", - "All that matters is we can correctly calulate the forward and backward pass." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now the same thing is PyTorch" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7071066904050358\n", - "---\n", - "x2 0.5000001283844369\n", - "w2 0.0\n", - "x1 -1.5000003851533106\n", - "w1 1.0000002567688737\n" - ] - } - ], - "source": [ - "x1 = torch.Tensor([2.0]).double() ; x1.requires_grad = True\n", - "x2 = torch.Tensor([0.0]).double() ; x2.requires_grad = True\n", - "w1 = torch.Tensor([-3.0]).double() ; w1.requires_grad = True\n", - "w2 = torch.Tensor([1.0]).double() ; w2.requires_grad = True\n", - "b = torch.Tensor([6.8813735870195432]).double() ; b.requires_grad = True\n", - "n = x1*w1 + x2*w2 + b\n", - "o = torch.tanh(n)\n", - "\n", - "print(o.data.item())\n", - "o.backward()\n", - "\n", - "print('---')\n", - "print('x2', x2.grad.item())\n", - "print('w2', w2.grad.item())\n", - "print('x1', x1.grad.item())\n", - "print('w1', w1.grad.item())\n", - "\n", - "# The result is exactly the same" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Building Neural Net(s) now" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class Neuron:\n", - "\n", - " def __init__(self, nin):\n", - " self.w = [Value(random.uniform(-1,1)) for _ in range(nin)]\n", - " self.b = Value(random.uniform(-1,1))\n", - "\n", - " def __call__(self, x):\n", - " # w*x +b # w*x is a dot product\n", - " act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b )\n", - " out = act.tanh()\n", - " return out\n", - "\n", - " def parameters(self):\n", - " return self.w + [self.b]\n", - "\n", - "\n", - "class Layer:\n", - "\n", - " def __init__(self, nin, nout):\n", - " self.neurons = [Neuron(nin) for _ in range(nout)]\n", - "\n", - " def __call__(self, x):\n", - " outs = [n(x) for n in self.neurons]\n", - " return outs[0] if len(outs) == 1 else outs\n", - "\n", - " def parameters(self): \n", - " return [p for n in self.neurons for p in n.parameters()]\n", - "\n", - " # for neuron in self.neurons:\n", - " # ps = neuron.parameters()\n", - " # params.extend(ps)\n", - " # return params\n", - "\n", - "\n", - "class MLP:\n", - " \n", - " def __init__(self, nin, nouts):\n", - " sz = [nin] + nouts\n", - " self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))]\n", - "\n", - " def __call__(self, x):\n", - " for layer in self.layers:\n", - " x = layer(x)\n", - " return x\n", - "\n", - " def parameters(self):\n", - " return [p for layer in self.layers for p in layer.parameters()]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=0.007657120625740258)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = [2.0, 3.0, -1.0]\n", - "n = MLP(3, [4, 4, 1])\n", - "n(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "41" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(n.parameters())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#draw_dot(n(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Examples for training\n", - "xs = [\n", - " [2.0, 3.0, -1.0],\n", - " [3.0, -1.0, 0.5],\n", - " [0.5, 1.0, 1.0],\n", - " [1.0, 1.0, -1.0],\n", - "]\n", - "ys = [1.0, -1.0, -1.0, 1.0] # desired targets" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Value(data=0.007657120625740258),\n", - " Value(data=0.9629698232963938),\n", - " Value(data=0.6722963345252638),\n", - " Value(data=-0.24414394993705751)]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ypred =[n(x) for x in xs]\n", - "ypred" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=9.182464116048688)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We wil calculate the loss to measure the correcteness of the prediction: how well the neural net is performing\n", - "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", - "loss" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# We want the loss to be low\n", - "loss.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.17473429586587344" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n.layers[0].neurons[0].w[0].grad # If the gradient is negative, that means by increasing the weight of this neuron, the loss will go down\n", - "\n", - "# We have this information for all of our neurons and their parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "w1 grad 0.17473429586587344\n", - "w1 data -0.03862055212227311\n" - ] - } - ], - "source": [ - "print('w1 grad', n.layers[0].neurons[0].w[0].grad)\n", - "print('w1 data', n.layers[0].neurons[0].w[0].data)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# we will itirate through all the parameters\n", - "\n", - "for p in n.parameters():\n", - " p.data += -0.01 * p.grad # if gradient is negative, we increase the weight, if it is positive, we decrease the weight" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "w1 grad 0.17473429586587344\n", - "w1 data -0.040367895080931845\n" - ] - } - ], - "source": [ - "# after the change\n", - "print('w1 grad', n.layers[0].neurons[0].w[0].grad)\n", - "print('w1 data', n.layers[0].neurons[0].w[0].data)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=8.607967674232228)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now we recalculate the loss # we expecct it to be lower\n", - "ypred =[n(x) for x in xs]\n", - "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", - "loss\n", - "\n", - "# It is lower\n", - "\n", - "# You won't get the exact same results as I did, but you should get a loss that is lower than the initial loss (it was randomly initialized) : \n", - "# but now you will do because i seeded the random number generator" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we will do backward pass\n", - "loss.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "for p in n.parameters():\n", - " p.data += -0.1 * p.grad" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=6.247012816219952)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ypred =[n(x) for x in xs]\n", - "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", - "loss\n", - "\n", - "# Now the loss should decrease more" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is gradient descent, we are doing forward pass, backward pass and then update. As a result neural net is improving its predictions<>" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Value(data=0.8009931947462453),\n", - " Value(data=-0.6030405624037342),\n", - " Value(data=0.7932559750217888),\n", - " Value(data=0.7905048504993913)]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's now do this a few more time with an increased learning rate\n", - "\n", - "for e in range(5):\n", - " ypred =[n(x) for x in xs]\n", - " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", - " loss.backward()\n", - " for p in n.parameters():\n", - " p.data += -0.1 * p.grad\n", - "\n", - "ypred\n", - "\n", - "# You can see the output is getting closer to the desired output" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Value(data=3.4568357132496788)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the loss here is:\n", - "loss\n", - "\n", - "# see it is very low now\n", - "# loss = Value(data=3.1391947868174866e-07)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's look at the parameters\n", - "# There are a set of trained neural net parameters that will give us the desired output\n", - "\n", - "#n.parameters()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Let's do it in a proper way from begining like we do in regular trainings" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Data\n", - "xs = [\n", - " [2.0, 3.0, -1.0],\n", - " [3.0, -1.0, 0.5],\n", - " [0.5, 1.0, 1.0],\n", - " [1.0, 1.0, -1.0],\n", - "]\n", - "ys = [1.0, -1.0, -1.0, 1.0] # desired targets" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current step: 0 Loss: 8.590244752121581\n", - "Current step: 2 Loss: 3.251279565120799\n", - "Current step: 4 Loss: 0.4314166532029969\n", - "Current step: 6 Loss: 0.008903970131844198\n", - "Current step: 8 Loss: 0.00017030867419075724\n", - "Current step: 10 Loss: 6.909809892444565e-06\n", - "Current step: 12 Loss: 3.79564352360394e-07\n", - "Current step: 14 Loss: 2.3918872973270876e-08\n", - "Current step: 16 Loss: 1.6092511096719524e-09\n", - "Current step: 18 Loss: 1.3445988501866946e-06\n", - "[Value(data=0.9999971000178582), Value(data=-0.9999999999981305), Value(data=-0.999999999998144), Value(data=0.9999981856078193)]\n" - ] - } - ], - "source": [ - "x = [2.0, 3.0, -1.0]\n", - "n = MLP(3, [4, 4, 1])\n", - "\n", - "learning_rate = 0.05\n", - "\n", - "for e in range(20):\n", - " # forward pass\n", - " ypred =[n(x) for x in xs]\n", - " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", - " \n", - " # backward pass\n", - " loss.backward()\n", - "\n", - " # update\n", - " for p in n.parameters():\n", - " p.data += -learning_rate * p.grad\n", - " \n", - " # print every\n", - " if e % 2 == 0:\n", - " print(\"Current step: \", e, \"Loss: \", loss.data)\n", - "\n", - "# After last step of training let's predict again\n", - "ypred =[n(x) for x in xs]\n", - "print(ypred)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current step: 0 Loss: 3.0671681764088587\n", - "Current step: 2 Loss: 0.7522797144376534\n", - "Current step: 4 Loss: 0.21049320414541034\n", - "Current step: 6 Loss: 0.12931817259939735\n", - "Current step: 8 Loss: 0.09173860850980789\n", - "Current step: 10 Loss: 0.07046366092766423\n", - "Current step: 12 Loss: 0.05690191664948921\n", - "Current step: 14 Loss: 0.04755552992867856\n", - "Current step: 16 Loss: 0.04074910543584072\n", - "Current step: 18 Loss: 0.03558485523377747\n", - 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We are not setting the gradients back to zero. lol\n", - "# The only reason previous one was working because the data is very simple.\n", - "# Also you can see the number of epochs has increased by a lot. Gradients are being set back to zero.\n", - "# Previously they were being added.\n", - "# Let's fix that!\n", - "\n", - "x = [2.0, 3.0, -1.0]\n", - "n = MLP(3, [4, 4, 1])\n", - "\n", - "learning_rate = 0.05\n", - "\n", - "# We are doing gradient descent here\n", - "for e in range(1000):\n", - " # forward pass\n", - " ypred =[n(x) for x in xs]\n", - " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", - " \n", - " # zero grad\n", - " for p in n.parameters():\n", - " p.grad = 0.0 # we need to set the gradients back to zero, otherwise they will accumulate\n", - " \n", - " # backward pass\n", - " loss.backward()\n", - "\n", - " # update\n", - " for p in n.parameters():\n", - " p.data += -learning_rate * p.grad\n", - " \n", - " # print every\n", - " if e % 2 == 0:\n", - " print(\"Current step: \", e, \"Loss: \", loss.data)\n", - "\n", - "# After last step of training let's predict again\n", - "ypred =[n(x) for x in xs]\n", - "print(ypred)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/my_code/micrograd/micrograd_demo.ipynb b/my_code/micrograd/micrograd_demo.ipynb deleted file mode 100644 index 217da7a5..00000000 --- a/my_code/micrograd/micrograd_demo.ipynb +++ /dev/null @@ -1,310 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#%pip install micrograd --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from micrograd.engine import Value\n", - "from micrograd.nn import Neuron, Layer, MLP" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1337)\n", - "random.seed(1337)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make up a dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.datasets import make_moons, make_blobs\n", - "X, y = make_moons(n_samples=100, noise=0.1)\n", - "\n", - "y = y*2 - 1 # make y be -1 or 1\n", - "# visualize in 2D\n", - "plt.figure(figsize=(5,5))\n", - "plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize a model " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MLP of [Layer of [ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2)], Layer of [ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16)], Layer of [LinearNeuron(16)]]\n", - "number of parameters 337\n" - ] - } - ], - "source": [ - "model = MLP(2, [16, 16, 1]) # 2-layer neural network\n", - "print(model)\n", - "print(\"number of parameters\", len(model.parameters()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Loss function " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Value(data=0.9219220440708354, grad=0) 0.5\n" - ] - } - ], - "source": [ - "def loss(batch_size=None):\n", - " \n", - " # inline DataLoader :)\n", - " if batch_size is None:\n", - " Xb, yb = X, y\n", - " else:\n", - " ri = np.random.permutation(X.shape[0])[:batch_size]\n", - " Xb, yb = X[ri], y[ri]\n", - " inputs = [list(map(Value, xrow)) for xrow in Xb]\n", - " \n", - " # forward the model to get scores\n", - " scores = list(map(model, inputs))\n", - " \n", - " # svm \"max-margin\" loss\n", - " losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(yb, scores)]\n", - " data_loss = sum(losses) * (1.0 / len(losses))\n", - " \n", - " # L2 regularization\n", - " alpha = 1e-4\n", - " reg_loss = alpha * sum((p*p for p in model.parameters()))\n", - " total_loss = data_loss + reg_loss\n", - " \n", - " # also get accuracy\n", - " accuracy = [(yi > 0) == (scorei.data > 0) for yi, scorei in zip(yb, scores)]\n", - " return total_loss, sum(accuracy) / len(accuracy)\n", - "\n", - "total_loss, acc = loss()\n", - "print(total_loss, acc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Optimization" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 0 loss 0.19804839220213744, accuracy 94.0%\n", - "step 5 loss 0.24672248029881824, accuracy 90.0%\n", - "step 10 loss 0.11462603871211018, accuracy 97.0%\n", - "step 15 loss 0.16748079173634225, accuracy 93.0%\n", - "step 20 loss 0.061716686945202125, accuracy 99.0%\n", - "step 25 loss 0.06513144508540747, accuracy 98.0%\n", - "step 30 loss 0.04222444942627202, accuracy 99.0%\n", - "step 35 loss 0.04889118476767827, accuracy 99.0%\n", - "step 40 loss 0.024106844141993013, accuracy 99.0%\n", - "step 45 loss 0.02942947332012279, accuracy 100.0%\n", - "step 50 loss 0.024905708104035043, accuracy 99.0%\n", - "step 55 loss 0.019293939608525292, accuracy 100.0%\n", - "step 60 loss 0.01778576221766042, accuracy 100.0%\n", - "step 65 loss 0.015836073894976793, accuracy 100.0%\n", - "step 70 loss 0.014353628187808278, accuracy 100.0%\n", - "step 75 loss 0.014283006307858672, accuracy 100.0%\n", - "step 80 loss 0.013501075237247298, accuracy 100.0%\n", - "step 85 loss 0.013070661438117028, accuracy 100.0%\n", - "step 90 loss 0.012660005324128556, accuracy 100.0%\n", - "step 95 loss 0.01286610384023592, accuracy 100.0%\n" - ] - } - ], - "source": [ - "for k in range(100):\n", - " \n", - " # forward\n", - " total_loss, acc = loss()\n", - " \n", - " # backward\n", - " model.zero_grad()\n", - " total_loss.backward()\n", - " \n", - " # update (sgd)\n", - " learning_rate = 1.0 - 0.9*k/100\n", - " for p in model.parameters():\n", - " p.data -= learning_rate * p.grad\n", - " \n", - " if k % 5 == 0:\n", - " print(f\"step {k} loss {total_loss.data}, accuracy {acc*100}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualize decision boundary\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-1.7222875713140986, 2.027712428685901)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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v3agh0iYipYSUct0F2TIl1AyOV0tg5qIbAW8QOw53ZbzpNWIwNEQCEQgh0HxF44rTRiuI6JHxqvZKKKqCHTduxdDvRjBz0Z04nMnofp+h7lF0HmjL6N9AVKiW9qHkUrLgssSswQUosPCSiePHj+PYsWOxv3u9XrS18cWOMjc/7cPEuSm4R7yAjNY9qd1ag/rtNdF2ABvE4SqBtcSSfhNECSzM+DFz0Z3Rxl2/N4DRd5MsGS1jXTzVVLOlClpYu9SyYCl3ScDhLMHWD2yJBSmLTcWWa9ugh3W4h5PMlEpgdtCN1n1NsHL5iIrE6g20uZQouAhHvamDC1Bg4aWxsRHj4ytfNMfHx+F0OuPOugCA3W6H3W7fiOHRJjB9cRYXXxlaUWwuHIhg9J1xzA64sePGrg3bhyGEQMPOumggyMDkhem0w4vfG8C5kxega7qh65c/fv32WlS1V2KmfxaBuSAUi4LKZifK68pix6qXW5hdW313DRldwrI2ZP8IuhFSSizM+OEe8ULXdJRU2FHdXrmhIZaKB4NL9hRUeDl48CB+9atfrbjtySefxMGDB/M0ItpMgr7QpaWPOMsZgfkgBl4fQdfB9rWfzJG67TXwewOY7ptN3UNpFSOVeFcbemMkGlwMNJtceiNfzmq3oGFnnbEnM7gqFif3GCZ1icB8EJDRNg3pLKOFA2H0vjgA3/TCipmkoe5RtF3VnLXj6LQ5MLhkV07Dy/z8PHp6emJ/7+vrQ3d3N6qrq9He3o7jx49jeHgYP/nJTwAAn/nMZ/Dd734Xf/d3f4c///M/x1NPPYX//b//N/7jP/4jl8MkAgBMXZhOfoEE3MMehPxh2BzZKwKXjBAC7ftbUNVWiakL01iY9UMCCC+krquS7n6X4HwIcxM+Q9eW15ah833t6yok52yowFTfTNKgJBSRUbNGqUuMn5vExPkpRILRzcuKRUFtZxUaL6vHwmwAAe+lonurv5+6puP8M32xWjbLxyh1iYHTw1AsyprwRhRPoloruZRucFlihuAC5Di8vPbaa7jxxhtjf1/am3L77bfjwQcfxOjoKAYGBmKf7+zsxH/8x3/gb/7mb/Dtb38bra2t+OEPf8hj0pQT81M+TPRMwzcZfcM2NOMgo0XZbK2u3A9wkRACzoZyOBeXTqSUePfx95LPrAigqi29MQa8AUPXWR0W7DjcldZjx1O7tRpTvTOJLxBATWdV2ks0Upe48OLFaK+pZfSIjonz05jomV7zfa5qd6F9fwtUS/S5Zgc9CHiDSZ9n5K0xVLW54i6JES0xUt02Z9IILs89oZsmuAA5Di+HDx+GlInfDeJVzz18+DDeeOONHI6KCBh5ZzxabyTNpRggg+uzTAiBxl11uPjacNJr6rbVpPe4BmdRsrXfo7TSgbarmzH4+kjc70NplQMtexrTftyZi7NrgssKcb5/s4MehBbC2PHBLghFYNpANeDQQhi+6QXD/Z9o8ymU4HJJcQQXoMD2vBBthNkhz6VCaRkEkdLq9JcxjNK16Akcz4gXuiZR4rSjtrMa9nLbiuuqt1Qh6Atj7MzaACYUga5DHSipSG8je3lNKRSLAj2SZLOuACpbsvciV7e1BiXOEky8NwnP6BwgAVuZFXXbalG3tTqjZamJnhTLf/FIwDe1APeIF1WtLoT9qZflAKR/Eow2jUIKLtFZl+IJLgDDC21C4+dSdDZORET3adjLbKmvzYDfG0DPs30I+y+9IXpGgfGzk2i6vB6Nu+tjSxTR2ioNqGx1YurCDBZm/RCKgLOxArVdVbCWpL8nR7EoqN9eg7Ezib8+QhGo7UpvRieViroyVNSVQUqJsD+MoC8MRRGGN/QuJ6WE321s+SueqQszqGp1weqwGtrwbC3hSyitZaSDc64Vc3ABGF5ok9HCGhZmDBzRXU0A1hIr2ve3ZH9QACIhDeefjvYKWmFxRmX0nQlY7BbUbV0ZHEorHVkdU9NlDQh4g9H6K8tndEQ0MG091JHVjtVLgr4Qhn83GqutAwCqTUX9jlo07qpLa1+JECLpcnXycUT3udRsqcL8ZPLNy7ZSK8pqcr/xkszFSHBZuZSTO6mCi5kxvNCmkkm3YkUVqOmsRuPuuoxmNIyY7p+JnYpJZOzdCdR2Ved0g6hQBDoPtsM7OofJCzPwewLRei0tTtRtrYatNPuzTkFftHP26gaUWkjD6NvjCHgC2HKgzdC/WwiB8voyzI3PZzSWpf08VW0ujJ2dRHDxmHU8zVc2crMurWBkqaiQgotZZ10AhhfaZCx2FapNhbZ6hmMVq8OKXb+/FVKTsNgt6zoSbMTMRXfKa8KByIZsEBVCwNXshKs5+sIW8ocx1TuD88/2Qw9rsFfYUbu1GlUtLghl/W/ew78bTdo5e3bQg+r2yth4kokEI9HAkaGlo8+KqmDHBztx4YWL0WJ6i6tYUkYDXuu+Jh6TphVSBpdl/YRy7dLJouIMLgDDC20yQgjUba2JbnRNom5bDaz2janlAiBlmEr3umyZn/Kh57n+FcfIw4EI5id9mKqbwbYPbFlXB+twILxiqSguAUz2TKcML1JKXHjhIkIGauDEew7VqqKmsyp2k9Vhxc4PbYVveiG2gdpeYUN1exUsNlbYpbXSa4SY05EAKN7gAjC80CbUsLMW7mFPtADZ6jdNEd1HUp/mMeP1spXZDL3p2nK0WTieSEhDz/P9CU8fzU/6MNg9go5rWjN+joA38bJMjAQWPKk34fqmF6LVcI1YdULLYrNg2w1bYLGtfEkUQqC8tozHoSmpTDs451IxBxeA4YU2IdWqYseNWzHy1him+2ch9ei7mFAFajur0Xxlw7pmEzJR21WdcoOoo7IEDlfJBo0ImO6fhR5Ovqlvun8WLVc2ZtzvyWjnbMXA8tTskCd13R4RnVUTQiCwuJ/H1exEVZsr50uDVJwYXPKD4YU2FSkl/J4Awv4wyuvKoEU0uIe90b0tNhVWhzXlTEBgPojp3hn4vUEoqgJXcwWqWtf35lfV6sLE+ano/op4zy+A1r1NGT9+JjwjSTo+L5GAd2Ie1W2VGT1HaaUj9R4kAUP7XbQUQWvxoaCoClquTL/4HdFqqYLLkkTVbXOpmIMLwPBCm4h3bA5Db44i4Im/oTPsj0RnY/pmsOPGrWtqeEgpMfruxJrKvO4hD0beHMO2GzrTmhmRUl6q26IIbL+hExdfG4J7aDE0LD6H1WFBxzWtqKjf2M7KRjtLywxOcC1R1GhtmdF3Uu9BSsVelnqPkpTIWZ0e2lyMBBej/YRypViDC8DwQpuEe9iL3hcvGrp2qbv0tuu3rLh9qncmYWXecDCC88/04rKbdibdzKmFNUxemMHUhWmEFsIQqkBliwsNO2pRWuVA18EOBH0heEfnoGs6SpwlcDaW5+VIbmmlI/FM0DIlzvQq+a7WuKsefk8Q7tXLPov/5M4D7YaqBddsqUoZgoQq0u75RLTa0skigMElXxheqOjpuo6Lrw0Zv4OMztIE54Owl0ffNKUenXVJdp9IUMN0/wwadtTFvSQSjOC9p3tXNPyTmsTsoBuzg250vq8dVa0u2MtsafclyoXartSNEx3Okoy6Pq94GEWg831t8IxUYvLCNPxuP4SyWFtmW43hNge2UhsadtVh/GziCsEtVzZmrTcTbU7Lj0QzuOQPwwsVPc/wXEZHjOcmfbHw4ptZQMRAH5uZi+6E4WXg9HD0hNNqizMN/S8NouwjpbA5Nu6IdjKlVQ7U76jFxHtTaz+5WHG3/ZqWrMwKCSFQ2eJcd9+k5isaoFoVjJ2ZXHFKSrWpaLmyEbVd1esdKm1i2QouG1HdtpiDC8DwQptAwBvIqHv00ikkYP11WEIL4WjJ/WTPJyWmemfQfHmD8UHmWMueRthKrRg7O7kivJXXlqF1b9O6Z12yLdpxux5122rhHZtDJBiB1WGFs7EcisLTRJS5bAaXYg8WG4HhhYqalBLz0wsZdY8uq750pNFofZXV3Z+XzE8lPwa9ZG58Hiig8CKEQP32WtRtrcHCrB9aRIe93FaQm16llFiY8SMcjMDmsKKyxcny/ZQVRoPLJQwuucbwQkVt9J2J9PvcCMDhWrmXw+EqgaOyJGXH4poEyxJGGwVm2lAw14QiCroJ4czFWYy8Pb6i0J+9woaWPU2oNHDMmiiRdIJLsXZwLkScR6WiFQ6EMXY2+emTNQSgWBRsua5tzada9zXFTsDEu19ZTSmqWuKfZFk+i5PsucsLOCAUqsmeafS/MrSmQnFwLoTeFy5iZsCdn4GR6a3uVwQwuBQKhhcqWjMX3WkvFykWBfVba2ArXbtptqKuHNuu3xItZLdKVasL267fkrBRYUmFHeV1ZYnDDwBIoHZr/k8ZmUkkGMFQ90jSawZODydscUCUSLxGi5kGF8o+LhtR0QothCFEtDCZUXpYx9jZSUxfdGPH4c7YaaMlzoYKXPGRnZgbn0fAG4RQBVxNFbCVpt4D0nFNK8491ZOwg3Lr3ibDx4KXW15zIpcGx7ZsyPOkY7p/NuX3V4/omB3yoGZLVfILiRZlO7hw1iX7GF6oaKk2NZN9ugCiS049z/fjsqM71mz6FELA2VgBZ2NFWo9pL7dh15FtGH13AjMX3bHTTKXVDjTuql/XMeHl09q5sNA7iLbG/oILMIG5YMqTZEIg/hF1ojjSDy5RDC4bi+GFilZVq+tSRdx0yeieCe/4PFxphpRkbKU2dFzTitZ9zQgHwlAtCqwlmdd1WV6iPJdKu9oKMsAY6SclASgGG0DS5pZZcCllcMkD7nmhouVwlUQb+mX6viUAr5HmhBlQLQpKyu2mCC5Llp5ro5apjHA1VaTe1yQBVxPfRCg5Bhdz4cwLFbUtB9rQd+oivGPzl5YX0ihYp+uFeXQ5XlO4XAu/uVBwMzAVDeUocdqjy0IJunGX15QWXDE9KizxgksMg0tBYnihoqNHdMwMuuEZ8ULXJEqcdmzpqML8pA/hQATWEgssdhVjZxL3wAEASKDEabxL9EZJ2s02R/SJAVj3lBZcgBFCYOsHtuD8M70I+cJrPl9SYUfnwY41twfnQ5i8MA3PqBdSkyitcqBuWw3K68pY2G6TSRRclPp2BpcCxvBCRWXB7UfPs32IBC+V6Z+bmMeknEbj7jq0Xd0MIQR0TcdkzzS0cOJjjEIRqOmo3IBRG5e0m20OKfXtcQNMIbCX2bD7w9sxc9GNmYuzCAeiFXZrOqtR1eZasy/GPexB36mB6ETN4mxNyB9t31DTVYX2q7PTr4kKH4OLeTG8UNGIBCM4/0wftPCq/kKLb1BjZyZhdVhRt7UGiqqg/ZpW9J0aSPh4bVc1w2IvnP9FjFT6zJnevrgBpg35n30BANWiom5rDepS1MkJeAPoPTWwdolp8e/TvbNwVJSgfkdtbgZKBSPT4LKEwSW/CueVmWidpvpmUzZQHHt3ArVd1RBCoKrVBeX6LRh5cwx+z6Wy//ZyG5qvaERVW/xqufmQfm+V7JJdKPgAY8Rkz3TKa8bPTaJuew1nX4rYeoLLz3v9DC4FgOGFioaRMvDhQAS+6QWU15YBAFyNFXA2lCPgDSLsD8Nit8BRWVJQb1z5Di4xXZ2mDzAzQ56Um7XDgQj87gA3+RappMFlEYNL4eNRaSoaqWZdYtet2ucihIDDVQJnYwVKqxwMLnHEnmPxOZfGsDSm0q62gjpCHc/MoBta0NjPiK6zpHsxShlcujoZXEyCMy9UNOzlNoT9a0+crLmuLPPaKhsp/W62ubWrcjukfyLhDMzSmAtxBsYz4kX/S8Y3GNvKUrd7IHNicCkOnHmholHbVZ3ymtJqR0Eef14ts262ufyIPk+yGZhCLGIHAFJKDL05ltZ9gl62Eyg2SUsMpAguSxhcCgfDCxWNqlYXympKE1fUFdHmh4Uu06ZwuWXeAON3BxBMs7dRgOGlqKw3uJSolQwuBYbhhYqGUAS23bAl7ikhW5kN22/ojG3ULVSFGVyWmDPAGFlKXE2wF1LRYHApTtzzQkVFtajoPNCOlj1heMfmIBcr7Jqhcup6u9nm2p92ORB9YV+IjSXRHphSFE4V3kxq9aTbMZwKU6rgsoTBxXwYXqgo2RxW1Ham3gNTKLLVFC5XApobP+/1xwLMWfd57KrcbooAU1rtgK3UitCCgRkYAVS1VcLmMMembkrMSHARjnoGF5PishFRnmWrKVwuLT3HpVme0thYEi0hAYXRiVoIgeYrGw1dW15Thvb9LTkeEeVa0jYaDC5FgeGFKI+y1VtlI6QbYJbXgAHyG2Cq2yvRelUT4q4cCqCsphSdB9ux/YOdUC25f1kMzAXhGZvD/LQPUhZm53KzMlpiYOlnd3rEjld/HcQP/mUevh4ddnFpzxyDS+HishFRnmS7KVyuXX9UQYlamdYSknXPQMF0oq7fVovqtkpM988iMBeEoipwNVWgoqF8w/ZD+aYXMNg9goWZS/uUrCUWNF5WH2tbQZlLJ7gseCV++QOg723fpROKEph0zmC2rRwNjcn7ZFF+bUh4uf/++/H1r38dY2Nj2Lt3L77zne/guuuui3vtgw8+iE996lMrbrPb7QgEAnGvp81FC2uYueiG3xOAogo4mypQUb9xbz7ZYrZutuN+L557XMNVOzRMvyKwMBrCNyxhXH2tDVf/nsRZmCPAWOwWNOysy8tzz0360PNML1ZPtIQDEQy+PoKwP4zmK4wtb9Fa6RR1DAcl/vVrEtOji8F/2fck5NVR/q4XuqMMiqvwa0JtVjkPLz/72c9w7NgxPPDAAzhw4ADuu+8+HD16FOfOnUN9ffyy5k6nE+fOnYv93WxvTJQb0/2zGHh9GFKTsd+UJs5Po8Rpx9YPbIHdJFVRzdjNtr6kAnNvj6LvVDC62Lz4mv/y4yG8+mvgj+9SgKvNEWCWSCkxNz6PiZ5pLMwsQAiBisZy1G+rzXpfIyklLr46tCa4LDd2ZhLVHVUoqbBn9bk3g3SrUb/1ghVTw/F/IRYAIIFIzwxs+5tzOWxah5wv7v7jP/4j7rjjDnzqU5/CZZddhgceeAClpaX40Y9+lPA+Qgg0NjbGPhoaGnI9TCpw7mFv9MVfW3z1l4j9thSYC+K93/ZCCxvrW5NPZu1mqw14YZ9YLNy2fJVKAroGPHK/jpkxmXAPDICC2gMjpcTg6yPoea4f3rE5RIIawoEIZi66cfY3PZi8kLr7dDrmJ30I+ULJLxLAVO9MVp93M8ikGnX30ym+FwCkOwiZQY0g2hg5DS+hUAinT5/GkSNHLj2houDIkSM4depUwvvNz8+jo6MDbW1t+NjHPoZ33nkn4bXBYBBer3fFBxUXKSVG3kpS3l1GC5FN989u3KAyYNZutlKX0FJ07NZ14PRJFQDiBph4jRyB/AWYqd6ZS0Fh+WzI4p8HXx/B/LQva89nqGKvBPweLo+nI9OijpPjxvaJyUDh/0K0WeU0vExNTUHTtDUzJw0NDRgbi/9mtHPnTvzoRz/CL37xC/zP//k/oes6Dh06hKGhobjXnzhxAi6XK/bR1tYW9zoyL78ngICB8u7jZycLthuwmbvZyvkQEErxdZVA98shLK/CCxRmgJFSYvzcVMrrBl4bztpzGq3Yq7Cyr2GZBJeFeR33fWkOutHuD1YeyC1UBfedOXjwIG677Tbs27cPH/zgB/Hv//7vqKurw/e///241x8/fhwejyf2MThovHMsmUMkaOy3n3AgggvPXyzYAFOIwUVGdGhDXkTem0bkwgz0eDMEmrGjvHpk6Rh1YQeYkC+UegkH0dkSvzc7MyHOBmMVe11NPJprRCbBRdckHvjqPPwDxn6eRakFwiQd6DejnIaX2tpaqKqK8fHxFbePj4+jsdHYrnqr1YqrrroKPT09cT9vt9vhdDpXfFBxsZYY31c+Nz6P8bOpf6veSIXazTYy5EXouQFEzk1DG/JCu+hB+NURhF4bgQxdCoyi1MALuAAc9cuvK9wAo+vG66pMXcjOHhRbqTXacytJ01CLTUVVe2VWnq+YZdpG4ydPLCA4ipXLhEmoXVU8LFLAchpebDYb9u/fj5MnT8Zu03UdJ0+exMGDBw09hqZpeOutt9DUVPjdgCk3Spx2ONI4sjjZMw2Z4g1KSgnPqBd9Lw+i57k+DJwehm96IesFwwqtKZwMaYhcmEHw6X5o56YBfe0GaOkNIvzGaOxrKOwqRF2Sbt2L96++tgQlauWKInbL/22FEmDspTYIxdibkm96IWvP276/BWXVi6fGVj29alGx7YaNKZBnZutpo+H9nZ78Z3gZdUc11IbybA2bciDn/6ccO3YM//zP/4yHHnoIZ86cwZ133gmfzxer5XLbbbfh+PHjseu//OUv49e//jV6e3vx+uuv47/8l/+Cixcv4i/+4i9yPVQqUEIINO8xXv8iEowguJB4WSAciODsb3pw4fmLmB10wzs2j6m+GZx76gL6Tg1A17Kz7FRoTeFkIILQK8PQ+j3Jl4IkIOfD0CcubVi1bq8G1MQvF+EqK94assbGnTLAAHkLMIpFQXmdse7iRkOOEapVxY7DXdhyoA3ltWWwOqwocdrRfEUDLrt5R9aPZxeb9bbR0OdVQ7Mu6rYqWOJ0pqfCkvPw8vGPfxzf+MY38MUvfhH79u1Dd3c3Hn/88dgm3oGBAYyOjsaun52dxR133IHdu3fjD/7gD+D1evHiiy/isssuy/VQqYC5GivQ+b40NmMneJGSUuLC8/2XTnXIlde7h70YfGMk43EuKcSmcOG3JwCD+4cAQBuZi/1ZOKywXdsMUbPqDVYVUDtcKNvXAggRG3PKALOqD9JGB5jWvcbCsLMxu799C0Wgur0SOw534co/3IXLju5A4+56WDPofL2ZZKOoo7VCMbRiJExSL2qzE7LIGmt4vV64XC48/8efQbmVxZ7MbGHWj5kBNyLBCKwlFlR3VOHi6SEsTPuT3s9iV3HlH+6O+1uzd3wePc/2pXzuK/5wV8adhdPtrbIRwUWfDyH8cnqnZ0SpFbaDrWtul4EI9PkQhCIgXHaIZTMy4/5oqYLrj0ZvC2juxTYCABBdgtlVuT36OP4JoDf6vdAnBgAA4Tej1yz0Dua8iN2FF/vhGZ5L+HmhCFzxkZ2wlnDTZj5lqxr1Sw8HUHYu8fc7+uAKbB9oBwQgZwPQZ/yALiEqbFDqy1b8rFP2hfw+PPT3N8Hj8aTcv8rvBBUcPaLjwgv9OPubHkycn8LMgBvj703hzK/PG9pAV7etJuF0v3vIY2jd2z3kSXfYAAozuADRF+K02dW4N4sSC9TaUijVjjUv5ktjXt5vKdMZmFzPvnRc0wZHZZy9VAIQAug62M7gkmfZrEYdrrZCVNiS/v9v2VoFBCIIvzSM8Btj0AY80dN4704h9NwAtMns1f6h9WF4oYLT9/IgPKOLvyFJrNhM6ptagL0i8bRueV1Z0t41kZCWet1bAFo4/X0v2QguS7JfxyX9CVa1KbMlk+UBJl4naqAwAozFpmLnjVvRelUTSirsEIqAalVR21mNXR/eDlczTy7mU9arUZe6YL2qEWIpsApcCjICULdXQ6kvQ+j06KXKustee6BJRN6cgD6bfOaXNgYXWqmgLLj98Iwkr5IcnAuhZU9jrEEjAFgdVtRvr0HdthooSaZ27WXW6AtWsvdyCdjSrO+QreDy3BN6TgrQCWcaDeZEdI+LUm9sU2s8DQ5ntJnjEzquP7q2EzWwsLIbdRdifZD0iQFY95TGeiG1IXd9kBSLgvpttajfVpuTx6fM5KoatbCqsF3dBH0uCH1iAdD06M96YxmEVUWkzw2Eku8Li1yYhe0abq7ON868UEGZHTS2rCMlsPvD27H3lsuw549244qP7ETDzrqkwQUAajqrU05CKBYFVS3GTxuk0xQuamODCwAIpw2i3NhGRFFug/XqxnWv76czAwMgr0tIVDg2ohq1UmGHZWsVLDtqoLY5IazRJdLlm9QTkR72PCoEDC+Uc7qmG66fEglGDF03O+iGFtagWlVY7BbDxaRKKuyo3Vqd9JqWPY1QDNbbSLebbT6CCxA9bm65vA6wKAnDoahxwLq/CdZrmyGydPrFaIBZ3ciRAWZzynsbDYPNXWWqdhmUcwwvlBNaWMPomQm89X/OoPvf38Eb/9/buPDCRcxPJd/wZjV4wsfvDmTcSbrtqmY07Kpbs6lXtSpo39+Cuq01xh4ng262wMYHl9jYym2wXdcMpbF8RYARVSWwXt0I275GKJUlhoKglBL6bACRc9MIvzuJSJ8bMhA/eGYrwAD57URNGyOvbTSs8TepryZsfOvMN+55oayLhCJ477e9KzvpSsAz6oVnxIuOa1tRs6Uq7n1rOiox9u6EoefxewIYf28KzZc3pL54GSEEWq5sRMPOOnhGvdCCGqylVriaKlIuOy3JtJttvOCykYTDCutldZA7a6Jr+xYlNmVulAxpCP9uHNIbvBSCJKD1zkLtrITaWbkmAC3tgQGi//Zke2CkfyL6NVy1B6YUbVjoHURbY+72wFD+FEIbDbW5HFqvO+k1wmWHyLCMAmUP4yNl3eDrI/G7QC+uHF18dQjB+fhtXe3ldtR2JV/WWW6yZzrjkv4Wm4qajirU76hFVasrb8FlIztELxGqAuGwph9cdBltHbD0/V1+GgOA1ueGNhh/w3WDw7miiB2Q/gxMvjpRZ5MW0TE76MZEzxRmhzxZq+hsZoXSRkNtcQK25P9PWLri/+JFG4vhhbIqHAhjdsiTfFOsACaTNLxru6rZcIDRQhoiCZYrciHTpnCFFFzWQ59agJwPJ/3+an1uyARvyEYCDICiDDBSSoydncRb/+cM+l4axNAbo+g7NYA3f3kGE+enst5XyywKqY2GsKmw7m+CKF1clFh+nFoVsFxZD6WaJ40KAcMLZZVveiF1SREJzE3MJ/y0UATarmo2/JzZ7D+TzHqawgHmDy4AoI2mPo2BiB6tTJpAqgATr5EjYP4AM/ruBEbeGoMeWRns9IiOoe5RTLxXWN3QN0IhttFQSq2wvq8V1n0NUNucUFucsOyuhe36dqjrKB9A2cXwQlll+JfHFNcJRaCivizlsWlHZQksG9AXZr1N4YohuEhdGq/Um6LI33oDDJDdPkhaRENoIbwmWKyHlBKRkAYtoiPsD2PsTPK9XCNvjyMS2rhZxHxb/n0rlOCyRAgBpaYUlu01sOysgdpcwdYABYYbdimryoxMqQoY6upbv6MOcxPJTyclq6abLdnqrWLm4AIAWr87eTfq5VLsG1gSLWKnxN3Eu6KI3apNvNY9A7EiduvZxLsw68fYmQm4hxf36Qigqs2Fxt31cKRT2G8ZLaJh/MwkpvtnEV5c0rQ6LCkDu9QlZgc8qNtm7LSbmRV2NWoyA0ZJyipbqQ3OporkMyYSKWutAICrqQLNVyyeJFr+eIt/rt9Zi6oct65ncImSuoQ2lLzycYxVMbQvYHUfpHRnYNbbido7NodzJ3vgXl7RWUYLJZ77TU90CTQNUkpM9c3gzV+ewdjZyVhwAYCwP/WMihBA0BdK6znNqNCrUZM5MLxQ1rXvb4k2tEsQYFr2Nhn+rbZxdz123NiFyhYXLDYVqk2Fq8mJbTd0onVPk+HidJlgcLlE+sMpl4KWWLZVG96HlK8Ao0d09L00EF3mXD0jIgFdk+g9NZDWJtqRt8Yx8NowpNHZqbVPC9Va3C/JDC6ULcX9fwrlhc1hxa4j26J9hpZVqi2rKUXX+zvQsCO9PjLltWXoOtiOPR+7DHs/dhm2vr8DzobMmgYalc1utoC5g0taBKA2V6R1l9QBBlkPMLNDnpTNN8P+8KUGoSnMT/kwfm7S0LUJSaCyNbcziflkhjYaZB7c80I5YS2xoG1fM1r2NCIS0KBYBCw2c/24La+cC8RvCrfsagArK30uKYYXWuGwRlsLpNjQGuvYm6blReyAS4XsoqJF7C6NZXEPzKJ4e2BS8U0vpG7QKaLXVRroLj3RM5368ZIR0WXSTPfZFDqztNEg8+DMC+WUoiiwlVpNF1xoJaEIqK2pZ1TUtuJ6YzG6KumbMlAiYMUDr/xveW0ZtlzXlvByMzNbGw0yB76jEJEh6pZK6LMBSE/86shKSwWU2tK4nys05XVlmOpNXCgRACCNnYoDjIccIHq8v8RZgkggDKvDipotVSivK8vp/q18MWsbDSp8DC9EZIhQFVivaoQ26I22AAhFm2KKMivUdheUpnLTvAFXtjph6VYRCSZo7CkAe5kNFfXG9lY5Gysw1TdjaPal+YoGuJqKfwahGNpoUOFieCEiw4SqwLKlEmqHCwjp0YVni2Ka0LJEURR0vb8DPc/0QdflytAhANWioOtQh+F/V922mtQzOQDqttfA2ZjehmYz2uxtNCj3uOeFiNImhICwqxBW1XTBZUl5TRl2/f521HZWQ6jRf4NiUVC3rQa7P7wdDpfxzbMOVwna97dE/xLnyyFUgfZrWtC6N7fH+wsB22jQRuDMCxFtWiUVdrTvb0Hb1c2QuoRQRMbhorarGiVOOybem4JndA5Sl7CX21DbVR0tG7AJysuzjQZtFIYXItr0hBCx2Zf1KK8tQ3ltdJOvlLLoZ1mWK/SijtIfhjbkhTY6Hy24aFehNldAbXVCGGxnQYWD4YWIKAeSBRcpJTyjc/COzkHXJRxOO2q2VG1Ik9FcKPTgonuDCL8+urI3V1CD1ueGNjwH2zVN0VpGZBrm/D+FiMikAnNB9DzXj5AvdGl/jIy2F2i9qgl1W83VmLHQg4vUJcK/G0/cVDSsIfzWBKzXNm+qmTKzY3ihnJBSYmbAjcmeafjdAQhFwNlYgfrtNbFpdaLNJhKK4L2nexEJLjZqXPZ+KqXE4OsjsNgtqDJJmwAztNHQJ3yxY/1xSUDOhSC9QYg0NmlTfjG8UNZJXaL3xYsr+sJIXcI97IF7yIPWq5pQvy29/kYUfXOT8yHIoAZhUyEqbPxN0WSm+2YRCSTvMD3y9jgqW5wF/71dT3BZ3kYj15tz9Rm/odYN+owfCsOLaTC8UNaNnZ2I39Bu8cVj6I1RlFWXoqzaHNVYC4E26YPWMwO5cOmNT5RaoG6thlrPmSyzmO6fTXlNcC4IvyeA0krHBowoM0mDy6JCCC6GracvFeVF8Z/dow2l6zomzk8nv0gAE+enNmZARUAbnUfkzYkVwQUA5EIEkbcmoI2k7nwsdQl9egHa2Dx0dwC6rkNGdEiNZdc3UsKKvqtoyZY58ixlcOnqLKjgolTYUgcTCQinPafjoOzizAtlVcATTP3CKwHv2PzGDMjkZERH5FzyoBc5Nw2lvgzCsvZ3ESlltJx/nzthR2hRaYfaUQnVJH2JCoWu6Zgd9GC6fxZh/6U+RVVtroQ1XawOy6X9LklYC/zki1mCCwAojeVAzyygJ0kwdhVKTeHOdNFanHmhrJLS4Nyr0es2OX3cl/iUROwiCT1BGNR6Z6Gdn0kYXABAuoOI/G4ckX73Oka6uYQDYZx9sgcXXx3C/KQPwfkQ5id9uPjqEM7+pgfhQDju/Wq7qlM+dmmVAyUVhTkL0NbYn3FwWbLRS0XCqsJyWV3iCxQB6+X1Bb/HiFbizAtlVUmFHUIRkMl+yxHRF2hKTS4dp02WXwSg+0JYXWZL+sPQ+j2Gn0u7MAulqoSbFlOQUuLCCxcRmI/fXTswF0TviwPYcWPXmjfE6o4qTJyfQnA+lPB72rKnMdtDzor1BpcStTLt4KJPLyAy6IWcDQAAhMsOtdUJpa40rbChNpRB2BoR6XVDugOx20VtKSxdlVAKNCxSYgwvtC5SSnjH5i4diVYF7OU2BOaCid9wJVDH00bGKMZeoIWydhLVyF6YlQ8CaENehpcUfNMLWJjxJ75AXrqmrGblUpxqUbDjcBf6Tg1gfmoBEIvZVAKqTUXHta2GO1lvpFTBZUk2g0vkwsya8C1nA4jMBqA0l8OyqzatAKNUOWDb74AMRiDDeqw3F5kTwwtlTEqJ/lcGMTvgSWu3fnVHJVzNxd9ZNxuU2lJoF1PMnkhAqV07kyUX4i9dJHscfTaQ+rpNzjPiNTQb5h7xrgkvAGAtsWLHjVuxMOuHZ2wOUpNwuErgaqmAEieE5puR4CIc9VkNLtqkL+msoT4yD91VAnXZ64iU0e7gIkXgF3YLBCdaTI/hhTI2fnYyGlyAhC/ky5eQrKVWNOyoRd22Gq4vGyRcdogKG2SSZQZRboOoXDtbIvk1zgldk0ayC/Qk+4yA6NJpoS+fLp0sAjYuuACANpB6uTNyfgb6QhjCqkCfDUDO+KPfFJsKtXWxZxFnVooWwwtlRNd1jL+X+rhzeW0ZWvc1QSjR5SSGlvQIIWDd04DQ66OAP84plRILrHsb4n5dhUizdIUAlDghiFYqqbCn3G8uJVBi8qO3y49Eb2RwkbqEdMffT7RCRIceb1YypEHrdUMbnYftmmY2XSxShTdHSabgnw0YqkUxNzGPuYn56EZeBpeMiBILbAdaYNlZA+G0AXYVwmmDZWcNbAdaIEri/w6iewy8ASwnAbUtz8XCTKCqvTL10oQiUN1euTEDyoFsBJcleStAF4ggfGYyP89NOceZF8qInuw00SpD3aNwNTlhL7flcETFTagK1FYn1NY03gjSLHSmdlVys64BFpuK1n1NGHx9JOE1bVc1QzXpkkW2gstzT+gZBRehCIhSy5qijGmTgJzyQ/rDEA4rpD8c3QemCAhXScoASoVtQ2Ze7r//fmzZsgUlJSU4cOAAXnnllaTX/9u//Rt27dqFkpISXHnllfjVr361EcOkNJRUpBFEBDDVO5O7wVB8BqfLRYUNlj31sHRW5XhAxaNuaw22HGiDtXRlMTlbqRVbDrQZqudSiIwGl0uyG1yWqG3Za0ypjc8j9PooQi8OIdw9jvDrYwg9N4BI36zxulRUcHIeXn72s5/h2LFj+NKXvoTXX38de/fuxdGjRzExMRH3+hdffBH/6T/9J3z605/GG2+8gVtuuQW33HIL3n777VwPldJgLbGissXgi5MEfDMLuR0QraE2GThyW2GD7boWqHXr648kpYQ+F4Q24YM+409e5yeJpT0S8Vz6Tb8wVLdX4oo/2IkdN3ah82A7dtzYhcv/YKdpl4vSCS7R70VuggsAKM0VENXZmQXU+jyxOjExER1arxuRdycZYEwq5+HlH//xH3HHHXfgU5/6FC677DI88MADKC0txY9+9KO413/729/GTTfdhL/927/F7t278ZWvfAVXX301vvvd7+Z6qJSmlr1NUCxG65CYZ4p2+QkLIHrKIl5NCyD+i/iSZG/EG0FtcUZnX5J86a1b1z/bos/6EX5lBOFXRhB5awLhN8YQen4AkYuetN4Y4r3hXfqaRr/GKwLMqu/F0hsusPZ7mCtCCJTXlqGq1YXy2jLT7uta3a8IyF9wAaKvF9a9jVA7KwHrOt+mkgRpfcy3NtiQKeQ0vIRCIZw+fRpHjhy59ISKgiNHjuDUqVNx73Pq1KkV1wPA0aNHE15P+WMvs2HrBzpTXwjA2XBpFkBKCe/4HHpPDeDMk+dx/pleTPXOQEtxtHQjxGs6l+mLOJDfbrnCpsJ6dSOwtKFX4FKQUQQsV9RBiVOHJB36jB/hN8aiR7mXC+vQemag9aS3XNjgcMa+dktfy3gBJrZ0sfg9WfoeWfeUxr53GxVgzC7bP/PZIhQBS1cVbB9oh/VAC4QrB6e3BBAZ8mb/cSnnchpepqamoGkaGhoaVtze0NCAsbGxuPcZGxtL6/pgMAiv17vigzZORV1ZtOBckl84hSpQsyX6G76u6bjw/EX0PNsP97AHfncAcxM+DJwexruPvxetzJsn6b+IRxVicFmilNlgO9gKy94GqC1OKM0V0VNK17dDbVhfJVcpJcJnppKex9YGvNBXB5sUjAQYAAwwWWCGsC4UAaXcBsu26qSvM2uoBi6WWBu8yRRMf9roxIkTuPfee/M9jE2t45pWvPfb3rXBQ0Sn1bsOdcBij/6oDXaPwDu2WLZ+1ZteOBBGz7N9uOymHQm78uZKZsGltKCDyxIhRLRjdJa7RsvZABBIcSJEANrwHJSdNWk9djTAeHH9UQUlaiUCmhs/7/XjT7scAEpx1n0euyq3QzjqIf0T0e9Rbx+U+nboEwPRAIM2LPQOoq2xH4NjWzL+d+bLRgUvs4R1pbIElj0NiLw9kbJZqVLjgNJYjsg7qY9Km2lJmy7JaXipra2FqqoYHx9fcfv4+DgaG+M3H2tsbEzr+uPHj+PYsWOxv3u9XrS1tcW9lnLDYrdg54e2Yqp3BpMXZhBaCEG1KKhqq0Td9ho4nNGNd+FABNN9s4kfSAKhhTDcw94N3fQYL7jEmDy45JKh9gNysblkBtYbYIDo99TMAWb5HpRcMktYV2tLoVzfDn3cB90dgJwPQfrDQEQCCiBqy6BucUGtsENqenT2JVXQqV/fZnXKj5yGF5vNhv379+PkyZO45ZZbAEQrs548eRJ333133PscPHgQJ0+exD333BO77cknn8TBgwfjXm+322G3m7uSZTFQrSoadtahYWfi1vPesTlDJV/dQ54NCy+JgotS317QL+IFwci0fDrXJfDcE3pGAca6ZwDhNxdMGWDaGvvh6GzFyLtzeO/5GcwOBSAUgZbLK7DjA1VwNWWvHo/ZwrpQFajNFWv6Gi3fLC2lBHQJpdUZvwrvEkVAbWGfNTPK+dz8sWPH8M///M946KGHcObMGdx5553w+Xz41Kc+BQC47bbbcPz48dj1f/3Xf43HH38c3/zmN3H27Fn81//6X/Haa68lDDtkHlrEWNG0jdq4y+CyPkq1w9AehPUcw176mibbA7P0vYm3B2bpe2qmPTBtjf1wbGnFy/9rBM/88yDGzvkQmNPg90Rw4aVZ/Orrveh71Z2V5yqWn/ml4CKDGiLnZxB65iJCzw5Eg8tSvaPVP6uKiLbWsJt+98SmlPPv2sc//nFMTk7ii1/8IsbGxrBv3z48/vjjsU25AwMDKzqpHjp0CA8//DC+8IUv4POf/zy2b9+ORx99FFdccUWuh0o5VlJuYIZMRHvH5FqmwWVJob6IbyRht0BpLIc+Op/4IpsKpWF90/INDifG/d4kMzAomhmYpQ7OZ5+eRt+r0RkDuSzLL/35pf81gtrDlajeuv59TGYPLktkIILQayNAcNUvSUuVpstsQESDUBQo9aVQW5wJW2tQ4ROyyCr0eL1euFwuPP/Hn0G5lctJhURKibf/4yzC8RoMLrP7w9vhyGGZ+vUEl5/3+gv+RXwjSU1H+HfjCWtlKK1OWLoqs9Ldd9wfPUl4/dHoLzsBzQ0AiwEmWgRxV+X26Lj8i0Uwe/sAAPpENMAAwELvIAAUXIBZCi66LvHLr/bAP5N4T5FQBbb9UTve/+X963/iIgguABA6PQrpCSRemhaA7f1tnGkpYCG/Dw/9/U3weDxwOpP/jLExI20YIQTar25Jek3t1moGFxMRqgLrVY1Qd8Q/xqoPeRF6eTi6qXKdki8hrSxit3oJCUBBLyEtH8uCUyQNLgAgNYmBp0fX/8RFMsuo+0KQ7iTBBQAkoI3MbdiYKLcYQWlDuZqd2PqBLRjqHkFwWX0FxaKgYWcdGncn3vC7XkmDyyIGl8xoA97EbxxBDeE3x2G9rmXdFWiTLyGVAliIu4SkALEj1IW2hLS6LL92zmfofnoEa6oMZ6IYfual21h9KN3NarrFguGFNpyrqQLOxh3wTS8g6AtBtapwNpTntLZLyuDS1VkUL+L5oE/7U9Z7kfNhSHcQomr9s2rFFGDi9RNy7d0KxXoOejjxxnWhClTtbV7VJDFzm+Znvqg2SWxuXDaivFjqCVPTUYXKZueGFKVjcMkNfdpv6NSRnsXmnJkuIS2vwgvkdwkpUSNEu9OGzo/vhkhyxFxqErv/n/jlI9JVCD/zUtOhDXsR6h5D6PQowmenoKdRbdto6wClMndL0rSxGF6o6C1thATSDy5LGFySkNLQb7SGitqlIVGAiSrsAJOqg/P+//oBOJqcEJY4L9FCoO2PdmPLrXvWPY5CCC76XAihFwYROTsNOe2HdAegj8xFG32+N22ouadSboOotCcP0QKs6VJEeNqIitp6g0uJWsngkoI25EXk3HTqCx0W2A9lv2JsvFNIS0eok51CWqrCu9GnkFIFFyAathZGvPjNX/8vTD/aF6sSq7psaPrLy9Hy/14FZb3dlmPy9zMvwxpCp4aAJEtk6rYqWDoqUz+WP4zQa6NAWIsbpi2X10FtXF8/L8qtdE4bMbxQ0UoVXIDomwSDy/rIiI7QMxcNXWs71ArhsGZ9DGkHmGVHqIGNCzCZNEIMTy3Af34WwqKgbE8dlBwc9c3Xz3xkwAPtfIrO4xYFtuvbDfUgksEItAEPtOG5WOATtQ5YOiq5ZGQCPCpNmx6Dy8YRFsVwt1+Z5Dfs9Yi3hLS6E/WKJaQ4naiB3C4hZdrB2VpbCufBFlRc21RUwQUA9PEkBQ6XRPToMWgDhN0Cy/Ya2G7ogO2GdtgOd8C2t5HBpQgxvFDRWf7Gw+CyMYxWKhX29RerSyTbASabMg0uuZb3n3mDYVam2TJEKALCqkJscHd62jg8Kk1Fxeh+AgaX7FJaKqD1JOkYDkBUl+S8uunqY9QAVjRyXHOMugsrOlEvP0bdhuwcoU4/uCA27o2Q15/5UiuQouI2AAgH36poJf5EUNHIRnBZwuAS3QCpDc9Bnw0AkFAqS6L9YErX7llRmyugDc0BwUj8k0cCsGytzvmYgdUBZm0n6o0MMJkFl7Vl+XMpn2FdbalAZDp5SBPlNigb0O+MzIVzalQUshVcnntCZ3ABoI3OIfTiELSLHkhvENIbgjbgRejUELQh75rrhVWFZXctsHz5aGkfjE2FdV8jFOfGvQEtX0KK14kaWDnLkWwJKdP9L4kKIy5/vnwHlyW5/JnX50PQRuehjc9DrmqaqNSWQlQn2Y8iAMuOmujj+EKIXJhB+MwkIj0z0JdV6KbNhzMvZHpGg8slDC7J6O4AIu9OJfx85Nw0hMMKpSZ6mkf3hRA5MwXpWVlUTJTZoG5xQakrM3RSJNvSmYFZ3Yl6vTMwmfbQStQIMZdy9TMf9+dCAEpDGSw7ayEsCoQQsO5pQOS96Wh38uWzdg4LrLtqIVx2hN+ZgD7mW7ExXLvoiT7W7lrubdmEGF7I1NIJLss3QjK4JBa56Im+SSQpohC56IatxgF9IYzwa6NAnA2Vcj4EbdALpa4sd4NNIVsBBoDhNgLZDi5m/LmUiX4uJKCP+RD2hWHd3wShKtHmnrvrILdWQ59eADQJUWaFqCyBEALhdyejwWXx/svp4z5EAFivyE6bBDIPxlUyrdVvEgCDy3pJXUJOLaSsmCtnA5ARHZGeGUBLPDsgPUHoYwaOw66DjOjQphagjfviLiUYXUJa3Yl6+RKS0SPUDC5RkQspfi7mQtGZlmWETYXaVAG11QmlygEhBKQ/vOa61fRxH/QsV2+mwsfwQqaUdCPkomRHTzdyWt5UdOM1K2UgAjmZOujE2yOTDVKXiJyfQei5AUR+N47I2xMIvzyM0Gsja/riLA8BuQowmQaX1eMye3CRYQ36hIGfi+G5lI+ljRvssJ3jgEyFh+GFTCtb9TjM+iaRE6oAjJSdVwVkWEt9HbLf0wgApJQIvzUBbcCzJnBJTxDh10ahz62chWlwOBM0cgTWG2DWE1yKrfmnNHD0GTD4cxHSUhdAFIAMGftZpOLB8EJEMUIIqC2p3zjVlgoIq8GCc1naTCmlhDY+j9BrIwg91R9d3kpEl4i8t7bfkpEAAyCtAJM0uKx6vGIPLgCAeM0k49Elwm+OJw8edjV100+Z2+KHVJgYXohoBbXdCTgs8X/jFQDsKtSOSogy68qj0fEIQG1Y/4ZdKSUi704i8vbkmlNNCe/jDsT97T5VgInXiRpIHmAybf5ZdMEF0YJyotTYWRB9agHh06MJK+iqDcYaKbLh4ubD8EJEKwirCtv+ZojatSXqRbUDtmuaIWwqhBCwbKlM+XhK6/rfkPXhuUsnTtK5X4KlifUGGOBSYCn24CIjOiKDHoRODSH4dD+Czw0gcn4G0h//ayuEgGrg5yL64NHlI23QE/+xSixQ2lI06GupyEmzTypsDC9EtIawq7DtaYDt/W2wXFEHy+V1sB1qhW1f44o+RkpzOdQO1+KdVj2IImDd2wAlTkXedEgpERmI/+aWilCTb5jINMCs7oOUbnBZ/fwFG1xCGsKvjUB7byY6i6VJIKRBG/Ag9PIw9AQNE9WmCqidlYafRxtKvHnXsr16ZYBZ9i1VWitiRexoc2F4IaKERIkFakM51MbyuL/dCiFg2VYN67XNUJrKIcptEE471K5K2A61QqnJQoPBoGao/80aFgXClbh6a7xGjkD6ASaT4GKWHlrhdychfQk21moS4d+NJ17y2VIJpcPgvy2kQSY46SaEgHVHDWwfaIO6vRpqmwvqtmrY3t8G687avBRApPxjeCGidVOcdlh318F2oAW2a5th6azKeRPGVNQOV8o3ttQBBoYCzOr6QsvvZ9bgIhfCkCn6DiGixz2mrE0tIPTcAPSLBo/JC6Q8VSTsFljaXbBsr4alw2W4kzkVJ4YXIipsNtXY8e1lRGPZpeWsFJIHmJV9kOIFmGLtWq7PGOtqvfo63R1A5M3xuFWX4xKAUlcKITiDQsYxvBDRhpCBCHRvEDKQ3hKQUATUNDf9Wjoq03ozXE+AWf7nYgkuQHSvkaHrVi33RHpnUx9vXvEAgNpuLGgSLWF4IaKc0mf80dosLwwi/Gr0v6HTI9Bnjf1mDywuAaXTlVrTITUd2sgcIj0ziPTOrqm6u1rGAaYIgwsAKOXGvt5KxaXrZCACORt/E29cArBcXgclyd4kongYXogoZ7QJH8JvjK2pzSLdQYRfH4M2maTQ3DJCVWC9utHw8pHuCUTbBpyZgjbggdbvRviVEYROjyYtipZRgEHq4LL68c1AVNoN1WtRWypif06n0q3a4YLtYCtrtFBGGF6IKCekpiPy7mTSayLvTkImaeC3nFAVqClqfgAAym3Qzs9Gj/UC0SWMpT96Agi/MZrwZAuQfoAxElyWN/+UYQ2RvlkEXxhE8Km+aN2Unpm0l9NyTQgBy+X1QJJNz+qO6hUbZ4XNYKVbuwrLtmrWZ6GMMbwQUU7o475LASKRiA59InXxOX16AaFXR6D1upNfqCDaDycRCcj5cMrnTBRgolYGmNW3Jw0u/jBCLw9H/x2BSDRUhTRoFxfrpniNVQ/eKIrTDus1zRC1jhW3izIrLFfUw9K2cq+KKLFAuFIvN6nNFSmvIUqG4YWIckKfCxprqjcfSnqJNjKHcPc4ZKo3docF6o6a5OFl2WOmEi/AxGvkuLxredLgsthMMuH4Inq0bkoanb03glJhg21vI2wfaIP12mZYD7bCeqAlYdsHS1dV8ge0KiuWmogywfBCRDlh+LRPkutkUEPk7FTy+1sVWPY1wHaw1XizSINLNEYCjJHgAgDSG4ScCyU/iRPSDM1EZYMMRKB7ggnL/K8m7BYoTjuUUmvS761S7YDlyvpoh3JgZQ2XEgusVzflvQYQmR9/gogoJ0S1AxhMUaRMRt/sEtFG51Ifuw3rEIqAEMJ4eDG6NwPRADPu9+K5J3Rcf1RZDDBu/GmXA8mCy2qG6qYIQJ/153QTq+4JIHJhdsWpIFFhg9pVBTVOP6tMqPVlUKod0Mfnoc+Fot+bageUWgfruVBWcOaFiHJCqXEk7k4NAAIQpVaIqsTHZFMuFS3SvdGlJ1FpB+ypg0m6ey6SzcAkCi5rThZJpF5GW7a5OBf0GX+0i/Oq48xyLoTI78ajYTFLhEWB2uKEdVctLDtroLIQHWURwwsR5YQQ0caMCY83W1VY9zYkf0MzvPR06TktW5PsuRAAHBYoCfZrJLM6wABpBBcAosJuLJikMSuUDqlLhN+ZTDqGyJkpyLDx485E+cLwQkQ5o5TZYDvQCrWrEiixRI/dlligbq2C7UALRIqO00q1seJly5ee1KYKqDuqL81yLNtzIcqssF3dBKFm9tK3PMCUqJWxj6Xbll+zZoy1DkPBRL/oQej15PVoMqFPL6TezCwBbXRtryKiQsM9L0SUU8KmwtJZBUtnilMocSgN5UDPbOI+OQIQLjuUctuKmy1tLqgN5dBG5yEXQhCKEu2fU1Wy7qWL1XtggNTBBVicibqiDuE3xlLOwEh3AOHXR2G9tjlu0JJSQp/2QxvyRk9rKYDiLIFS7YBSVRK3foqcC0VDXKrnTnH6i6gQMLwQUcESFgXWPQ0Id48BUq5947VbYL28Pv59bSosBpszpmt5gFl+W0o2FbCqhmZApC8MfXR+TV8nqUtE3pmAPrGyOrHu90Vr6wAQVSWw7KhZGeoUkXrZykB3Z6JCwGUjIipoSlUJbAdaoLY4AcviS1aJGl16uq55RYXXRKSU0N0BaJML0OeChpsOJrM8rBgJLjKsIfz6GJDGnpJ49Wi0Pvea4LLmuWYDCL82An3ZLEqyU12X7ggo1ZdOHEldQpv0QRv0QhubhzTaKZoox3IaXmZmZvBnf/ZncDqdqKysxKc//WnMzydfTz18+HD0WN2yj8985jO5HCYRFThRaoVlZw3sH+yA/UOdsL+/HZYtlYaORmvDc9GmkKdHEXlzHOFXRhB+eRj6tPHGkIk0OJyG+xVpI/PRGZc0ctPqlgFS06ENeozdWZOInJuO/VVx2qPNLZPNrNhVKHXR8KKNziH0/AAib04g8t40Iu9MRvtFXZjJSvgjWo+chpc/+7M/wzvvvIMnn3wSjz32GJ599ln85V/+Zcr73XHHHRgdHY19/MM//EMuh0lERSrS744WuQuunO2QvjDC3cYbQ2aDnsEx5NXhTHqCqVsuLL/eHUDwZB9Cr41AG5+H5Yo6IFGBOIsC695GCEVEu3G/OwWEV8206BJav2dFKCLKh5zteTlz5gwef/xxvPrqq7jmmmsAAN/5znfwB3/wB/jGN76B5ubmhPctLS1FY2NjroZGRJuADESgXZhNek3kzCSUmnaIJM0Hszae1UHAAKVpZbG6TFsHSE8QEc8klIYyWK9tgj7qgz7sjZ5osijRE1qtFRB2S7Sh5vmZpI+nD89Bb3Wu2ShNtFFyNvNy6tQpVFZWxoILABw5cgSKouDll19Oet9//dd/RW1tLa644gocP34cCwsb99sRERUHI/2LENajR4gzIKVMa/nEyN6cFaxKLLzIsBY9WWSkSm8S+rgP+pgPlg4XbIfaYD+8BfYPtMOytSpWsl+fWkh8umuJyGwmiShbcjbzMjY2hvr6lacALBYLqqurMTY2lvB+//k//2d0dHSgubkZb775Jv7+7/8e586dw7//+7/HvT4YDCIYvFSF0+tNUY6ciDYF6TPQs0csXldn8DGlhD65AG3QA+mOvu4Ipx1qmxNKQ1nSY9hqSwUi6XSNDusIPz8IlFmBhbCxCr0GaAMeqG3OhGOV/kjqI9Vy8TqiPEk7vHzuc5/D1772taTXnDlzJuMBLd8Tc+WVV6KpqQkf+tCHcOHCBWzdunXN9SdOnMC9996b8fMRUZEyshQkDV6HaHCJvDcNfWjljIP0BhF5ZxLK9AIsl9UlDAVKQxnEgAdyKYjEEy80LA9h2dgnG9SijSnj1IIBosfTDR2ptvCwKuVP2uHls5/9LD75yU8mvaarqwuNjY2YmJhYcXskEsHMzExa+1kOHDgAAOjp6YkbXo4fP45jx47F/u71etHW1mb48YmoOCm1pdDHUleLVQw2I9QnFtYElxWfH/NBd5WsqcuyRKgKrFc3IfzOBOTMYm+hpbBiVaJhwGC36/WSMvEkjlJXCqTakCsBpT79FgtE2ZJ2eKmrq0NdXeo51oMHD8LtduP06dPYv38/AOCpp56CruuxQGJEd3c3AKCpqSnu5+12O+x2u+HHI6LNQakrjbYkSBIIRK0DSooWBUu0gdRHlLVBD5SWioSzL8KmwnZVE/T5UHSvjS4hymyATUXk9KihcaxRZl05O5OKKpLuvxF2C5TmcugjCYKfiLZZUGoM1I0hypGczfvt3r0bN910E+644w688soreOGFF3D33XfjE5/4ROyk0fDwMHbt2oVXXnkFAHDhwgV85StfwenTp9Hf349f/vKXuO2223DDDTdgz549uRoqERUhoQhYr2pM2E9IVNhgvczYZhepS0MdruVCZO3x4jiUchssHZWwdFZBrS+LHoHOhIjWb0lnL4za4kx5usqysxZisd7L6scWpVZY9zWyQzTlVU7bA/zrv/4r7r77bnzoQx+Coij4kz/5E/zTP/1T7PPhcBjnzp2LnSay2Wz4zW9+g/vuuw8+nw9tbW34kz/5E3zhC1/I5TCJqEgppVbYDrZCG52DPjoPGdYhSixQWyqg1Jfl5oj0RhZwk9FTTJbdtdG6LCmIcivUzsrU1ykC1ivrIb1BaCNzkP4IhFWF0lgGpaZ0Q46WEyWT0/BSXV2Nhx9+OOHnt2zZsuKoYVtbG5555plcDomINhlhUWBpcwFtmfc5EoqAKLVGN9smY1MNdY5eTXHZkWkPaaWhDEqZDaLEgki/+9J+muVUAbWlAmpnVXRDrgFCCAhXCRSXsc7eRBuJjRmJiAxQ25wpK8smO4KcjHDZIcqsxo53L6PUR4MLAChVDtiqHNH+Q5oOaVGiJ4t0CeGwxO1OTWRW/GkmIjJAaa6ASHIySVSWQG3PbHZHCAHLFfXRE0epss/i50VdKSyX1a79tEWJbrpVFSilVijlNgYXKjqceSEiMmBpH4g26I02R1zql2RToLY6oXZUrmsviFJug+26ZkT63NEj3osr6qLGET05NR+GDGsQNhVKY3l0oy7RJsXwQkRkkFAELB0uqO3OS0ew7ZasbWAVDiusl9VB7qyJnlqyKIb3qBBtJgwvRERpEkIkrFCblcdXFYBLPUQJ8f8OIiIiMhWGFyIiIjIVhhciIiIyFYYXIiIiMhWGFyIiIjIVnjYiIkqTDESge6Jl+BWnHSKHJ4+IaC2GFyIig2RIQ+TsFPTJhRW3i2oHrLtrIUr4kkq0EbhsRERkgIzoCJ8ehT61sPZzs36EXhuBDEbyMDKizYfhhYjIAG3QE+0qLeN8UgIIadD6PRs9LKJNieGFiMgAbWgu+QUS0EbnIPV46YaIsonhhUxroXcw30OgTULqEghpqS/UDF5HROvC8EKmNDi2BcDKAKNPDKy4Rvonlv1t5T6FgOYGAFx/VMG435uLIVIxEYsfRqjZadJIRIkxvJBpLQ8w4Tej4SQWYHr7AEQDzK7K7Yv3iF7zp10OAAwwZJwQAqK2NGWAES47hFXdmEERbWIML2Rq2QowABhgKClLhyv+Zt1l1C2VGzIWos2O4YVMbynAAMg4wFx/NPq/AgMMJaK4SmC5vG7t7Mvi3y07qqHWlm74uIg2I4YXKgqDY1ti+18YYChX1MZy2A62Qu1wQThtEBU2qK1OWN/XArXNle/hEW0aDC9UNBhgaCMIhxWWbdWwXdsC23UtsOyogVJmy/ewiDYVhhcqKkYCDAAGGCIiE2N4oaKTKsAsHaFmgCEiMieGFypK6w0wABhgiIgKFMMLFbVMA8zyGjAAAwwRUSFheKGitboKLwMMEVFxYHihopYywAAMMEREJsPwQkUvaYBZdoQaYIAhIjIDhhfaFNYTYAAwwBARFRCGF9o0Mg0w8Ro5AgwwRET5wvBCmwoDDBGR+TG80KbDAENEZG4ML7QpJQowABhgiIgKHMMLbVrxAky8Ro4AAwwRUSFheKFNLbMAAwYYIqI8YnihTS/9ALO2kSPAAENEtFEYXoiQ/QBDRES5w1daokWrAwyAjAMMZ1+IiHInZ+Hlq1/9Kg4dOoTS0lJUVlYauo+UEl/84hfR1NQEh8OBI0eO4Pz587kaItEaywNMvEaOAAMMEVG+5Sy8hEIh3HrrrbjzzjsN3+cf/uEf8E//9E944IEH8PLLL6OsrAxHjx5FIBDI1TCJ1lgKMED8TtRrrQwwSxhgiIhyI2fh5d5778Xf/M3f4MorrzR0vZQS9913H77whS/gYx/7GPbs2YOf/OQnGBkZwaOPPpqrYRKltKIGzCrLTyAREdHGKJg9L319fRgbG8ORI0dit7lcLhw4cACnTp1KeL9gMAiv17vig4iIiIpXwYSXsbExAEBDQ8OK2xsaGmKfi+fEiRNwuVyxj7a2tpyOk4iIiPIrrfDyuc99DkKIpB9nz57N1VjjOn78ODweT+xjcHAw9Z2IiIjItCzpXPzZz34Wn/zkJ5Ne09XVldFAGhsbAQDj4+NoamqK3T4+Po59+/YlvJ/dbofdbs/oOYmIiMh80govdXV1qKury8lAOjs70djYiJMnT8bCitfrxcsvv5zWiSUiIiIqbjnb8zIwMIDu7m4MDAxA0zR0d3eju7sb8/PzsWt27dqFRx55BAAghMA999yD//bf/ht++ctf4q233sJtt92G5uZm3HLLLbkaJhEREZlMWjMv6fjiF7+Ihx56KPb3q666CgDw29/+FocPHwYAnDt3Dh6PJ3bN3/3d38Hn8+Ev//Iv4Xa78YEPfACPP/44SkpKcjVMIiIiMpmchZcHH3wQDz74YNJrpJQr/i6EwJe//GV8+ctfztWwiIiIyOQK5qg0ERERkREML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqOQsvX/3qV3Ho0CGUlpaisrLS0H0++clPQgix4uOmm27K1RCJiIjIhCy5euBQKIRbb70VBw8exP/4H//D8P1uuukm/PjHP4793W6352J4REREZFI5Cy/33nsvAODBBx9M6352ux2NjY05GBEREREVg4Lb8/L000+jvr4eO3fuxJ133onp6emk1weDQXi93hUfREREVLwKKrzcdNNN+MlPfoKTJ0/ia1/7Gp555hncfPPN0DQt4X1OnDgBl8sV+2hra9vAERMREdFGSyu8fO5zn1uzoXb1x9mzZzMezCc+8Qn80R/9Ea688krccssteOyxx/Dqq6/i6aefTnif48ePw+PxxD4GBwczfn4iIiIqfGntefnsZz+LT37yk0mv6erqWs941jxWbW0tenp68KEPfSjuNXa7nZt6iYiINpG0wktdXR3q6upyNZY1hoaGMD09jaampg17TiIiIipsOdvzMjAwgO7ubgwMDEDTNHR3d6O7uxvz8/Oxa3bt2oVHHnkEADA/P4+//du/xUsvvYT+/n6cPHkSH/vYx7Bt2zYcPXo0V8MkIiIik8nZUekvfvGLeOihh2J/v+qqqwAAv/3tb3H48GEAwLlz5+DxeAAAqqrizTffxEMPPQS3243m5mZ8+MMfxle+8hUuCxEREVFMzsLLgw8+mLLGi5Qy9meHw4EnnngiV8MhIiKiIlFQR6WJiIiIUmF4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlNheCEiIiJTYXghIiIiU2F4ISIiIlOx5HsA2SalBAD4wqE8j4TMzBsMIxK49DNk9alQ5v2Ln1yACM/HPjfvXYj9OTDvB1Rr7O/hgEQIau4HTERkcqGAD8Cl9/FkhDRylYkMDQ2hra0t38MgIiKiDAwODqK1tTXpNUUXXnRdx8jICCoqKiCESHm91+tFW1sbBgcH4XQ6N2CEBPDrnk/82ucHv+75w699fqT7dZdSYm5uDs3NzVCU5Ltaim7ZSFGUlIktHqfTyR/qPODXPX/4tc8Pft3zh1/7/Ejn6+5yuQxdxw27REREZCoML0RERGQqmz682O12fOlLX4Ldbs/3UDYVft3zh1/7/ODXPX/4tc+PXH7di27DLhERERW3TT/zQkRERObC8EJERESmwvBCREREpsLwQkRERKbC8LKov78fn/70p9HZ2QmHw4GtW7fiS1/6EkIh9kjaCF/96ldx6NAhlJaWorKyMt/DKVr3338/tmzZgpKSEhw4cACvvPJKvoe0KTz77LP46Ec/iubmZggh8Oijj+Z7SJvCiRMncO2116KiogL19fW45ZZbcO7cuXwPq+h973vfw549e2LF6Q4ePIj/+3//b1afg+Fl0dmzZ6HrOr7//e/jnXfewbe+9S088MAD+PznP5/voW0KoVAIt956K+688858D6Vo/exnP8OxY8fwpS99Ca+//jr27t2Lo0ePYmJiIt9DK3o+nw979+7F/fffn++hbCrPPPMM7rrrLrz00kt48sknEQ6H8eEPfxg+ny/fQytqra2t+O///b/j9OnTeO211/B7v/d7+NjHPoZ33nkna8/Bo9JJfP3rX8f3vvc99Pb25nsom8aDDz6Ie+65B263O99DKToHDhzAtddei+9+97sAon3A2tra8Fd/9Vf43Oc+l+fRbR5CCDzyyCO45ZZb8j2UTWdychL19fV45plncMMNN+R7OJtKdXU1vv71r+PTn/50Vh6PMy9JeDweVFdX53sYROsWCoVw+vRpHDlyJHaboig4cuQITp06lceREW0cj8cDAHxd30CapuGnP/0pfD4fDh48mLXHLbrGjNnS09OD73znO/jGN76R76EQrdvU1BQ0TUNDQ8OK2xsaGnD27Nk8jYpo4+i6jnvuuQfvf//7ccUVV+R7OEXvrbfewsGDBxEIBFBeXo5HHnkEl112WdYev+hnXj73uc9BCJH0Y/WL9/DwMG666SbceuutuOOOO/I0cvPL5GtPRJQLd911F95++2389Kc/zfdQNoWdO3eiu7sbL7/8Mu68807cfvvtePfdd7P2+EU/8/LZz34Wn/zkJ5Ne09XVFfvzyMgIbrzxRhw6dAg/+MEPcjy64pbu155yp7a2FqqqYnx8fMXt4+PjaGxszNOoiDbG3XffjcceewzPPvssWltb8z2cTcFms2Hbtm0AgP379+PVV1/Ft7/9bXz/+9/PyuMXfXipq6tDXV2doWuHh4dx4403Yv/+/fjxj38MRSn6iamcSudrT7lls9mwf/9+nDx5MrZRVNd1nDx5EnfffXd+B0eUI1JK/NVf/RUeeeQRPP300+js7Mz3kDYtXdcRDAaz9nhFH16MGh4exuHDh9HR0YFvfOMbmJycjH2Ov5nm3sDAAGZmZjAwMABN09Dd3Q0A2LZtG8rLy/M7uCJx7Ngx3H777bjmmmtw3XXX4b777oPP58OnPvWpfA+t6M3Pz6Onpyf2976+PnR3d6O6uhrt7e15HFlxu+uuu/Dwww/jF7/4BSoqKjA2NgYAcLlccDgceR5d8Tp+/DhuvvlmtLe3Y25uDg8//DCefvppPPHEE9l7EklSSil//OMfSwBxPyj3br/99rhf+9/+9rf5HlpR+c53viPb29ulzWaT1113nXzppZfyPaRN4be//W3cn+/bb78930Mraole03/84x/ne2hF7c///M9lR0eHtNlssq6uTn7oQx+Sv/71r7P6HKzzQkRERKbCTR1ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQqDC9ERERkKgwvREREZCoML0RERGQq/z/m9RNT/1IhsAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "h = 0.25\n", - "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", - "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", - "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", - " np.arange(y_min, y_max, h))\n", - "Xmesh = np.c_[xx.ravel(), yy.ravel()]\n", - "inputs = [list(map(Value, xrow)) for xrow in Xmesh]\n", - "scores = list(map(model, inputs))\n", - "Z = np.array([s.data > 0 for s in scores])\n", - "Z = Z.reshape(xx.shape)\n", - "\n", - "fig = plt.figure()\n", - "plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n", - "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n", - "plt.xlim(xx.min(), xx.max())\n", - "plt.ylim(yy.min(), yy.max())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - 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f"Value(data={self.data}, grad={self.grad})" \ No newline at end of file diff --git a/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py b/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py deleted file mode 100644 index 7ec8e60b..00000000 --- a/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py +++ /dev/null @@ -1,58 +0,0 @@ -from micrograd_ir.engine import Value -import random - -class Module: - - def zero_grad(self): - for p in self.parameters(): - p.grad = 0 - - def parameters(self): - return [] - - -class Neuron(Module): - - def __init__(self, nin): - self.w = [Value(random.uniform(-1,1)) for _ in range(nin)] - self.b = Value(random.uniform(-1,1)) - - def __call__(self, x): - # w * x + b - act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b) - out = act.tanh() - return out - - def parameters(self): - return self.w + [self.b] - - -class Layer(Module): - - def __init__(self, nin, nout): - self.neurons = [Neuron(nin) for _ in range(nout)] - - def __call__(self, x): - outs = [n(x) for n in self.neurons] - return outs[0] if len(outs) == 1 else outs - - def parameters(self): - return [p for neuron in self.neurons for p in neuron.parameters()] - - -class MLP(Module): - - def __init__(self, nin, nouts): - sz = [nin] + nouts - self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))] - - def __call__(self, x): - for layer in self.layers: - x = layer(x) - return x - - def parameters(self): - return [p for layer in self.layers for p in layer.parameters()] - - def __repr__(self): - return f"MLP of [{', '.join(str(layer) for layer in self.layers)}]" \ No newline at end of file diff --git a/my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc b/my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc deleted file mode 100644 index d951a3e08997fcea2e7dd9003cd1e11fc55b0f9a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4168 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torch.Tensor([-4.0]).double() - x.requires_grad = True - z = 2 * x + 2 + x - q = z.relu() + z * x - h = (z * z).relu() - y = h + q + q * x - y.backward() - xpt, ypt = x, y - - # forward pass went well - assert ymg.data == ypt.data.item() - # backward pass went well - assert xmg.grad == xpt.grad.item() - -def test_more_ops(): - - a = Value(-4.0) - b = Value(2.0) - c = a + b - d = a * b + b**3 - c += c + 1 - c += 1 + c + (-a) - d += d * 2 + (b + a).relu() - d += 3 * d + (b - a).relu() - e = c - d - f = e**2 - g = f / 2.0 - g += 10.0 / f - g.backward() - amg, bmg, gmg = a, b, g - - a = torch.Tensor([-4.0]).double() - b = torch.Tensor([2.0]).double() - a.requires_grad = True - b.requires_grad = True - c = a + b - d = a * b + b**3 - c = c + c + 1 - c = c + 1 + c + (-a) - d = d + d * 2 + (b + a).relu() - d = d + 3 * d + (b - a).relu() - e = c - d - f = e**2 - g = f / 2.0 - g = g + 10.0 / f - g.backward() - apt, bpt, gpt = a, b, g - - tol = 1e-6 - # forward pass went well - assert 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"view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JnGHatCI51JP" - }, - "source": [ - "# micrograd exercises\n", - "\n", - "1. watch the [micrograd video](https://www.youtube.com/watch?v=VMj-3S1tku0) on YouTube\n", - "2. come back and complete these exercises to level up :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OFt6NKOz6iBZ" - }, - "source": [ - "## section 1: derivatives" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "3Jx9fCXl5xHd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.336362190988558\n" - ] - } - ], - "source": [ - "# here is a mathematical expression that takes 3 inputs and produces one output\n", - "from math import sin, cos\n", - "\n", - "def f(a, b, c):\n", - " return -a**3 + sin(3*b) - 1.0/c + b**2.5 - a**0.5\n", - "\n", - "print(f(2, 3, 4))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "qXaH59eL9zxf" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OK for dim 0: expected -12.353553390593273, yours returns -12.353553390593273\n", - "OK for dim 1: expected 10.25699027111255, yours returns 10.25699027111255\n", - "OK for dim 2: expected 0.0625, yours returns 0.0625\n" - ] - } - ], - "source": [ - "# write the function df that returns the analytical gradient of f\n", - "# i.e. use your skills from calculus to take the derivative, then implement the formula\n", - "# if you do not calculus then feel free to ask wolframalpha, e.g.:\n", - "# https://www.wolframalpha.com/input?i=d%2Fda%28sin%283*a%29%29%29\n", - "\n", - "def gradf(a, b, c):\n", - " df_da = -3*a**2 - 0.5*a**-0.5\n", - " df_db = 3*cos(3*b) + 2.5*b**1.5\n", - " df_dc = c**-2\n", - " return [df_da, df_db, df_dc] # todo, return [df/da, df/db, df/dc]\n", - "\n", - "# expected answer is the list of \n", - "ans = [-12.353553390593273, 10.25699027111255, 0.0625]\n", - "yours = gradf(2, 3, 4)\n", - "for dim in range(3):\n", - " ok = 'OK' if abs(yours[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", - " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {yours[dim]}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "_27n-KTA9Qla" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OK for dim 0: expected -12.353553390593273, yours returns -12.35355817641448\n", - "OK for dim 1: expected 10.25699027111255, yours returns 10.256986371359744\n", - "OK for dim 2: expected 0.0625, yours returns 0.06250111539429781\n" - ] - } - ], - "source": [ - "# now estimate the gradient numerically without any calculus, using\n", - "# the approximation we used in the video.\n", - "# you should not call the function df from the last cell\n", - "\n", - "# -----------\n", - "h = 1e-10\n", - "df_da = (f(2+h, 3, 4) - f(2, 3, 4)) / h\n", - "df_db = (f(2, 3+h, 4) - f(2, 3, 4)) / h\n", - "df_dc = (f(2, 3, 4+h) - f(2, 3, 4)) / h\n", - "numerical_grad = [df_da, df_db, df_dc] # TODO\n", - "# -----------\n", - "\n", - "for dim in range(3):\n", - " ok = 'OK' if abs(numerical_grad[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", - " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad[dim]}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "BUqsGb5o_h2P" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OK for dim 0: expected -12.353553390593273, yours returns -12.353553735522382\n", - "OK for dim 1: expected 10.25699027111255, yours returns 10.256990812251843\n", - "OK for dim 2: expected 0.0625, yours returns 0.06250111539429781\n" - ] - } - ], - "source": [ - "# there is an alternative formula that provides a much better numerical \n", - "# approximation to the derivative of a function.\n", - "# learn about it here: https://en.wikipedia.org/wiki/Symmetric_derivative\n", - "# implement it. confirm that for the same step size h this version gives a\n", - "# better approximation.\n", - "\n", - "# -----------\n", - "h = 1e-10\n", - "df_da = (f(2+h, 3, 4) - f(2-h, 3, 4)) / (2*h)\n", - "df_db = (f(2, 3+h, 4) - f(2, 3-h, 4)) / (2*h)\n", - "df_dc = (f(2, 3, 4+h) - f(2, 3, 4-h)) / (2*h)\n", - "numerical_grad2 = [df_da, df_db, df_dc] # TODO\n", - "# -----------\n", - "\n", - "for dim in range(3):\n", - " ok = 'OK' if abs(numerical_grad2[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", - " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad2[dim]}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tklF9s_4AtlI" - }, - "source": [ - "## section 2: support for softmax" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "nAPe_RVrCTeO" - }, - "outputs": [], - "source": [ - "# Value class starter code, with many functions taken out\n", - "from math import exp, log\n", - "\n", - "class Value:\n", - " \n", - " def __init__(self, data, _children=(), _op='', label=''):\n", - " self.data = data\n", - " self.grad = 0.0\n", - " self._backward = lambda: None\n", - " self._prev = set(_children)\n", - " self._op = _op\n", - " self.label = label\n", - "\n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - " \n", - " def __add__(self, other): # exactly as in the video\n", - " other = other if isinstance(other, Value) else Value(other)\n", - " out = Value(self.data + other.data, (self, other), '+')\n", - " \n", - " def _backward():\n", - " self.grad += 1.0 * out.grad\n", - " other.grad += 1.0 * out.grad\n", - " out._backward = _backward\n", - " \n", - " return out\n", - " \n", - " def __radd__(self, other):\n", - " return other + self.data\n", - "\n", - " def __mul__(self, other):\n", - " other = other if isinstance(other, Value) else Value(other)\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad += other.data * out.grad\n", - " other.grad += self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " def log(self):\n", - " out = Value(log(self.data), (self, ), \"log\")\n", - " def _backward():\n", - " self.grad += (1/out.data) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - " \n", - " def __rmul__(self, other): #other * self\n", - " return self * other\n", - "\n", - " def __truediv__(self, other):\n", - " return self * (other**-1)\n", - "\n", - " def exp(self):\n", - " out = Value(exp(self.data), (self,), 'exp')\n", - "\n", - " def _backward():\n", - " self.grad += out.data * out.grad\n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " \n", - " # ------\n", - " # re-implement all the other functions needed for the exercises below\n", - " # your code here\n", - " # TODO\n", - " # ------\n", - "\n", - " def backward(self): # exactly as in video \n", - " topo = []\n", - " visited = set()\n", - " def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)\n", - " build_topo(self)\n", - " \n", - " self.grad = 1.0\n", - " for node in reversed(topo):\n", - " node._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "\n", - "class Value:\n", - "\n", - " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", - " self.data = data\n", - " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", - " self._prev = set(_children)\n", - " self._backward = lambda: None\n", - " self._op = _op\n", - " self.label = label\n", - " \n", - " \n", - " def __repr__(self):\n", - " return f\"Value(data={self.data})\"\n", - "\n", - " \n", - " def __add__(self, other):\n", - " other = other if isinstance(other, Value) else Value(other) # now, we can add integer directly\n", - " out = Value(self.data + other.data, (self, other), \"+\")\n", - " \n", - " def _backward():\n", - " self.grad += 1.0 * out.grad\n", - " other.grad += 1.0 * out.grad\n", - "\n", - " # this means the gradient is just copied as it is as \n", - " # we have seen it happening for addition \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - " \n", - " def __radd__(self, other):\n", - " return self + other\n", - "\n", - " \n", - " def __mul__(self, other):\n", - " other = other if isinstance(other, Value) else Value(other)\n", - " out = Value(self.data * other.data, (self, other), \"*\")\n", - " \n", - " def _backward():\n", - " self.grad += other.data * out.grad\n", - " other.grad += self.data * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - "\n", - " def __pow__(self, other):\n", - " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", - " out = Value(self.data**other, (self,), f'**{other}')\n", - "\n", - " def _backward():\n", - " self.grad += (other*self.data**(other - 1)) * out.grad\n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - " \n", - "\n", - " def __rmul__(self, other): #other * self\n", - " return self * other\n", - "\n", - " \n", - " def __truediv__(self, other):\n", - " return self * (other**-1)\n", - "\n", - " \n", - " def __sub__(self, other):\n", - " return self + (-other) \n", - " \n", - " def __neg__(self):\n", - " return -1*self\n", - "\n", - " def exp(self):\n", - " x = self.data\n", - " out = Value(math.exp(x), (self, ), \"exp\")\n", - "\n", - " def _backward():\n", - " self.grad += out.data * out.grad # exp(x) * out.grad\n", - " \n", - " out._backward = _backward\n", - " \n", - " return out\n", - "\n", - "\n", - " def tanh(self):\n", - " x = self.data\n", - " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", - " out = Value(t, (self, ), \"tanh\")\n", - "\n", - " def _backward():\n", - " self.grad += (1 - t**2) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out \n", - " \n", - " def log(self):\n", - " out = Value(log(self.data), (self, ), \"log\")\n", - " def _backward():\n", - " self.grad += (1/self.data) * out.grad \n", - " \n", - " out._backward = _backward\n", - "\n", - " return out\n", - "\n", - " \n", - " def backward(self):\n", - " topo = []\n", - " visited = set()\n", - "\n", - " def build_topo(v):\n", - " if v not in visited:\n", - " visited.add(v)\n", - " for child in v._prev:\n", - " build_topo(child)\n", - " topo.append(v)\n", - " \n", - " build_topo(self)\n", - "\n", - " self.grad = 1.0\n", - " for node in reversed(topo):\n", - " node._backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "VgWvwVQNAvnI" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.1755153626167147\n", - "[Value(data=0.0), Value(data=3.0), Value(data=-2.0), Value(data=1.0)]\n", - "OK for dim 0: expected 0.041772570515350445, yours returns 0.041772570515350445\n", - "OK for dim 1: expected 0.8390245074625319, yours returns 0.8390245074625319\n", - "OK for dim 2: expected 0.005653302662216329, yours returns 0.005653302662216329\n", - "OK for dim 3: expected -0.8864503806400986, yours returns -0.8864503806400986\n" - ] - } - ], - "source": [ - "# without referencing our code/video __too__ much, make this cell work\n", - "# you'll have to implement (in some cases re-implemented) a number of functions\n", - "# of the Value object, similar to what we've seen in the video.\n", - "# instead of the squared error loss this implements the negative log likelihood\n", - "# loss, which is very often used in classification.\n", - "\n", - "# this is the softmax function\n", - "# https://en.wikipedia.org/wiki/Softmax_function\n", - "def softmax(logits):\n", - " counts = [logit.exp() for logit in logits]\n", - " denominator = sum(counts)\n", - " out = [c / denominator for c in counts]\n", - " return out\n", - "\n", - "# this is the negative log likelihood loss function, pervasive in classification\n", - "logits = [Value(0.0), Value(3.0), Value(-2.0), Value(1.0)]\n", - "probs = softmax(logits)\n", - "loss = -probs[3].log() # dim 3 acts as the label for this input example\n", - "loss.backward()\n", - "print(loss.data)\n", - "\n", - "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", - "\n", - "print(logits)\n", - "\n", - "logits_grad_value = []\n", - "for dim in range(4):\n", - " ok = 'OK' if abs(logits[dim].grad - ans[dim]) < 1e-5 else 'WRONG!'\n", - " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits[dim].grad}\")\n", - " logits_grad_value.append(logits[dim].grad)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "q7ca1SVAGG1S" - }, - "outputs": [], - "source": [ - "# verify the gradient using the torch library\n", - "# torch should give you the exact same gradient\n", - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "a = torch.Tensor([0.0]).double() ; a.requires_grad = True\n", - "b = torch.Tensor([3.0]).double() ; b.requires_grad = True\n", - "c = torch.Tensor([-2.0]).double() ; c.requires_grad = True\n", - "d = torch.Tensor([1.0]).double() ; d.requires_grad = True\n", - "\n", - "logits = torch.Tensor([a, b, c, d]); logits.requires_grad = True" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([0.0418, 0.8390, 0.0057, 0.1135], grad_fn=)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "probs = torch.nn.functional.softmax(logits, dim=-1)\n", - "probs" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(2.1755)\n" - ] - } - ], - "source": [ - "loss = -1*probs[3].log() # dim 3 acts as the label for this input example\n", - "loss.backward()\n", - "print(loss.data)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OK for dim 0: expected 0.041772570515350445, yours returns 0.041772566735744476\n", - "OK for dim 1: expected 0.8390245074625319, yours returns 0.8390244841575623\n", - "OK for dim 2: expected 0.005653302662216329, yours returns 0.005653302650898695\n", - "OK for dim 3: expected -0.8864503806400986, yours returns -0.8864504098892212\n" - ] - } - ], - "source": [ - "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", - "logits_grad_tensor = []\n", - "for dim in range(4):\n", - " ok = 'OK' if abs(logits.grad[dim].item() - ans[dim]) < 1e-5 else 'WRONG!'\n", - " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits.grad[dim].item()}\")\n", - " logits_grad_tensor.append(logits.grad[dim].item())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.7796059684791494e-09\n", - "2.330496962077433e-08\n", - "1.1317634329710557e-11\n", - "2.9249122568408836e-08\n" - ] - } - ], - "source": [ - "# Checking the difference between two\n", - "\n", - "for v, t in zip(logits_grad_value, logits_grad_tensor):\n", - " print(v-t)\n", - "\n", - "# As you can see the difference is very small" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3.10.4 ('deeply')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "vscode": { - "interpreter": { - "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 7b1e245ca1e3e615dcd22200d40005e9d124f782 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 8 Oct 2022 11:31:15 +0200 Subject: [PATCH 19/20] makemore2 stats plot bug fixed --- lectures/makemore/makemore_part2_mlp.ipynb | 132 ++++++++++----------- 1 file changed, 64 insertions(+), 68 deletions(-) diff --git a/lectures/makemore/makemore_part2_mlp.ipynb b/lectures/makemore/makemore_part2_mlp.ipynb index 3ce90f54..2cb7cacf 100644 --- a/lectures/makemore/makemore_part2_mlp.ipynb +++ b/lectures/makemore/makemore_part2_mlp.ipynb @@ -226,13 +226,13 @@ { "data": { "text/plain": [ - "tensor([[-0.1173, 0.9927, -0.3330, ..., 0.9685, -0.9261, -0.9764],\n", - " [-0.9989, 1.0000, -0.7699, ..., 0.9995, -0.9570, -1.0000],\n", - " [ 0.9983, 0.9999, -0.8472, ..., 0.9419, -0.4396, -0.9535],\n", + "tensor([[ 1.0000, 0.1136, 1.0000, ..., -1.0000, -0.4076, -1.0000],\n", + " [-0.8894, 0.9575, 1.0000, ..., -1.0000, 0.5107, -0.9999],\n", + " [ 0.8602, 0.9999, 1.0000, ..., -0.9904, -0.9702, -0.9999],\n", " ...,\n", - " [-0.9909, -0.8530, 0.8724, ..., 0.7324, 0.2845, 0.4413],\n", - " [ 0.4456, 0.8637, 0.2240, ..., 0.6836, 0.9979, 1.0000],\n", - " [ 0.8108, -1.0000, 0.9985, ..., -0.7292, -0.9997, 0.0684]])" + " [ 0.9080, 0.9589, -0.6141, ..., -0.9841, 0.9413, -0.7702],\n", + " [ 0.6753, 0.1300, 0.8133, ..., 0.3599, -0.9929, -0.8642],\n", + " [ 0.9993, 0.9127, -0.2354, ..., -0.9065, 0.9878, -0.0749]])" ] }, "execution_count": 12, @@ -343,29 +343,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "shape mismatch: indexing tensors could not be broadcast together with shapes [32], [228146]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/ibad/Desktop/nn-zero-to-hero/lectures/makemore/makemore_part2_mlp.ipynb Cell 20\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39m-\u001b[39mprob[torch\u001b[39m.\u001b[39;49marange(\u001b[39m32\u001b[39;49m), Y]\u001b[39m.\u001b[39mlog()\u001b[39m.\u001b[39mmean()\n\u001b[1;32m 2\u001b[0m loss\n", - "\u001b[0;31mIndexError\u001b[0m: shape mismatch: indexing tensors could not be broadcast together with shapes [32], [228146]" - ] - } - ], + "outputs": [], "source": [ - "loss = -prob[torch.arange(32), Y].log().mean()\n", - "loss" + "# loss = -prob[torch.arange(32), Y].log().mean()\n", + "# loss" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -374,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -383,7 +371,7 @@ "(torch.Size([182625, 3]), torch.Size([182625]))" ] }, - "execution_count": 30, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -394,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -409,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -418,7 +406,7 @@ "11897" ] }, - "execution_count": 32, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -429,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -449,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -461,11 +449,11 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "for i in range(50000):\n", + "for i in range(200000):\n", " \n", " # minibatch construct\n", " ix = torch.randint(0, Xtr.shape[0], (32,))\n", @@ -499,12 +487,22 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -519,16 +517,16 @@ }, { "cell_type": "code", - "execution_count": 797, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(2.1260, grad_fn=)" + "tensor(2.1204, grad_fn=)" ] }, - "execution_count": 797, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -543,16 +541,16 @@ }, { "cell_type": "code", - "execution_count": 798, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(2.1701, grad_fn=)" + "tensor(2.1617, grad_fn=)" ] }, - "execution_count": 798, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -567,19 +565,17 @@ }, { "cell_type": "code", - "execution_count": 710, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -594,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 805, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -613,7 +609,7 @@ "torch.Size([1, 3, 10])" ] }, - "execution_count": 805, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -625,33 +621,33 @@ }, { "cell_type": "code", - "execution_count": 820, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "carmahela.\n", - "jhovi.\n", - "kimrin.\n", - "thil.\n", - "halanna.\n", - "jazhien.\n", - "amerynci.\n", - "aqui.\n", - "nellara.\n", - "chaiiv.\n", + "carmah.\n", + "amelle.\n", + "khyrmir.\n", + "revillenanden.\n", + "jazhnen.\n", + "delynn.\n", + "jareen.\n", + "ner.\n", + "kentzeriav.\n", "kaleigh.\n", "ham.\n", - "joce.\n", - "quinton.\n", + "jorn.\n", + "quintis.\n", "lilea.\n", - "jamilio.\n", - "jeron.\n", - "jaryni.\n", - "jace.\n", - "chrudeley.\n" + "jadique.\n", + "jero.\n", + "dearynix.\n", + "kaek.\n", + "dura.\n", + "med.\n" ] } ], From eaa03fdabb5ab1dff09851279557e92462be31b2 Mon Sep 17 00:00:00 2001 From: Ibad Rather Date: Sat, 8 Oct 2022 11:47:01 +0200 Subject: [PATCH 20/20] added my_code again --- .../makemore/1_bigram_torch_exercise.ipynb | 2464 + .../1_bigram_torch_exercise_approach_2.ipynb | 198541 +++++++++++++++ .../makemore/1_makemore_bigram_torch.ipynb | 2653 + my_code/makemore/2_makemore_mlp.ipynb | 249 + my_code/makemore/names.txt | 32033 +++ .../1_gradients_and_backpropagation.ipynb | 1963 + ..._activation_optmization_grad_descent.ipynb | 1610 + my_code/micrograd/micrograd_demo.ipynb | 310 + .../micrograd_ir/micrograd_ir/__init__.py | 0 .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 173 bytes .../__pycache__/engine.cpython-310.pyc | Bin 0 -> 4445 bytes .../micrograd_ir/micrograd_ir/engine.py | 130 + .../micrograd/micrograd_ir/micrograd_ir/nn.py | 58 + .../test_engine.cpython-310-pytest-7.1.3.pyc | Bin 0 -> 4168 bytes .../micrograd_ir/test/test_engine.py | 72 + my_code/micrograd/neuron_model.jpeg | Bin 0 -> 37814 bytes .../solved_micrograd_exercises.ipynb | 616 + 17 files changed, 240699 insertions(+) create mode 100644 my_code/makemore/1_bigram_torch_exercise.ipynb create mode 100644 my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb create mode 100644 my_code/makemore/1_makemore_bigram_torch.ipynb create mode 100644 my_code/makemore/2_makemore_mlp.ipynb create mode 100644 my_code/makemore/names.txt create mode 100644 my_code/micrograd/1_gradients_and_backpropagation.ipynb create mode 100644 my_code/micrograd/2_activation_optmization_grad_descent.ipynb create mode 100644 my_code/micrograd/micrograd_demo.ipynb create mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/__init__.py create mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/__pycache__/__init__.cpython-310.pyc create mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/__pycache__/engine.cpython-310.pyc create mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/engine.py create mode 100644 my_code/micrograd/micrograd_ir/micrograd_ir/nn.py create mode 100644 my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc create mode 100644 my_code/micrograd/micrograd_ir/test/test_engine.py create mode 100644 my_code/micrograd/neuron_model.jpeg create mode 100644 my_code/micrograd/solved_micrograd_exercises.ipynb diff --git a/my_code/makemore/1_bigram_torch_exercise.ipynb b/my_code/makemore/1_bigram_torch_exercise.ipynb new file mode 100644 index 00000000..2ee03127 --- /dev/null +++ b/my_code/makemore/1_bigram_torch_exercise.ipynb @@ -0,0 +1,2464 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises:\n", + "E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model?\n", + "\n", + "E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n", + "\n", + "E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n", + "\n", + "E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of F.one_hot in favor of simply indexing into rows of W?\n", + "\n", + "E05: look up and use F.cross_entropy instead. You should achieve the same result. Can you think of why we'd prefer to use F.cross_entropy instead?\n", + "\n", + "E06: meta-exercise! Think of a fun/interesting exercise and complete it." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n", + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))\n", + "\n", + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('.', 'e', 'm'): 288,\n", + " ('e', 'm', 'm'): 100,\n", + " ('m', 'm', 'a'): 72,\n", + " ('m', 'a', '.'): 174,\n", + " ('.', 'o', 'l'): 104,\n", + " ('o', 'l', 'i'): 69,\n", + " ('l', 'i', 'v'): 54,\n", + " ('i', 'v', 'i'): 78,\n", + " ('v', 'i', 'a'): 147,\n", + " ('i', 'a', '.'): 903,\n", + " ('.', 'a', 'v'): 243,\n", + " ('a', 'v', 'a'): 161,\n", + " ('v', 'a', '.'): 93,\n", + " ('.', 'i', 's'): 124,\n", + " ('i', 's', 'a'): 142,\n", + " ('s', 'a', 'b'): 76,\n", + " ('a', 'b', 'e'): 173,\n", + " ('b', 'e', 'l'): 201,\n", + " ('e', 'l', 'l'): 822,\n", + " ('l', 'l', 'a'): 337,\n", + " ('l', 'a', '.'): 684,\n", + " ('.', 's', 'o'): 152,\n", + " ('s', 'o', 'p'): 21,\n", + " ('o', 'p', 'h'): 37,\n", + " ('p', 'h', 'i'): 61,\n", + " ('h', 'i', 'a'): 81,\n", + " ('.', 'c', 'h'): 352,\n", + " ('c', 'h', 'a'): 236,\n", + " ('h', 'a', 'r'): 329,\n", + " ('a', 'r', 'l'): 287,\n", + " ('r', 'l', 'o'): 44,\n", + " ('l', 'o', 't'): 14,\n", + " ('o', 't', 't'): 34,\n", + " ('t', 't', 'e'): 121,\n", + " ('t', 'e', '.'): 175,\n", + " ('.', 'm', 'i'): 393,\n", + " ('m', 'i', 'a'): 95,\n", + " ('.', 'a', 'm'): 384,\n", + " ('a', 'm', 'e'): 226,\n", + " ('m', 'e', 'l'): 188,\n", + " ('e', 'l', 'i'): 537,\n", + " ('l', 'i', 'a'): 518,\n", + " ('.', 'h', 'a'): 505,\n", + " ('a', 'r', 'p'): 8,\n", + " ('r', 'p', 'e'): 5,\n", + " ('p', 'e', 'r'): 77,\n", + " ('e', 'r', '.'): 683,\n", + " ('.', 'e', 'v'): 154,\n", + " ('e', 'v', 'e'): 142,\n", + " ('v', 'e', 'l'): 76,\n", + " ('e', 'l', 'y'): 353,\n", + " ('l', 'y', 'n'): 976,\n", + " ('y', 'n', '.'): 953,\n", + " ('.', 'a', 'b'): 190,\n", + " ('a', 'b', 'i'): 76,\n", + " ('b', 'i', 'g'): 15,\n", + " ('i', 'g', 'a'): 35,\n", + " ('g', 'a', 'i'): 18,\n", + " ('a', 'i', 'l'): 259,\n", + " ('i', 'l', '.'): 119,\n", + " ('e', 'm', 'i'): 160,\n", + " ('m', 'i', 'l'): 259,\n", + " ('i', 'l', 'y'): 203,\n", + " ('l', 'y', '.'): 270,\n", + " ('.', 'e', 'l'): 488,\n", + " 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1453,\n", + " ('m', 'a', 'd'): 136,\n", + " ('a', 'd', 'i'): 190,\n", + " ('d', 'i', 's'): 42,\n", + " ('i', 's', 'o'): 68,\n", + " ('s', 'o', 'n'): 341,\n", + " ('o', 'n', '.'): 1503,\n", + " ('.', 'l', 'u'): 162,\n", + " ('l', 'u', 'n'): 25,\n", + " ('u', 'n', 'a'): 61,\n", + " ('n', 'a', '.'): 1673,\n", + " ('.', 'g', 'r'): 165,\n", + " ('g', 'r', 'a'): 120,\n", + " ('r', 'a', 'c'): 103,\n", + " ('a', 'c', 'e'): 129,\n", + " ('c', 'e', '.'): 234,\n", + " ('c', 'h', 'l'): 22,\n", + " ('h', 'l', 'o'): 24,\n", + " ('l', 'o', 'e'): 17,\n", + " ('o', 'e', '.'): 28,\n", + " ('.', 'p', 'e'): 92,\n", + " ('p', 'e', 'n'): 31,\n", + " ('e', 'n', 'e'): 151,\n", + " ('n', 'e', 'l'): 145,\n", + " ('e', 'l', 'o'): 83,\n", + " ('l', 'o', 'p'): 11,\n", + " ('o', 'p', 'e'): 33,\n", + " ('p', 'e', '.'): 26,\n", + " ('.', 'l', 'a'): 463,\n", + " ('l', 'a', 'y'): 234,\n", + " ('a', 'y', 'l'): 483,\n", + " ('y', 'l', 'a'): 381,\n", + " ('.', 'r', 'i'): 201,\n", + " ('r', 'i', 'l'): 62,\n", + " ('i', 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'l'): 11,\n", + " ('k', 'l', 'y'): 41,\n", + " ('.', 'b', 'e'): 253,\n", + " ('.', 'c', 'l'): 68,\n", + " ('c', 'l', 'a'): 60,\n", + " ('l', 'a', 'i'): 177,\n", + " ('a', 'i', 'r'): 189,\n", + " ('i', 'r', 'e'): 122,\n", + " ('r', 'e', '.'): 231,\n", + " ('.', 's', 'k'): 55,\n", + " ('s', 'k', 'y'): 44,\n", + " ('k', 'y', 'l'): 104,\n", + " ('l', 'a', 'r'): 153,\n", + " ('a', 'r', '.'): 228,\n", + " ('l', 'u', 'c'): 69,\n", + " ('u', 'c', 'y'): 6,\n", + " ('c', 'y', '.'): 38,\n", + " ('.', 'p', 'a'): 175,\n", + " ('p', 'a', 'i'): 31,\n", + " ('a', 'i', 's'): 145,\n", + " ('i', 's', 'l'): 63,\n", + " ('s', 'l', 'e'): 138,\n", + " ('e', 'r', 'l'): 101,\n", + " ('r', 'l', 'y'): 80,\n", + " ('.', 'a', 'n'): 623,\n", + " ('a', 'r', 'o'): 103,\n", + " ('r', 'o', 'l'): 42,\n", + " ('l', 'i', 'n'): 505,\n", + " ('i', 'n', 'e'): 241,\n", + " ('n', 'e', '.'): 607,\n", + " ('n', 'o', 'v'): 52,\n", + " ('o', 'v', 'a'): 90,\n", + " ('.', 'g', 'e'): 110,\n", + " ('g', 'e', 'n'): 86,\n", + " ('n', 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'a'): 78,\n", + " ('e', 'r', 'n'): 41,\n", + " ('r', 'n', 'a'): 35,\n", + " ('n', 'a', 'n'): 104,\n", + " ('h', 'a', 't'): 20,\n", + " ('a', 't', 't'): 73,\n", + " ('t', 't', 'i'): 38,\n", + " ('d', 'a', 'y'): 90,\n", + " ('m', 'a', 'i'): 120,\n", + " ('i', 's', 'i'): 35,\n", + " ('a', 'y', 's'): 133,\n", + " ('y', 's', 'i'): 45,\n", + " ('k', 'a', 'r'): 218,\n", + " ('e', 'e', 'n'): 200,\n", + " ('h', 'o', 'l'): 40,\n", + " ('.', 'a', 'z'): 152,\n", + " ('a', 'z', 'a'): 123,\n", + " ...}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trigrams = {}\n", + "for w in words:\n", + " #chs = [''] + list(w) + ['']\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " trigram = (ch1, ch2, ch3)\n", + " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", + "\n", + "trigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": 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52),\n", + " (('a', 'w', 'n'), 52),\n", + " (('a', 'j', 'a'), 52),\n", + " (('i', 'k', 'o'), 52),\n", + " (('d', 'd', 'i'), 51),\n", + " (('g', 'a', 'r'), 51),\n", + " (('n', 'd', 'o'), 51),\n", + " (('e', 'i', 'r'), 51),\n", + " (('s', 'e', 'y'), 51),\n", + " (('t', 'i', 'e'), 51),\n", + " (('a', 'm', 'b'), 51),\n", + " (('p', 'r', 'i'), 51),\n", + " (('e', 'r', 't'), 51),\n", + " (('r', 'i', 'd'), 51),\n", + " (('k', 'i', 'e'), 51),\n", + " (('j', 'a', 'e'), 51),\n", + " (('i', 't', 't'), 51),\n", + " (('h', 'i', 't'), 51),\n", + " (('a', 'i', 'm'), 51),\n", + " (('w', 'n', '.'), 51),\n", + " (('r', 'a', 'd'), 51),\n", + " (('a', 'y', 'o'), 51),\n", + " (('e', 'a', 'l'), 51),\n", + " (('g', 'i', 'a'), 50),\n", + " (('e', 'p', 'h'), 50),\n", + " (('a', 'i', 't'), 50),\n", + " (('i', 'x', '.'), 50),\n", + " (('z', 'a', 'h'), 50),\n", + " (('c', 'a', 'y'), 50),\n", + " (('a', 'n', 'u'), 50),\n", + " (('e', 'k', '.'), 50),\n", + " (('r', 'i', 'u'), 50),\n", + " (('a', 'u', 'd'), 49),\n", 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'o'), 44),\n", + " (('h', 'm', 'a'), 44),\n", + " (('e', 'c', 'k'), 44),\n", + " (('j', 'a', 'x'), 44),\n", + " (('b', 'a', 's'), 44),\n", + " (('d', 'h', 'a'), 44),\n", + " (('o', 'n', 'y'), 43),\n", + " (('s', 'i', 'd'), 43),\n", + " (('y', 'e', '.'), 43),\n", + " (('y', 's', 't'), 43),\n", + " (('l', 'o', 'u'), 43),\n", + " (('e', 'n', 'y'), 43),\n", + " (('y', 'v', 'i'), 43),\n", + " (('s', 'm', 'a'), 43),\n", + " (('u', 'h', 'a'), 43),\n", + " (('v', 'e', 'e'), 43),\n", + " (('m', 'i', 't'), 43),\n", + " (('t', 'h', 'o'), 43),\n", + " (('y', 'm', 'a'), 43),\n", + " (('d', 'i', 's'), 42),\n", + " ...]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(trigrams.items(), key = lambda item: -item[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n" + ] + } + ], + "source": [ + "chars = sorted(list(set(\"\".join(words))))\n", + "print(chars)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}\n" + ] + } + ], + "source": [ + "# string to int mapping\n", + "stoi = {}\n", + "for i, char in enumerate(chars):\n", + " stoi[char] = i+1\n", + "\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n" + ] + } + ], + "source": [ + "stoi = {char:i+1 for i, char in enumerate(chars)}\n", + "stoi['.'] = 0\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], + "source": [ + "# int to string mapping\n", + "itos = {i+1:char for i, char in enumerate(chars)}\n", + "itos[0] = \".\"\n", + "print(itos)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".em: 0 5 13\n", + "emm: 5 13 13\n", + "mma: 13 13 1\n", + "ma.: 13 1 0\n", + "tensor([[ 0, 5],\n", + " [ 5, 13],\n", + " [13, 13],\n", + " [13, 1]])\n", + "tensor([13, 13, 1, 0])\n" + ] + } + ], + "source": [ + "# First let's create a training set of bigrams (x, y)\n", + "\n", + "xs, ys = [], []\n", + "\n", + "for w in words[:1]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " ix3 = stoi[ch3]\n", + " xs.append((ix1, ix2))\n", + " ys.append(ix3)\n", + " print(f\"{ch1}{ch2}{ch3}: {ix1} {ix2} {ix3}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "\n", + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([4, 2, 27])\n" + ] + } + ], + "source": [ + "xenc = F.one_hot(xs, num_classes=27).float() # we dont want an int we want a float\n", + "print(xenc.shape)\n", + "# plt.imshow(xenc, cmap='Blues')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples: 8\n" + ] + } + ], + "source": [ + "# Create the dataset\n", + "xs, ys = [], []\n", + "\n", + "for w in words[:1]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " ix3 = stoi[ch3]\n", + " xs.append((ix1, ix2))\n", + " ys.append(ix3)\n", + " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "num = xs.nelement()\n", + "\n", + "print(\"Number of examples: \", num)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", + "W = torch.zeros((2, 27, 27), requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2, 4, 27])" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xenc = F.one_hot(xs, num_classes=27).float().permute(1, 0, 2)\n", + "xenc.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 4, 27])\n", + "torch.Size([2, 27, 27])\n" + ] + } + ], + "source": [ + "xenc @ W\n", + "\n", + "print(xenc.shape)\n", + "print(W.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([4, 2, 27])\n", + "torch.Size([4])\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 50\n", + "alpha = 0.01\n", + "\n", + "for k in range(1):\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=27).float().permute(1, 0, 2) # input is one-hot encoded\n", + "\n", + " logits = xenc @ W # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = (counts / counts.sum(1, keepdim=True)).permute(1, 0, 2) # probabilities for next character \n", + " \n", + " print(probs.shape)\n", + " print(ys.shape)\n", + "\n", + " # # regularised loss\n", + " # loss = -probs[torch.arange(num), torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", + " # if k % 25 == 0:\n", + " # print('loss:', loss.item())\n", + "\n", + " # # Backward pass\n", + " # W.grad = None # set gradient to zero\n", + " # loss.backward()\n", + "\n", + " # # update\n", + " # W.data += -lr * W.grad # gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([27])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor([0, 1, 2, 3, 4, 5, 6, 7]),\n", + " tensor([0, 1, 2, 3, 4, 5, 6, 7]),\n", + " tensor([13, 13, 1, 0])]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[torch.arange(num), torch.arange(num), ys]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb new file mode 100644 index 00000000..24fda497 --- /dev/null +++ b/my_code/makemore/1_bigram_torch_exercise_approach_2.ipynb @@ -0,0 +1,198541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises:\n", + "E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model?\n", + "\n", + "E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see?\n", + "\n", + "E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve?\n", + "\n", + "E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of F.one_hot in favor of simply indexing into rows of W?\n", + "\n", + "E05: look up and use F.cross_entropy instead. You should achieve the same result. Can you think of why we'd prefer to use F.cross_entropy instead?\n", + "\n", + "E06: meta-exercise! Think of a fun/interesting exercise and complete it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "32033\n" + ] + } + ], + "source": [ + "print(len(words))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n", + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))\n", + "\n", + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('.e', 'm'): 288,\n", + " ('em', 'm'): 100,\n", + " ('mm', 'a'): 72,\n", + " ('ma', '.'): 174,\n", + " ('.o', 'l'): 104,\n", + " ('ol', 'i'): 69,\n", + " ('li', 'v'): 54,\n", + " ('iv', 'i'): 78,\n", + " ('vi', 'a'): 147,\n", + " ('ia', '.'): 903,\n", + " ('.a', 'v'): 243,\n", + " ('av', 'a'): 161,\n", + " ('va', '.'): 93,\n", + " ('.i', 's'): 124,\n", + " ('is', 'a'): 142,\n", + " ('sa', 'b'): 76,\n", + " ('ab', 'e'): 173,\n", + " ('be', 'l'): 201,\n", + " ('el', 'l'): 822,\n", + " ('ll', 'a'): 337,\n", + " ('la', '.'): 684,\n", + " ('.s', 'o'): 152,\n", + " ('so', 'p'): 21,\n", + " ('op', 'h'): 37,\n", + " ('ph', 'i'): 61,\n", + " ('hi', 'a'): 81,\n", + " ('.c', 'h'): 352,\n", + " ('ch', 'a'): 236,\n", + " ('ha', 'r'): 329,\n", + " ('ar', 'l'): 287,\n", + " ('rl', 'o'): 44,\n", + " ('lo', 't'): 14,\n", + " ('ot', 't'): 34,\n", + " ('tt', 'e'): 121,\n", + " ('te', '.'): 175,\n", + " ('.m', 'i'): 393,\n", + " ('mi', 'a'): 95,\n", + " ('.a', 'm'): 384,\n", + " ('am', 'e'): 226,\n", + " ('me', 'l'): 188,\n", + " ('el', 'i'): 537,\n", + " ('li', 'a'): 518,\n", + " ('.h', 'a'): 505,\n", + " ('ar', 'p'): 8,\n", + " ('rp', 'e'): 5,\n", + " ('pe', 'r'): 77,\n", + " ('er', '.'): 683,\n", + " ('.e', 'v'): 154,\n", + " ('ev', 'e'): 142,\n", + " ('ve', 'l'): 76,\n", + " ('el', 'y'): 353,\n", + " ('ly', 'n'): 976,\n", + " ('yn', '.'): 953,\n", + " ('.a', 'b'): 190,\n", + " ('ab', 'i'): 76,\n", + " ('bi', 'g'): 15,\n", + " ('ig', 'a'): 35,\n", + " ('ga', 'i'): 18,\n", + " ('ai', 'l'): 259,\n", + " ('il', '.'): 119,\n", + " ('em', 'i'): 160,\n", + " ('mi', 'l'): 259,\n", + " ('il', 'y'): 203,\n", + " ('ly', '.'): 270,\n", + " ('.e', 'l'): 488,\n", + " ('li', 'z'): 81,\n", + " ('iz', 'a'): 93,\n", + " ('za', 'b'): 40,\n", + " ('be', 't'): 61,\n", + " ('et', 'h'): 114,\n", + " ('th', '.'): 178,\n", + " ('il', 'a'): 279,\n", + " ('av', 'e'): 166,\n", + " ('ve', 'r'): 160,\n", + " ('er', 'y'): 84,\n", + " ('ry', '.'): 201,\n", + " ('so', 'f'): 18,\n", + " ('of', 'i'): 19,\n", + " ('fi', 'a'): 20,\n", + " ('.c', 'a'): 628,\n", + " ('ca', 'm'): 92,\n", + " ('am', 'i'): 355,\n", + " ('.a', 'r'): 482,\n", + " ('ar', 'i'): 950,\n", + " ('ri', 'a'): 499,\n", + " ('.s', 'c'): 27,\n", + " ('sc', 'a'): 14,\n", + " ('ca', 'r'): 193,\n", + " ('rl', 'e'): 146,\n", + " ('le', 't'): 91,\n", + " ('et', 't'): 203,\n", + " ('tt', '.'): 102,\n", + " ('.v', 'i'): 161,\n", + " ('vi', 'c'): 26,\n", + " ('ic', 't'): 21,\n", + " ('ct', 'o'): 22,\n", + " ('to', 'r'): 110,\n", + " ('or', 'i'): 220,\n", + " ('.m', 'a'): 1453,\n", + " ('ma', 'd'): 136,\n", + " ('ad', 'i'): 190,\n", + " ('di', 's'): 42,\n", + " ('is', 'o'): 68,\n", + " ('so', 'n'): 341,\n", + " ('on', '.'): 1503,\n", + " ('.l', 'u'): 162,\n", + " ('lu', 'n'): 25,\n", + " ('un', 'a'): 61,\n", + " ('na', '.'): 1673,\n", + " ('.g', 'r'): 165,\n", + " ('gr', 'a'): 120,\n", + " ('ra', 'c'): 103,\n", + " ('ac', 'e'): 129,\n", + " ('ce', '.'): 234,\n", + " ('ch', 'l'): 22,\n", + " ('hl', 'o'): 24,\n", + " ('lo', 'e'): 17,\n", + " ('oe', '.'): 28,\n", + " ('.p', 'e'): 92,\n", + " ('pe', 'n'): 31,\n", + " ('en', 'e'): 151,\n", + " ('ne', 'l'): 145,\n", + " ('el', 'o'): 83,\n", + " ('lo', 'p'): 11,\n", + " ('op', 'e'): 33,\n", + " ('pe', '.'): 26,\n", + " ('.l', 'a'): 463,\n", + " ('la', 'y'): 234,\n", + " ('ay', 'l'): 483,\n", + " ('yl', 'a'): 381,\n", + " ('.r', 'i'): 201,\n", + " ('ri', 'l'): 62,\n", + " ('il', 'e'): 197,\n", + " ('le', 'y'): 443,\n", + " ('ey', '.'): 602,\n", + " ('.z', 'o'): 72,\n", + " ('zo', 'e'): 19,\n", + " ('oe', 'y'): 14,\n", + " ('.n', 'o'): 156,\n", + " ('no', 'r'): 101,\n", + " ('or', 'a'): 202,\n", + " ('ra', '.'): 627,\n", + " ('.l', 'i'): 269,\n", + " ('li', 'l'): 133,\n", + " ('el', 'e'): 219,\n", + " ('le', 'a'): 173,\n", + " ('ea', 'n'): 196,\n", + " ('an', 'o'): 98,\n", + " ('or', '.'): 148,\n", + " ('ha', 'n'): 469,\n", + " ('an', 'n'): 825,\n", + " ('nn', 'a'): 633,\n", + " ('na', 'h'): 185,\n", + " ('ah', '.'): 1714,\n", + " ('il', 'l'): 211,\n", + " ('ll', 'i'): 271,\n", + " ('ia', 'n'): 790,\n", + " ('an', '.'): 1509,\n", + " ('.a', 'd'): 366,\n", + " ('ad', 'd'): 94,\n", + " ('dd', 'i'): 51,\n", + " ('.a', 'u'): 152,\n", + " ('au', 'b'): 35,\n", + " ('ub', 'r'): 34,\n", + " ('br', 'e'): 189,\n", + " ('re', 'y'): 163,\n", + " ('li', 'e'): 285,\n", + " ('ie', '.'): 858,\n", + " ('.s', 't'): 135,\n", + " ('st', 'e'): 154,\n", + " ('te', 'l'): 63,\n", + " ('.n', 'a'): 470,\n", + " ('na', 't'): 101,\n", + " ('at', 'a'): 111,\n", + " ('ta', 'l'): 153,\n", + " ('al', 'i'): 575,\n", + " ('.l', 'e'): 366,\n", + " ('ea', 'h'): 92,\n", + " ('ha', 'z'): 42,\n", + " ('az', 'e'): 60,\n", + " ('ze', 'l'): 83,\n", + " ('el', '.'): 727,\n", + " ('vi', 'o'): 89,\n", + " ('io', 'l'): 21,\n", + " ('ol', 'e'): 83,\n", + " ('et', '.'): 106,\n", + " ('au', 'r'): 108,\n", + " ('ur', 'o'): 12,\n", + " ('ro', 'r'): 18,\n", + " ('.s', 'a'): 595,\n", + " ('sa', 'v'): 35,\n", + " ('va', 'n'): 237,\n", + " ('au', 'd'): 49,\n", + " ('ud', 'r'): 23,\n", + " ('dr', 'e'): 113,\n", + " ('.b', 'r'): 646,\n", + " ('br', 'o'): 69,\n", + " ('ro', 'o'): 38,\n", + " ('oo', 'k'): 27,\n", + " ('ok', 'l'): 11,\n", + " ('kl', 'y'): 41,\n", + " ('.b', 'e'): 253,\n", + " ('.c', 'l'): 68,\n", + " ('cl', 'a'): 60,\n", + " ('la', 'i'): 177,\n", + " ('ai', 'r'): 189,\n", + " ('ir', 'e'): 122,\n", + " ('re', '.'): 231,\n", + " ('.s', 'k'): 55,\n", + " ('sk', 'y'): 44,\n", + " ('ky', 'l'): 104,\n", + " ('la', 'r'): 153,\n", + " ('ar', '.'): 228,\n", + " ('lu', 'c'): 69,\n", + " ('uc', 'y'): 6,\n", + " ('cy', '.'): 38,\n", + " ('.p', 'a'): 175,\n", + " ('pa', 'i'): 31,\n", + " ('ai', 's'): 145,\n", + " ('is', 'l'): 63,\n", + " ('sl', 'e'): 138,\n", + " ('er', 'l'): 101,\n", + " ('rl', 'y'): 80,\n", + " ('.a', 'n'): 623,\n", + " ('ar', 'o'): 103,\n", + " ('ro', 'l'): 42,\n", + " ('li', 'n'): 505,\n", + " ('in', 'e'): 241,\n", + " ('ne', '.'): 607,\n", + " ('no', 'v'): 52,\n", + " ('ov', 'a'): 90,\n", + " ('.g', 'e'): 110,\n", + " ('ge', 'n'): 86,\n", + " ('ne', 's'): 93,\n", + " ('es', 'i'): 57,\n", + " ('si', 's'): 21,\n", + " ('is', '.'): 287,\n", + " ('il', 'i'): 180,\n", + " ('.k', 'e'): 601,\n", + " ('ke', 'n'): 252,\n", + " ('en', 'n'): 240,\n", + " ('nn', 'e'): 261,\n", + " ('ne', 'd'): 23,\n", + " ('ed', 'y'): 31,\n", + " ('dy', '.'): 128,\n", + " ('sa', 'm'): 136,\n", + " ('am', 'a'): 431,\n", + " ('ma', 'n'): 274,\n", + " ('an', 't'): 177,\n", + " ('nt', 'h'): 44,\n", + " ('th', 'a'): 168,\n", + " ('ha', '.'): 284,\n", + " ('ma', 'y'): 153,\n", + " ('ay', 'a'): 389,\n", + " ('ya', '.'): 716,\n", + " ('.w', 'i'): 91,\n", + " ('wi', 'l'): 56,\n", + " ('ll', 'o'): 58,\n", + " ('lo', 'w'): 27,\n", + " ('ow', '.'): 25,\n", + " ('.k', 'i'): 250,\n", + " ('ki', 'n'): 134,\n", + " ('in', 's'): 58,\n", + " ('ns', 'l'): 63,\n", + " ('na', 'o'): 10,\n", + " ('ao', 'm'): 6,\n", + " ('om', 'i'): 74,\n", + " ('mi', '.'): 124,\n", + " ('.a', 'a'): 207,\n", + " ('aa', 'l'): 46,\n", + " ('li', 'y'): 117,\n", + " ('iy', 'a'): 669,\n", + " ('ya', 'h'): 511,\n", + " ('le', 'n'): 383,\n", + " ('en', 'a'): 279,\n", + " ('sa', 'r'): 83,\n", + " ('ar', 'a'): 371,\n", + " ('ra', 'h'): 222,\n", + " ('an', 'a'): 804,\n", + " ('.a', 'l'): 632,\n", + " ('al', 'l'): 196,\n", + " ('li', 's'): 208,\n", + " ('.g', 'a'): 136,\n", + " ('ga', 'b'): 28,\n", + " ('ab', 'r'): 85,\n", + " ('br', 'i'): 254,\n", + " ('ri', 'e'): 394,\n", + " ('ie', 'l'): 395,\n", + " ('li', 'c'): 36,\n", + " ('ic', 'e'): 63,\n", + " ('ad', 'e'): 194,\n", + " ('de', 'l'): 217,\n", + " ('.c', 'o'): 255,\n", + " ('co', 'r'): 102,\n", + " ('.r', 'u'): 108,\n", + " ('ru', 'b'): 17,\n", + " ('ub', 'y'): 6,\n", + " ('by', '.'): 55,\n", + " ('ev', 'a'): 133,\n", + " ('.s', 'e'): 182,\n", + " ('se', 'r'): 67,\n", + " ('er', 'e'): 170,\n", + " ('re', 'n'): 281,\n", + " ('en', 'i'): 168,\n", + " ('ni', 't'): 58,\n", + " ('it', 'y'): 44,\n", + " ('ty', '.'): 88,\n", + " ('au', 't'): 13,\n", + " ('ut', 'u'): 6,\n", + " ('tu', 'm'): 13,\n", + " ('um', 'n'): 8,\n", + " ('mn', '.'): 4,\n", + " ('ha', 'i'): 119,\n", + " ('.g', 'i'): 128,\n", + " ('gi', 'a'): 50,\n", + " ('.v', 'a'): 121,\n", + " ('va', 'l'): 96,\n", + " ('al', 'e'): 601,\n", + " ('en', 't'): 95,\n", + " ('nt', 'i'): 48,\n", + " ('ti', 'n'): 142,\n", + " ('in', 'a'): 379,\n", + " ('sl', 'a'): 33,\n", + " ('.q', 'u'): 82,\n", + " ('qu', 'i'): 67,\n", + " ('ui', 'n'): 47,\n", + " ('in', 'n'): 103,\n", + " ('nn', '.'): 619,\n", + " ('.n', 'e'): 164,\n", + " ('ne', 'v'): 40,\n", + " ('va', 'e'): 16,\n", + " ('ae', 'h'): 13,\n", + " ('eh', '.'): 36,\n", + " ('.i', 'v'): 49,\n", + " ('iv', 'y'): 23,\n", + " ('vy', '.'): 28,\n", + " ('sa', 'd'): 32,\n", + " ('di', 'e'): 106,\n", + " ('.p', 'i'): 30,\n", + " ('pi', 'p'): 6,\n", + " ('ip', 'e'): 9,\n", + " ('.l', 'y'): 118,\n", + " ('ly', 'd'): 14,\n", + " ('yd', 'i'): 41,\n", + " ('di', 'a'): 98,\n", + " ('le', 'x'): 83,\n", + " ('ex', 'a'): 28,\n", + " ('xa', '.'): 10,\n", + " ('.j', 'o'): 429,\n", + " ('jo', 's'): 93,\n", + " ('os', 'e'): 166,\n", + " ('se', 'p'): 17,\n", + " ('ep', 'h'): 50,\n", + " ('hi', 'n'): 57,\n", + " ('em', 'e'): 80,\n", + " ('me', 'r'): 209,\n", + " ('.j', 'u'): 178,\n", + " ('ju', 'l'): 74,\n", + " ('ul', 'i'): 96,\n", + " ('.d', 'e'): 524,\n", + " ('la', 'h'): 224,\n", + " ('vi', 'v'): 28,\n", + " ('.k', 'a'): 1254,\n", + " ('ka', 'y'): 215,\n", + " ('yl', 'e'): 303,\n", + " ('le', 'e'): 517,\n", + " ('ee', '.'): 605,\n", + " ('hi', 'e'): 29,\n", + " ('ll', 'e'): 331,\n", + " ('le', '.'): 362,\n", + " ('pe', 'y'): 7,\n", + " ('ey', 't'): 16,\n", + " ('yt', 'o'): 38,\n", + " ('to', 'n'): 377,\n", + " ('.r', 'y'): 111,\n", + " ('ry', 'l'): 75,\n", + " ('ha', 'd'): 99,\n", + " ('ad', 'l'): 33,\n", + " ('dl', 'e'): 40,\n", + " ('.m', 'e'): 284,\n", + " ('el', 'a'): 308,\n", + " ('la', 'n'): 647,\n", + " ('an', 'i'): 703,\n", + " ('ni', 'e'): 223,\n", + " ('ma', 'c'): 77,\n", + " ('ac', 'k'): 59,\n", + " ('ck', 'e'): 52,\n", + " ('en', 'z'): 68,\n", + " ('nz', 'i'): 46,\n", + " ('zi', 'e'): 78,\n", + " ('.r', 'e'): 268,\n", + " ('re', 'a'): 72,\n", + " ('ea', 'g'): 26,\n", + " ('ag', 'a'): 42,\n", + " ('ga', 'n'): 99,\n", + " ('ad', 'a'): 164,\n", + " ('da', 'l'): 203,\n", + " ('al', 'y'): 310,\n", + " ('yn', 'n'): 516,\n", + " ('re', 'e'): 205,\n", + " ('.j', 'a'): 1255,\n", + " ('ja', 'd'): 44,\n", + " ('de', '.'): 92,\n", + " ('ka', 't'): 93,\n", + " ('at', 'h'): 142,\n", + " ('th', 'e'): 106,\n", + " ('he', 'r'): 132,\n", + " ('er', 'i'): 268,\n", + " ('ri', 'n'): 234,\n", + " ('.r', 'a'): 472,\n", + " ('ra', 'e'): 127,\n", + " ('ae', 'l'): 287,\n", + " ('ma', 'r'): 776,\n", + " ('.a', 't'): 72,\n", + " ('he', 'n'): 106,\n", + " ('.x', 'i'): 28,\n", + " ('xi', 'm'): 21,\n", + " ('im', 'e'): 29,\n", + " ('me', 'n'): 60,\n", + " ('ar', 'y'): 195,\n", + " ('ry', 'a'): 118,\n", + " ('le', 'i'): 401,\n", + " ('ei', 'l'): 111,\n", + " ('ni', '.'): 625,\n", + " ('.t', 'a'): 424,\n", + " ('ta', 'y'): 81,\n", + " ('yl', 'o'): 72,\n", + " ('lo', 'r'): 137,\n", + " ('.f', 'a'): 158,\n", + " ('fa', 'i'): 18,\n", + " ('ai', 't'): 50,\n", + " ('it', 'h'): 124,\n", + " ('.r', 'o'): 382,\n", + " ('ro', 's'): 196,\n", + " ('se', '.'): 228,\n", + " ('.k', 'y'): 229,\n", + " ('yl', 'i'): 209,\n", + " ('xa', 'n'): 38,\n", + " ('an', 'd'): 392,\n", + " ('nd', 'r'): 184,\n", + " ('dr', 'a'): 86,\n", + " ('ar', 'g'): 24,\n", + " ('rg', 'a'): 15,\n", + " ('ga', 'r'): 51,\n", + " ('ar', 'e'): 198,\n", + " ('re', 't'): 87,\n", + " ('ly', 'l'): 32,\n", + " ('.a', 's'): 194,\n", + " ('as', 'h'): 276,\n", + " ('sh', 'l'): 41,\n", + " ('hl', 'e'): 32,\n", + " ('za', '.'): 98,\n", + " ('.b', 'a'): 169,\n", + " ('ba', 'i'): 23,\n", + " ('ea', '.'): 158,\n", + " ('.k', 'h'): 240,\n", + " ('kh', 'l'): 11,\n", + " ('ja', 's'): 82,\n", + " ('as', 'm'): 27,\n", + " ('sm', 'i'): 18,\n", + " ('mi', 'n'): 141,\n", + " ('lo', 'd'): 10,\n", + " ('od', 'y'): 21,\n", + " ('.i', 'r'): 49,\n", + " ('ir', 'i'): 75,\n", + " ('ri', 's'): 360,\n", + " ('na', 'b'): 35,\n", + " ('le', 'r'): 87,\n", + " ('er', 's'): 76,\n", + " ('rs', 'o'): 40,\n", + " ('ei', 'g'): 279,\n", + " ('ig', 'h'): 290,\n", + " ('gh', '.'): 235,\n", + " ('.e', 'd'): 80,\n", + " ('ed', 'e'): 56,\n", + " ('de', 'n'): 318,\n", + " ('en', '.'): 1217,\n", + " ('rs', 'y'): 16,\n", + " ('sy', 'n'): 89,\n", + " ('na', 's'): 89,\n", + " ('as', 't'): 102,\n", + " ('st', 'a'): 139,\n", + " ('ta', 's'): 33,\n", + " ('as', 'i'): 157,\n", + " ('si', 'a'): 171,\n", + " ('ly', 's'): 84,\n", + " ('ys', 's'): 40,\n", + " ('ss', 'a'): 166,\n", + " ('sa', '.'): 253,\n", + " ('rl', 'i'): 97,\n", + " ('.e', 's'): 93,\n", + " ('es', 't'): 147,\n", + " ('st', 'h'): 7,\n", + " ('.c', 'e'): 65,\n", + " ('ce', 'c'): 10,\n", + " ('ec', 'i'): 23,\n", + " ('ci', 'l'): 24,\n", + " ('.m', 'o'): 168,\n", + " ('mo', 'l'): 11,\n", + " ('ol', 'l'): 70,\n", + " ('ll', 'y'): 124,\n", + " ('ee', 's'): 46,\n", + " ('es', 'e'): 49,\n", + " ('pa', 'r'): 45,\n", + " ('ar', 'k'): 61,\n", + " ('rk', 'e'): 23,\n", + " ('ke', 'r'): 69,\n", + " ('.f', 'i'): 71,\n", + " ('fi', 'n'): 54,\n", + " ('in', 'l'): 44,\n", + " ('nl', 'e'): 143,\n", + " ('mo', 'r'): 98,\n", + " ('or', 'g'): 36,\n", + " ('.s', 'y'): 71,\n", + " ('sy', 'd'): 7,\n", + " ('yd', 'n'): 14,\n", + " ('dn', 'e'): 15,\n", + " ('ne', 'y'): 76,\n", + " ('jo', 'r'): 54,\n", + " ('or', 'd'): 77,\n", + " ('rd', 'y'): 14,\n", + " ('dy', 'n'): 108,\n", + " ('lo', 'i'): 17,\n", + " ('oi', 's'): 14,\n", + " ('is', 'e'): 126,\n", + " ('.t', 'r'): 209,\n", + " ('tr', 'i'): 107,\n", + " ('in', 'i'): 87,\n", + " ('.d', 'a'): 700,\n", + " ('da', 'i'): 69,\n", + " ('is', 'y'): 29,\n", + " ('sy', '.'): 41,\n", + " ('ki', 'm'): 44,\n", + " ('im', 'b'): 22,\n", + " ('mb', 'e'): 59,\n", + " ('be', 'r'): 152,\n", + " ('la', 'u'): 46,\n", + " ('ur', 'e'): 48,\n", + " ('ev', 'i'): 83,\n", + " ('vi', 'e'): 95,\n", + " ('ie', 'v'): 10,\n", + " ('ve', '.'): 36,\n", + " ('ra', 'b'): 32,\n", + " ('ar', 'm'): 88,\n", + " ('rm', 'o'): 30,\n", + " ('mo', 'n'): 201,\n", + " ('on', 'y'): 43,\n", + " ('ny', '.'): 192,\n", + " ('re', 'm'): 69,\n", + " ('.t', 'e'): 181,\n", + " ('te', 'a'): 23,\n", + " ('ex', 'i'): 28,\n", + " ('xi', 's'): 6,\n", + " ('.l', 'o'): 186,\n", + " ('lo', 'n'): 147,\n", + " ('on', 'd'): 46,\n", + " ('nd', 'o'): 51,\n", + " ('do', 'n'): 142,\n", + " ('.s', 'l'): 17,\n", + " ('sl', 'o'): 11,\n", + " ('lo', 'a'): 12,\n", + " ('oa', 'n'): 42,\n", + " ('an', 'e'): 224,\n", + " ('uc', 'i'): 30,\n", + " ('ci', 'a'): 71,\n", + " ('.d', 'i'): 130,\n", + " ('ie', 't'): 30,\n", + " ('.s', 'i'): 172,\n", + " ('si', 'e'): 74,\n", + " ('ie', 'n'): 91,\n", + " ('nd', 'y'): 53,\n", + " ('.a', 'y'): 173,\n", + " ('ca', 'l'): 121,\n", + " ('ac', 'i'): 56,\n", + " ('ci', 'e'): 59,\n", 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't'): 102,\n", + " ('rt', 'e'): 28,\n", + " ('an', 'y'): 175,\n", + " ('ca', 'd'): 19,\n", + " ('en', 'c'): 58,\n", + " ('ma', 't'): 98,\n", + " ('ti', 'l'): 33,\n", + " ('il', 'd'): 17,\n", + " ('sy', 'l'): 18,\n", + " ('yl', 'v'): 10,\n", + " ('lv', 'i'): 37,\n", + " ('my', 'r'): 36,\n", + " ('yr', 'a'): 78,\n", + " ('er', 'n'): 41,\n", + " ('rn', 'a'): 35,\n", + " ('na', 'n'): 104,\n", + " ('ha', 't'): 20,\n", + " ('at', 't'): 73,\n", + " ('tt', 'i'): 38,\n", + " ('da', 'y'): 90,\n", + " ('ma', 'i'): 120,\n", + " ('is', 'i'): 35,\n", + " ('ay', 's'): 133,\n", + " ('ys', 'i'): 45,\n", + " ('ka', 'r'): 218,\n", + " ('ee', 'n'): 200,\n", + " ('ho', 'l'): 40,\n", + " ('.a', 'z'): 152,\n", + " ('az', 'a'): 123,\n", + " ...}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trigrams = {}\n", + "for w in words:\n", + " #chs = [''] + list(w) + ['']\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " trigram = (ch1+ch2, ch3)\n", + " trigrams[trigram] = trigrams.get(trigram, 0) + 1\n", + "\n", + "trigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(('ah', '.'), 1714),\n", + " (('na', '.'), 1673),\n", + " (('an', '.'), 1509),\n", + " (('on', '.'), 1503),\n", + " (('.m', 'a'), 1453),\n", + " (('.j', 'a'), 1255),\n", + " (('.k', 'a'), 1254),\n", + " (('en', '.'), 1217),\n", + " (('ly', 'n'), 976),\n", + " (('yn', '.'), 953),\n", + " (('ar', 'i'), 950),\n", + " (('ia', '.'), 903),\n", + " (('ie', '.'), 858),\n", + " (('an', 'n'), 825),\n", + " (('el', 'l'), 822),\n", + " (('an', 'a'), 804),\n", + " (('ia', 'n'), 790),\n", + " (('ma', 'r'), 776),\n", + " (('in', '.'), 766),\n", + " (('el', '.'), 727),\n", + " (('ya', '.'), 716),\n", + " (('an', 'i'), 703),\n", + " (('.d', 'a'), 700),\n", + " (('la', '.'), 684),\n", + " (('er', '.'), 683),\n", + " (('iy', 'a'), 669),\n", + " 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(('an', 'v'), 49),\n", + " (('er', 'm'), 49),\n", + " (('me', 's'), 49),\n", + " (('ja', 'r'), 49),\n", + " (('ar', 'v'), 49),\n", + " (('nt', 'i'), 48),\n", + " (('ur', 'e'), 48),\n", + " (('ke', '.'), 48),\n", + " (('.m', 'c'), 48),\n", + " (('ak', 'o'), 48),\n", + " (('wy', 'n'), 48),\n", + " (('ul', 'a'), 48),\n", + " (('me', 'i'), 48),\n", + " (('na', 'e'), 48),\n", + " (('om', 'e'), 48),\n", + " (('ma', 's'), 48),\n", + " (('ra', 's'), 48),\n", + " (('ja', 'v'), 48),\n", + " (('ui', 'n'), 47),\n", + " (('oe', 'l'), 47),\n", + " (('.z', 'u'), 47),\n", + " (('we', 'n'), 47),\n", + " (('no', 'n'), 47),\n", + " (('ed', 'i'), 47),\n", + " (('in', 'c'), 47),\n", + " (('aa', 'd'), 47),\n", + " (('ei', 's'), 47),\n", + " (('ce', 'n'), 47),\n", + " (('.o', 'r'), 47),\n", + " (('za', 'm'), 47),\n", + " (('za', 'k'), 47),\n", + " (('ir', 'o'), 47),\n", + " (('aa', 'l'), 46),\n", + " (('nz', 'i'), 46),\n", + " (('ee', 's'), 46),\n", + " (('la', 'u'), 46),\n", + " (('on', 'd'), 46),\n", + " (('ny', 'l'), 46),\n", + " (('se', 'a'), 46),\n", + " (('em', 'o'), 46),\n", + " (('.c', 'y'), 46),\n", + " (('si', 'm'), 46),\n", + " (('mi', 's'), 46),\n", + " (('av', 'y'), 46),\n", + " (('ba', '.'), 46),\n", + " (('um', 'a'), 46),\n", + " (('kh', 'y'), 46),\n", + " (('ru', 's'), 46),\n", + " (('ah', 'e'), 46),\n", + " (('rs', 'h'), 46),\n", + " (('ya', 'a'), 46),\n", + " (('pa', 'r'), 45),\n", + " (('he', 'a'), 45),\n", + " (('fe', 'r'), 45),\n", + " (('ys', 'i'), 45),\n", + " (('yd', 'a'), 45),\n", + " (('ma', 'x'), 45),\n", + " (('.g', 'u'), 45),\n", + " (('nv', 'i'), 45),\n", + " (('ze', '.'), 45),\n", + " (('ta', 'm'), 45),\n", + " (('at', 'o'), 45),\n", + " (('jo', 'n'), 45),\n", + " (('aj', '.'), 45),\n", + " (('av', '.'), 45),\n", + " (('rl', 'o'), 44),\n", + " (('sk', 'y'), 44),\n", + " (('nt', 'h'), 44),\n", + " (('it', 'y'), 44),\n", + " (('ja', 'd'), 44),\n", + " (('in', 'l'), 44),\n", + " (('ki', 'm'), 44),\n", + " (('os', 'i'), 44),\n", + " (('ge', '.'), 44),\n", + " (('fr', 'e'), 44),\n", + " (('kl', 'e'), 44),\n", + " (('it', 'z'), 44),\n", + " (('sa', 'h'), 44),\n", + " (('.c', 'i'), 44),\n", + " (('en', 'o'), 44),\n", + " (('de', 'a'), 44),\n", + " (('ay', 'r'), 44),\n", + " (('ev', 'o'), 44),\n", + " (('co', 'n'), 44),\n", + " (('sh', 'o'), 44),\n", + " (('hm', 'a'), 44),\n", + " (('ec', 'k'), 44),\n", + " (('ja', 'x'), 44),\n", + " (('ba', 's'), 44),\n", + " (('dh', 'a'), 44),\n", + " (('on', 'y'), 43),\n", + " (('si', 'd'), 43),\n", + " (('ye', '.'), 43),\n", + " (('ys', 't'), 43),\n", + " (('lo', 'u'), 43),\n", + " (('en', 'y'), 43),\n", + " (('yv', 'i'), 43),\n", + " (('sm', 'a'), 43),\n", + " (('uh', 'a'), 43),\n", + " (('ve', 'e'), 43),\n", + " (('mi', 't'), 43),\n", + " (('th', 'o'), 43),\n", + " (('ym', 'a'), 43),\n", + " (('di', 's'), 42),\n", + " ...]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_trigrams = sorted(trigrams.items(), key = lambda item: -item[1])\n", + "sorted_trigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "628\n" + ] + } + ], + "source": [ + "elements = []\n", + "\n", + "for trigram in sorted_trigrams:\n", + " elements.append(trigram[0][0])\n", + " elements.append(trigram[0][1])\n", + "\n", + "\n", + "elements = list(set(elements))\n", + "\n", + "print(len(elements))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "eltoi = {el: i for i, el in enumerate(elements)}\n", + "#print(eltoi)\n", + "break_int = eltoi[\".\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# int to string mapping\n", + "itoel = {i:el for i, el in enumerate(elements)}\n", + "#print(itoel)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".e m: 415 15\n", + "em m: 263 15\n", + "mm a: 343 91\n", + "ma .: 581 217\n", + ".o l: 195 137\n", + "ol i: 309 512\n", + "li v: 37 391\n", + "iv i: 221 512\n", + "vi a: 517 91\n", + "ia .: 230 217\n", + ".a v: 14 391\n", + "av a: 311 91\n", + "va .: 399 217\n", + ".i s: 404 283\n", + "is a: 589 91\n", + "sa b: 183 367\n", + "ab e: 202 348\n", + "be l: 147 137\n", + "el l: 162 137\n", + "ll a: 318 91\n", + "la .: 344 217\n", + ".s o: 56 325\n", + "so p: 539 560\n", + "op h: 433 497\n", + "ph i: 379 512\n", + "hi a: 35 91\n", + "ia .: 230 217\n", + ".c h: 559 497\n", + "ch a: 51 91\n", + "ha r: 370 596\n", + "ar l: 284 137\n", + "rl o: 341 325\n", + "lo t: 590 173\n", + "ot t: 242 173\n", + "tt e: 63 348\n", + "te .: 170 217\n", + ".m i: 440 512\n", + "mi a: 319 91\n", + "ia .: 230 217\n", + ".a m: 14 15\n", + "am e: 570 348\n", + "me l: 304 137\n", + "el i: 162 512\n", + "li a: 37 91\n", + "ia .: 230 217\n", + ".h a: 220 91\n", + "ha r: 370 596\n", + "ar p: 284 560\n", + "rp e: 510 348\n", + "pe r: 299 596\n", + "er .: 359 217\n", + ".e v: 415 391\n", + "ev e: 527 348\n", + "ve l: 307 137\n", + "el y: 162 224\n", + "ly n: 387 613\n", + "yn .: 577 217\n", + ".a b: 14 367\n", + "ab i: 202 512\n", + "bi g: 608 117\n", + "ig a: 381 91\n", + "ga i: 467 512\n", + "ai l: 437 137\n", + "il .: 368 217\n", + ".e m: 415 15\n", + "em i: 263 512\n", + "mi l: 319 137\n", + "il y: 368 224\n", + "ly .: 387 217\n", + ".e l: 415 137\n", + "el i: 162 512\n", + "li z: 37 565\n", + "iz a: 529 91\n", + "za b: 475 367\n", + "ab e: 202 348\n", + "be t: 147 173\n", + "et h: 289 497\n", + "th .: 438 217\n", + ".m i: 440 512\n", + "mi l: 319 137\n", + "il a: 368 91\n", + "la .: 344 217\n", + ".e l: 415 137\n", + "el l: 162 137\n", + "ll a: 318 91\n", + "la .: 344 217\n", + ".a v: 14 391\n", + "av e: 311 348\n", + "ve r: 307 596\n", + "er y: 359 224\n", + "ry .: 77 217\n", + ".s o: 56 325\n", + "so f: 539 548\n", + "of i: 372 512\n", + "fi a: 544 91\n", + "ia .: 230 217\n", + ".c a: 559 91\n", + "ca m: 146 15\n", + "am i: 570 512\n", + "mi l: 319 137\n", + "il a: 368 91\n", + "la .: 344 217\n", + ".a r: 14 596\n", + "ar i: 284 512\n", + "ri a: 413 91\n", + "ia .: 230 217\n", + ".s c: 56 102\n", + "sc a: 130 91\n", + "ca r: 146 596\n", + "ar l: 284 137\n", + "rl e: 341 348\n", + "le t: 595 173\n", + "et t: 289 173\n", + "tt .: 63 217\n", + ".v i: 11 512\n", + "vi c: 517 102\n", + "ic t: 442 173\n", + "ct o: 557 325\n", + "to r: 199 596\n", + "or i: 335 512\n", + "ri a: 413 91\n", + "ia .: 230 217\n", + ".m a: 440 91\n", + "ma d: 581 312\n", + "ad i: 58 512\n", + "di s: 92 283\n", + "is o: 589 325\n", + "so n: 539 613\n", + "on .: 256 217\n", + ".l u: 81 115\n", + "lu n: 275 613\n", + "un a: 280 91\n", + "na .: 262 217\n", + ".g r: 513 596\n", + "gr a: 152 91\n", + "ra c: 141 102\n", + "ac e: 121 348\n", + "ce .: 159 217\n", + ".c h: 559 497\n", + "ch l: 51 137\n", + "hl o: 501 325\n", + "lo e: 590 348\n", + "oe .: 519 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.: 359 217\n", + ".c a: 559 91\n", + "ca s: 146 283\n", + "as e: 150 348\n", + "se .: 57 217\n", + ".d a: 25 91\n", + "da v: 104 391\n", + "av i: 311 512\n", + "vi s: 517 283\n", + "is .: 589 217\n", + ".a s: 14 283\n", + "as a: 150 91\n", + "sa .: 183 217\n", + ".d a: 25 91\n", + "da r: 104 596\n", + "ar i: 284 512\n", + "ri u: 413 115\n", + "iu s: 82 283\n", + "us .: 229 217\n", + ".i s: 404 283\n", + "is a: 589 91\n", + "sa i: 183 512\n", + "ai a: 437 91\n", + "ia s: 230 283\n", + "as .: 150 217\n", + ".a d: 14 312\n", + "ad e: 58 348\n", + "de n: 605 613\n", + "en .: 493 217\n", + ".j a: 534 91\n", + "ja i: 172 512\n", + "ai m: 437 15\n", + "im e: 300 348\n", + "me .: 304 217\n", + ".l a: 81 91\n", + "la n: 344 613\n", + "an d: 330 312\n", + "nd y: 617 224\n", + "dy n: 537 613\n", + "yn .: 577 217\n", + ".r a: 76 91\n", + "ra u: 141 115\n", + "au l: 123 137\n", + "ul .: 67 217\n", + ".n i: 336 512\n", + "ni k: 606 213\n", + "ik o: 567 325\n", + "ko .: 485 217\n", + ".t r: 185 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.: 46 217\n", + ".i s: 404 283\n", + "is s: 589 283\n", + "ss a: 282 91\n", + "sa c: 183 102\n", + "ac .: 121 217\n", + ".j a: 534 91\n", + "ja s: 172 283\n", + "as i: 150 512\n", + "si a: 468 91\n", + "ia h: 230 497\n", + "ah .: 31 217\n", + ".c a: 559 91\n", + "ca l: 146 137\n", + "al l: 259 137\n", + "ll e: 318 348\n", + "le n: 595 613\n", + "en .: 493 217\n", + ".f o: 64 325\n", + "fo r: 212 596\n", + "or r: 335 596\n", + "rr e: 447 348\n", + "re s: 528 283\n", + "es t: 547 173\n", + "st .: 306 217\n", + ".m a: 440 91\n", + "ma k: 581 213\n", + "ak a: 533 91\n", + "ka i: 174 512\n", + "ai .: 437 217\n", + ".c r: 559 596\n", + "cr e: 295 348\n", + "re w: 528 360\n", + "ew .: 158 217\n", + ".k o: 574 325\n", + "ko b: 485 367\n", + "ob e: 408 348\n", + "be .: 147 217\n", + ".b o: 134 325\n", + "bo .: 530 217\n", + ".j u: 534 115\n", + "ju l: 94 137\n", + "ul i: 67 512\n", + "li e: 37 348\n", + "ie n: 376 613\n", + "en .: 493 217\n", + ".m a: 440 91\n", + "ma t: 581 173\n", + "at h: 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h: 35 497\n", + "ih e: 255 348\n", + "he n: 203 613\n", + "en g: 493 117\n", + "ng .: 61 217\n", + ".z i: 431 512\n", + "zi a: 169 91\n", + "ia a: 230 91\n", + "aa n: 503 613\n", + "an .: 330 217\n", + ".z i: 431 512\n", + "zi c: 169 102\n", + "ic h: 442 497\n", + "ch e: 51 348\n", + "he n: 203 613\n", + "en .: 493 217\n", + ".z i: 431 512\n", + "zi d: 169 312\n", + "id o: 279 325\n", + "do n: 451 613\n", + "on .: 256 217\n", + ".z i: 431 512\n", + "zi e: 169 348\n", + "ie n: 376 613\n", + "en .: 493 217\n", + ".z i: 431 512\n", + "zi e: 169 348\n", + "ie r: 376 596\n", + "er .: 359 217\n", + ".z i: 431 512\n", + "zi e: 169 348\n", + "ie r: 376 596\n", + "er r: 359 596\n", + "rr e: 447 348\n", + "re .: 528 217\n", + ".z i: 431 512\n", + "zi h: 169 497\n", + "ih i: 255 512\n", + "hi r: 35 596\n", + "ir .: 439 217\n", + ".z i: 431 512\n", + "zi m: 169 15\n", + "im .: 300 217\n", + ".z i: 431 512\n", + "zi n: 169 613\n", + "in .: 52 217\n", + ".z i: 431 512\n", + "zi s: 169 283\n", + "is h: 589 497\n", + "sh e: 314 348\n", + "he .: 203 217\n", + ".z m: 431 15\n", + "zm a: 459 91\n", + "ma r: 581 596\n", + "ar i: 284 512\n", + "ri .: 413 217\n", + ".z o: 431 325\n", + "zo e: 60 348\n", + "oe l: 519 137\n", + "el .: 162 217\n", + ".z o: 431 325\n", + "zo l: 60 137\n", + "ol a: 309 91\n", + "la .: 344 217\n", + ".z u: 431 115\n", + "zu b: 136 367\n", + "ub e: 520 348\n", + "be r: 147 596\n", + "er .: 359 217\n", + ".z u: 431 115\n", + "zu b: 136 367\n", + "ub e: 520 348\n", + "be y: 147 224\n", + "ey r: 531 596\n", + "yr .: 445 217\n", + ".z y: 431 224\n", + "zy e: 225 348\n", + "ye l: 116 137\n", + "el l: 162 137\n", + "ll .: 318 217\n", + ".z y: 431 224\n", + "zy h: 225 497\n", + "yh e: 448 348\n", + "he e: 203 348\n", + "ee m: 320 15\n", + "em .: 263 217\n", + ".z y: 431 224\n", + "zy k: 225 213\n", + "yk e: 254 348\n", + "ke e: 449 348\n", + "ee m: 320 15\n", + "em .: 263 217\n", + ".z y: 431 224\n", + "zy l: 225 137\n", + "yl a: 265 91\n", + "la s: 344 283\n", + "as .: 150 217\n", + ".z y: 431 224\n", + "zy r: 225 596\n", + "yr a: 445 91\n", + "ra n: 141 613\n", + "an .: 330 217\n", + ".z y: 431 224\n", + "zy r: 225 596\n", + "yr i: 445 512\n", + "ri e: 413 348\n", + "ie .: 376 217\n", + ".z y: 431 224\n", + "zy r: 225 596\n", + "yr o: 445 325\n", + "ro n: 18 613\n", + "on .: 256 217\n", + ".z z: 431 565\n", + "zz y: 110 224\n", + "zy z: 225 565\n", + "yz x: 155 405\n", + "zx .: 0 217\n", + "tensor([415, 263, 343, ..., 225, 155, 0])\n", + "tensor([ 15, 15, 91, ..., 565, 405, 217])\n" + ] + } + ], + "source": [ + "# First let's create a training set of bigrams (x, y)\n", + "\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " el1 = ch1+ch2\n", + " el2 = ch3\n", + "\n", + " ix1 = eltoi[el1]\n", + " ix2 = eltoi[el2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " print(f\"{el1} {el2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "\n", + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples: 196113\n" + ] + } + ], + "source": [ + "# Create the dataset\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2, ch3 in zip(chs, chs[1:], chs[2:]):\n", + " el1 = ch1+ch2\n", + " el2 = ch3\n", + "\n", + " ix1 = eltoi[el1]\n", + " ix2 = eltoi[el2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "num = xs.nelement()\n", + "\n", + "print(\"Number of examples: \", num)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "W = torch.randn((628, 628), generator=g, requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 999: 100%|██████████| 1000/1000 [14:30<00:00, 1.15Epoch/s, loss=2.31, time=873]\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 150\n", + "alpha = 0.01\n", + "\n", + "t_start = time.time()\n", + "\n", + "# Gpu accelerated now\n", + "#W.to(device)\n", + "xs.to(device)\n", + "ys.to(device)\n", + "\n", + "with tqdm(range(500), unit=\"Epoch\") as tepoch:\n", + " for epoch in tepoch:\n", + " tepoch.set_description(f\"Epoch {epoch}\")\n", + " \n", + "\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=628).float().to(device)\n", + "\n", + " logits = xenc @ W.to(device) # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = (counts / counts.sum(1, keepdim=True)) \n", + " \n", + " # print(probs.shape)\n", + " # print(ys.shape)\n", + "\n", + " # # regularised loss\n", + " loss = -probs[torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", + "\n", + " # Backward pass\n", + " W.grad = None # set gradient to zero\n", + " loss.backward()\n", + "\n", + " # update\n", + " W.data += -lr * W.grad # gradient descent\n", + "\n", + " tepoch.set_postfix(loss=loss.item(), time=time.time() - t_start)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "xlekeavlcg.kabtudirb.kecttzzwgy.\n", + "\n", + "ucaanijizbmdwmneerxdcujykshcdbssriwwdvidrenthujgwil.\n", + "\n", + "vhwnetvdldqanvibayeb.\n", + "\n", + "spwbrotwovonkylckigzrqiyuwaystmkxbgvlmaeanoelezemnuuebeux.\n", + "\n", + "ifezdhwwuymluwrsthwki.\n", + "\n" + ] + } + ], + "source": [ + "# finally, sample from the 'neural net' model\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "\n", + "for i in range(5):\n", + " \n", + " out = []\n", + " ix = 0\n", + " while True:\n", + " \n", + " # ----------\n", + " # BEFORE:\n", + " #p = P[ix]\n", + " # ----------\n", + " # NOW:\n", + " xenc = F.one_hot(torch.tensor([ix]), num_classes=628).float()\n", + " logits = xenc @ W # predict log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n", + " # ----------\n", + " \n", + " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", + " out.append(itoel[ix])\n", + " if ix == break_int:\n", + " break\n", + " print(''.join(out))\n", + " print()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/makemore/1_makemore_bigram_torch.ipynb b/my_code/makemore/1_makemore_bigram_torch.ipynb new file mode 100644 index 00000000..64ac3720 --- /dev/null +++ b/my_code/makemore/1_makemore_bigram_torch.ipynb @@ -0,0 +1,2653 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['emma',\n", + " 'olivia',\n", + " 'ava',\n", + " 'isabella',\n", + " 'sophia',\n", + " 'charlotte',\n", + " 'mia',\n", + " 'amelia',\n", + " 'harper',\n", + " 'evelyn']" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()\n", + "\n", + "words[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('.', 'e'): 1531,\n", + " ('e', 'm'): 769,\n", + " ('m', 'm'): 168,\n", + " ('m', 'a'): 2590,\n", + " ('a', '.'): 6640,\n", + " ('.', 'o'): 394,\n", + " ('o', 'l'): 619,\n", + " ('l', 'i'): 2480,\n", + " ('i', 'v'): 269,\n", + " ('v', 'i'): 911,\n", + " ('i', 'a'): 2445,\n", + " ('.', 'a'): 4410,\n", + " ('a', 'v'): 834,\n", + " ('v', 'a'): 642,\n", + " ('.', 'i'): 591,\n", + " ('i', 's'): 1316,\n", + " ('s', 'a'): 1201,\n", + " ('a', 'b'): 541,\n", + " ('b', 'e'): 655,\n", + " ('e', 'l'): 3248,\n", + " ('l', 'l'): 1345,\n", + " ('l', 'a'): 2623,\n", + " ('.', 's'): 2055,\n", + " 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bigrams[bigram] = bigrams.get(bigram, 0) + 1\n", + "\n", + "bigrams" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(('n', '.'), 6763),\n", + " (('a', '.'), 6640),\n", + " (('a', 'n'), 5438),\n", + " (('.', 'a'), 4410),\n", + " (('e', '.'), 3983),\n", + " (('a', 'r'), 3264),\n", + " (('e', 'l'), 3248),\n", + " (('r', 'i'), 3033),\n", + " (('n', 'a'), 2977),\n", + " (('.', 'k'), 2963),\n", + " (('l', 'e'), 2921),\n", + " (('e', 'n'), 2675),\n", + " (('l', 'a'), 2623),\n", + " (('m', 'a'), 2590),\n", + " (('.', 'm'), 2538),\n", + " (('a', 'l'), 2528),\n", + " (('i', '.'), 2489),\n", + " (('l', 'i'), 2480),\n", + " (('i', 'a'), 2445),\n", + " (('.', 'j'), 2422),\n", + " (('o', 'n'), 2411),\n", + " (('h', '.'), 2409),\n", + " (('r', 'a'), 2356),\n", + " (('a', 'h'), 2332),\n", + " (('h', 'a'), 2244),\n", + " (('y', 'a'), 2143),\n", + " (('i', 'n'), 2126),\n", + " (('.', 's'), 2055),\n", + " (('a', 'y'), 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1),\n", + " (('f', 'g'), 1),\n", + " (('z', 'g'), 1),\n", + " (('c', 'p'), 1),\n", + " (('p', 'k'), 1),\n", + " (('p', 'm'), 1),\n", + " (('x', 'n'), 1),\n", + " (('s', 'q'), 1),\n", + " (('k', 'f'), 1),\n", + " (('m', 'k'), 1),\n", + " (('x', 'h'), 1),\n", + " (('g', 'f'), 1),\n", + " (('v', 'b'), 1),\n", + " (('j', 'p'), 1),\n", + " (('g', 'z'), 1),\n", + " (('v', 'd'), 1),\n", + " (('d', 'b'), 1),\n", + " (('v', 'h'), 1),\n", + " (('h', 'h'), 1),\n", + " (('g', 'v'), 1),\n", + " (('d', 'q'), 1),\n", + " (('x', 'b'), 1),\n", + " (('w', 'z'), 1),\n", + " (('h', 'q'), 1),\n", + " (('j', 'b'), 1),\n", + " (('x', 'm'), 1),\n", + " (('w', 'g'), 1),\n", + " (('t', 'b'), 1),\n", + " (('z', 'x'), 1)]" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(bigrams.items(), key = lambda item: -item[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "N = torch.zeros((27, 27), dtype=torch.int32)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n" + ] + } + ], + "source": [ + "chars = sorted(list(set(\"\".join(words))))\n", + "print(chars)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}\n" + ] + } + ], + "source": [ + "# string to int mapping\n", + "stoi = {}\n", + "for i, char in enumerate(chars):\n", + " stoi[char] = i+1\n", + "\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n" + ] + } + ], + "source": [ + "stoi = {char:i+1 for i, char in enumerate(chars)}\n", + "stoi['.'] = 0\n", + "print(stoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], + "source": [ + "itos = {i+1:char for i, char in enumerate(chars)}\n", + "itos[0] = \".\"\n", + "print(itos)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# plt.figure(figsize=(16,16))\n", + "# plt.imshow(N, cmap='Blues')\n", + "# for i in range(27):\n", + "# for j in range(27):\n", + "# chstr = itos[i] + itos[j]\n", + "# plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n", + "# plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n", + "# plt.axis('off');" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's fill bigrams now into N\n", + "for w in words:\n", + " #chs = [''] + list(w) + ['']\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " itx1 = stoi[ch1]\n", + " itx2 = stoi[ch2]\n", + " N[itx1, itx2] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,16))\n", + "plt.imshow(N, cmap='Blues')\n", + "for i in range(27):\n", + " for j in range(27):\n", + " chstr = itos[i] + itos[j]\n", + " plt.text(j, i, chstr, ha=\"center\", va=\"bottom\", color='gray')\n", + " plt.text(j, i, N[i, j].item(), ha=\"center\", va=\"top\", color='gray')\n", + "plt.axis('off');" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0, 4410, 1306, 1542, 1690, 1531, 417, 669, 874, 591, 2422, 2963,\n", + " 1572, 2538, 1146, 394, 515, 92, 1639, 2055, 1308, 78, 376, 307,\n", + " 134, 535, 929], dtype=torch.int32)" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now lets'make probability distributions row wise\n", + "#sum(N[0] / sum(N[0]))\n", + "N[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "#P = N.float()\n", + "#P.sum(1).shape # we need keepdim=True otherwise calculations areincorrect" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "P = (N+1).float()\n", + "P /= P.sum(1, keepdim=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mor.\n", + "axx.\n", + "minaymoryles.\n", + "kondlaisah.\n", + "anchshizarie.\n" + ] + } + ], + "source": [ + "g = torch.Generator().manual_seed(2147483647)\n", + "\n", + "for i in range(5):\n", + " out = []\n", + " ix = 0\n", + " while True:\n", + " p = P[ix]\n", + " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", + " out.append(itos[ix]) \n", + " if ix==0: # we get a \".\"\n", + " break\n", + " \n", + " print(\"\".join(out))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We want to make a metric to measure (Quality) how good the model is. i.e we want a loss" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".e: 0.0478\n", + "em: 0.0377\n", + "mm: 0.0253\n", + "ma: 0.3885\n", + "a.: 0.1958\n", + ".o: 0.0123\n", + "ol: 0.0779\n", + "li: 0.1774\n", + "iv: 0.0152\n", + "vi: 0.3508\n", + "ia: 0.1380\n", + "a.: 0.1958\n", + ".a: 0.1376\n", + "av: 0.0246\n", + "va: 0.2473\n", + "a.: 0.1958\n" + ] + } + ], + "source": [ + "# Let's fill bigrams now into N\n", + "for w in words[:3]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", + " print(f\"{ch1}{ch2}: {prob:.4f}\")\n", + "\n", + "# There are 27 characters. That means during initialization 1/27 should be probability of each.\n", + "# If the probability is higger than that for some that means the model has learned something\n", + "# Which it has actually\n", + "# These rpobability should be near to 1 atleast on the training set meaning the model is confident." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Likelihood\n", + "\n", + "The product of probability distributions should be high\n", + "\n", + "Log probability; which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", + "\n", + "Loss function has the semantic that lower is better but with log-liklihood lower is worse so we use **negative log likelihood**\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GOAL: maximize likelihood of the data w.r.t. model parameters (statistical modeling)\n", + "\n", + "equivalent to maximizing the log likelihood (because log is monotonic)\n", + "\n", + "equivalent to minimizing the negative log likelihood\n", + "\n", + "equivalent to minimizing the average negative log likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log_likelihood=tensor(-559951.5625)\n", + "nll/(n+1)=tensor(2.4543)\n" + ] + } + ], + "source": [ + "log_likelihood = 0.0\n", + "# Let's fill bigrams now into N\n", + "n = 0\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", + " logprob = torch.log(prob) # log probability # which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", + " log_likelihood += logprob\n", + " n += 1\n", + " #print(f\"{ch1}{ch2}: {prob:.4f} {logprob: .4f}\")\n", + "\n", + "print(f\"{log_likelihood=}\") # Maximum it can go is 0 when all probs are 1\n", + "nll = -log_likelihood\n", + "print(f\"{nll/(n+1)=}\") # We want average # the lower it is the better we are" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can estimate probability for any word (name)\n", + "\n", + "But if we check probability for a name like **ibadqq** the model gives a loss of inf which means the probability is exactly zero.\n", + "This is highly undesirable. q followed by q occurs 0 times as you can see from the initial graph.\n", + "\n", + "To tackle this we can do something like model smoothning. We will add 1 to all occurances of bigrams. It will eliminate this\n", + "problem. We could even 1000 to the counts. Adding a larger number will make the model more smooth. But here we will just add 1." + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log_likelihood=tensor(-559951.5625)\n", + "nll/(n+1)=tensor(2.4543)\n" + ] + } + ], + "source": [ + "log_likelihood = 0.0\n", + "# Let's fill bigrams now into N\n", + "n = 0\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " prob = P[ix1, ix2] # probability model assigns to these bigrams\n", + " logprob = torch.log(prob) # log probability # which is sum of log of individual probabilitites: log(abc) = loga(a) + log(b) + log(c)\n", + " log_likelihood += logprob\n", + " n += 1\n", + " #print(f\"{ch1}{ch2}: {prob:.4f} {logprob: .4f}\")\n", + "\n", + "print(f\"{log_likelihood=}\") # Maximum it can go is 0 when all probs are 1\n", + "nll = -log_likelihood\n", + "print(f\"{nll/(n+1)=}\") # We want average # the lower it is the better we are" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now we will do try to reach the same place but with a different approach; using neural net" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".e: 0 5\n", + "em: 5 13\n", + "mm: 13 13\n", + "ma: 13 1\n", + "a.: 1 0\n", + "tensor([ 0, 5, 13, 13, 1])\n", + "tensor([ 5, 13, 13, 1, 0])\n" + ] + } + ], + "source": [ + "# First let's create a training set of bigrams (x, y)\n", + "\n", + "xs, ys = [], []\n", + "\n", + "for w in words[:1]:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "\n", + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### one hot encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([5, 27])\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import torch.nn.functional as F\n", + "xenc = F.one_hot(xs, num_classes=27).float() # we dont want an int we want a float\n", + "print(xenc.shape)\n", + "plt.imshow(xenc, cmap='Blues')" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.float32" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xenc.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's construct first neuron" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-7.6148e-01, -3.1424e-01, 1.9481e-02, -2.2533e-01, 4.5341e-01,\n", + " 4.2244e-01, -6.9604e-01, -1.6960e+00, -3.3331e-01, 3.7474e-01,\n", + " -8.0611e-02, -1.1231e+00, -4.4109e-01, -1.2095e+00, 4.1020e-01,\n", + " -1.5015e+00, -8.7129e-01, 2.9803e-01, -2.3551e+00, 3.4084e-04,\n", + " -9.6937e-01, 6.0936e-01, -8.3752e-01, 3.3975e-01, 1.5214e+00,\n", + " -8.9153e-01, 1.4327e+00],\n", + " [ 9.3261e-01, -9.2786e-01, -6.2602e-01, -9.7203e-01, 7.9021e-01,\n", + " 4.0905e-01, -9.1781e-01, 1.3454e+00, -4.0126e-01, -6.0294e-01,\n", + " -5.3656e-01, 8.8367e-01, 5.4779e-01, -3.5715e-01, 1.8984e-01,\n", + " 4.1010e-01, 1.6740e+00, 1.7788e+00, -5.2731e-01, 1.9860e+00,\n", + " 2.4396e+00, -1.4672e+00, 1.3123e+00, -6.3350e-01, 3.4797e-01,\n", + " -8.2656e-01, 4.2729e-02],\n", + " [ 2.1002e-01, -8.5984e-01, -9.2525e-01, -8.6639e-01, -1.2768e+00,\n", + " 3.8859e-01, 8.8408e-01, 4.0335e-01, 4.6471e-01, -6.9420e-01,\n", + " -1.5782e+00, -1.1070e-01, 3.6890e-02, 7.1488e-01, 4.3240e-01,\n", + " -4.2933e-01, 1.3047e+00, -9.7873e-01, 9.5837e-02, 6.6297e-01,\n", + " 8.1560e-01, 1.0019e+00, 5.6406e-01, -9.2049e-01, 3.8910e-01,\n", + " 1.3193e+00, 1.3622e+00],\n", + " [ 2.1002e-01, -8.5984e-01, -9.2525e-01, -8.6639e-01, -1.2768e+00,\n", + " 3.8859e-01, 8.8408e-01, 4.0335e-01, 4.6471e-01, -6.9420e-01,\n", + " -1.5782e+00, -1.1070e-01, 3.6890e-02, 7.1488e-01, 4.3240e-01,\n", + " -4.2933e-01, 1.3047e+00, -9.7873e-01, 9.5837e-02, 6.6297e-01,\n", + " 8.1560e-01, 1.0019e+00, 5.6406e-01, -9.2049e-01, 3.8910e-01,\n", + " 1.3193e+00, 1.3622e+00],\n", + " [ 1.3709e+00, -1.9400e+00, -2.4378e-01, 3.2082e-01, -1.1422e+00,\n", + " -8.0290e-01, 1.4555e+00, 7.6867e-01, -4.1894e-01, 1.0218e+00,\n", + " -1.0205e+00, -5.4941e-03, -7.9474e-02, -5.8220e-01, 1.2662e+00,\n", + " 7.7145e-01, -1.6896e-01, 3.9349e-01, 5.0482e-01, -2.5368e+00,\n", + " 3.9033e-01, 8.8935e-01, 5.9883e-01, -3.6300e-01, -1.2209e-01,\n", + " 6.1462e-01, 4.4726e-01]])" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W = torch.randn((27, 27)) # We created 27 neurons \n", + "# (5, 27) @ (27, 27) = (5, 27) # all 5 inputs will be trained for all 27 possible outputs this way\n", + "xenc @ W\n", + "\n", + "# W will be our only NN layer, the simplest dumbest type lol.\n", + "\n", + "# For every input we want to generate a probability distibution over all possible outputs (27 here)\n", + "\n", + "## We want interpret these results now somehow.\n", + "# We dont want counts directly because they are integers. What we will do is we will get log of counts from neural net.\n", + "# We will then exponentiate them to get counts back (somewhat)\n", + "\n", + "# for exp(0) = 1\n", + "# for exp(>0) > 1\n", + "# for exp(<0) < 1 \n", + "\n", + "# these exponentiated values will be our counts they cant be below 0." + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.4670, 0.7303, 1.0197, 0.7983, 1.5737, 1.5257, 0.4986, 0.1834,\n", + " 0.7165, 1.4546, 0.9226, 0.3253, 0.6433, 0.2983, 1.5071, 0.2228,\n", + " 0.4184, 1.3472, 0.0949, 1.0003, 0.3793, 1.8393, 0.4328, 1.4046,\n", + " 4.5785, 0.4100, 4.1900],\n", + " [ 2.5411, 0.3954, 0.5347, 0.3783, 2.2039, 1.5054, 0.3994, 3.8397,\n", + " 0.6695, 0.5472, 0.5848, 2.4198, 1.7294, 0.6997, 1.2091, 1.5070,\n", + " 5.3336, 5.9228, 0.5902, 7.2860, 11.4683, 0.2306, 3.7148, 0.5307,\n", + " 1.4162, 0.4376, 1.0437],\n", + " [ 1.2337, 0.4232, 0.3964, 0.4205, 0.2789, 1.4749, 2.4208, 1.4968,\n", + " 1.5916, 0.4995, 0.2063, 0.8952, 1.0376, 2.0439, 1.5409, 0.6509,\n", + " 3.6867, 0.3758, 1.1006, 1.9405, 2.2605, 2.7235, 1.7578, 0.3983,\n", + " 1.4757, 3.7407, 3.9049],\n", + " [ 1.2337, 0.4232, 0.3964, 0.4205, 0.2789, 1.4749, 2.4208, 1.4968,\n", + " 1.5916, 0.4995, 0.2063, 0.8952, 1.0376, 2.0439, 1.5409, 0.6509,\n", + " 3.6867, 0.3758, 1.1006, 1.9405, 2.2605, 2.7235, 1.7578, 0.3983,\n", + " 1.4757, 3.7407, 3.9049],\n", + " [ 3.9389, 0.1437, 0.7837, 1.3783, 0.3191, 0.4480, 4.2868, 2.1569,\n", + " 0.6577, 2.7781, 0.3604, 0.9945, 0.9236, 0.5587, 3.5472, 2.1629,\n", + " 0.8445, 1.4821, 1.6567, 0.0791, 1.4775, 2.4335, 1.8200, 0.6956,\n", + " 0.8851, 1.8490, 1.5640]])" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(xenc @ W).exp()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(24)" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logits = xenc @ W # log-counts\n", + "counts = logits.exp() # counts\n", + "probs = counts / counts.sum(1, keepdim=True) # probabilities # normalizse the rows of counts: sum of every row is 1\n", + "probs[0].argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summarised" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ 0, 5, 13, 13, 1])\n", + "tensor([ 5, 13, 13, 1, 0])\n" + ] + } + ], + "source": [ + "print(xs)\n", + "print(ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [], + "source": [ + "xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + "logits = xenc @ W # log-counts\n", + "counts = logits.exp() # counts\n", + "probs = counts / counts.sum(1, keepdim=True) # probabilities # normalizse the rows of counts: sum of every row is 1\n", + "# these two previous lines are the same as softmax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([5, 27])" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "# the forward pass is back-propagateable" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------\n", + "bigram example 1: .e (indexes 0,5)\n", + "input to the neural net: 0\n", + "output probabilities from the neural net: tensor([0.0607, 0.0100, 0.0123, 0.0042, 0.0168, 0.0123, 0.0027, 0.0232, 0.0137,\n", + " 0.0313, 0.0079, 0.0278, 0.0091, 0.0082, 0.0500, 0.2378, 0.0603, 0.0025,\n", + " 0.0249, 0.0055, 0.0339, 0.0109, 0.0029, 0.0198, 0.0118, 0.1537, 0.1459],\n", + " grad_fn=)\n", + "label (actual next character): 5\n", + "probability assigned by the net to the the correct character: 0.01228625513613224\n", + "log likelihood: -4.399273872375488\n", + "negative log likelihood: 4.399273872375488\n", + "--------\n", + "bigram example 2: em (indexes 5,13)\n", + "input to the neural net: 5\n", + "output probabilities from the neural net: tensor([0.0290, 0.0796, 0.0248, 0.0521, 0.1989, 0.0289, 0.0094, 0.0335, 0.0097,\n", + " 0.0301, 0.0702, 0.0228, 0.0115, 0.0181, 0.0108, 0.0315, 0.0291, 0.0045,\n", + " 0.0916, 0.0215, 0.0486, 0.0300, 0.0501, 0.0027, 0.0118, 0.0022, 0.0472],\n", + " grad_fn=)\n", + "label (actual next character): 13\n", + "probability assigned by the net to the the correct character: 0.018050700426101685\n", + "log likelihood: -4.014570713043213\n", + "negative log likelihood: 4.014570713043213\n", + "--------\n", + "bigram example 3: mm (indexes 13,13)\n", + "input to the neural net: 13\n", + "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n", + " 0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n", + " 0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126],\n", + " grad_fn=)\n", + "label (actual next character): 13\n", + "probability assigned by the net to the the correct character: 0.026691533625125885\n", + "log likelihood: -3.623408794403076\n", + "negative log likelihood: 3.623408794403076\n", + "--------\n", + "bigram example 4: ma (indexes 13,1)\n", + "input to the neural net: 13\n", + "output probabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n", + " 0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n", + " 0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126],\n", + " grad_fn=)\n", + "label (actual next character): 1\n", + "probability assigned by the net to the the correct character: 0.07367686182260513\n", + "log likelihood: -2.6080665588378906\n", + "negative log likelihood: 2.6080665588378906\n", + "--------\n", + "bigram example 5: a. (indexes 1,0)\n", + "input to the neural net: 1\n", + "output probabilities from the neural net: tensor([0.0150, 0.0086, 0.0396, 0.0100, 0.0606, 0.0308, 0.1084, 0.0131, 0.0125,\n", + " 0.0048, 0.1024, 0.0086, 0.0988, 0.0112, 0.0232, 0.0207, 0.0408, 0.0078,\n", + " 0.0899, 0.0531, 0.0463, 0.0309, 0.0051, 0.0329, 0.0654, 0.0503, 0.0091],\n", + " grad_fn=)\n", + "label (actual next character): 0\n", + "probability assigned by the net to the the correct character: 0.014977526850998402\n", + "log likelihood: -4.201204299926758\n", + "negative log likelihood: 4.201204299926758\n", + "=========\n", + "average negative log likelihood, i.e. loss = 3.7693049907684326\n" + ] + } + ], + "source": [ + "nlls = torch.zeros(5)\n", + "for i in range(5):\n", + " # i-th bigram:\n", + " x = xs[i].item() # input character index\n", + " y = ys[i].item() # label character index\n", + " print('--------')\n", + " print(f'bigram example {i+1}: {itos[x]}{itos[y]} (indexes {x},{y})')\n", + " print('input to the neural net:', x)\n", + " print('output probabilities from the neural net:', probs[i])\n", + " print('label (actual next character):', y)\n", + " p = probs[i, y]\n", + " print('probability assigned by the net to the the correct character:', p.item())\n", + " logp = torch.log(p)\n", + " print('log likelihood:', logp.item())\n", + " nll = -logp\n", + " print('negative log likelihood:', nll.item())\n", + " nlls[i] = nll\n", + "\n", + "print('=========')\n", + "print('average negative log likelihood, i.e. loss =', nlls.mean().item())" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "# Forwards pass\n", + "xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + "logits = xenc @ W # log-counts\n", + "counts = logits.exp() # counts, equivalent to N\n", + "probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", + "loss = -probs[torch.arange(5), ys].log().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [], + "source": [ + "# Backwards pass\n", + "W.grad = None # set the gradient to zero\n", + "loss.backward() # compute the gradient" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0121, 0.0020, 0.0025, 0.0008, 0.0034, -0.1975, 0.0005, 0.0046,\n", + " 0.0027, 0.0063, 0.0016, 0.0056, 0.0018, 0.0016, 0.0100, 0.0476,\n", + " 0.0121, 0.0005, 0.0050, 0.0011, 0.0068, 0.0022, 0.0006, 0.0040,\n", + " 0.0024, 0.0307, 0.0292],\n", + " [-0.1970, 0.0017, 0.0079, 0.0020, 0.0121, 0.0062, 0.0217, 0.0026,\n", + " 0.0025, 0.0010, 0.0205, 0.0017, 0.0198, 0.0022, 0.0046, 0.0041,\n", + " 0.0082, 0.0016, 0.0180, 0.0106, 0.0093, 0.0062, 0.0010, 0.0066,\n", + " 0.0131, 0.0101, 0.0018],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0058, 0.0159, 0.0050, 0.0104, 0.0398, 0.0058, 0.0019, 0.0067,\n", + " 0.0019, 0.0060, 0.0140, 0.0046, 0.0023, -0.1964, 0.0022, 0.0063,\n", + " 0.0058, 0.0009, 0.0183, 0.0043, 0.0097, 0.0060, 0.0100, 0.0005,\n", + " 0.0024, 0.0004, 0.0094],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", 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0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0125, -0.1705, 0.0194, 0.0133, 0.0270, 0.0080, 0.0105, 0.0100,\n", + " 0.0490, 0.0066, 0.0030, 0.0316, 0.0052, -0.1893, 0.0059, 0.0045,\n", + " 0.0234, 0.0049, 0.0260, 0.0023, 0.0083, 0.0031, 0.0053, 0.0081,\n", + " 0.0482, 0.0187, 0.0051],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000]])" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W.grad\n", + "\n", + "# W[0, 0] is positive. It means the loss will increse if this increases.\n", + "# We can use this gradient information to update the weights" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss before update: 3.7693049907684326\n", + "loss after update: 3.7693049907684326\n" + ] + } + ], + "source": [ + "W.data += -0.1 * W.grad\n", + "\n", + "# Loss should descrease after this update\n", + "\n", + "print('loss before update:', loss.item())\n", + "\n", + "loss = -probs[torch.arange(5), ys].log().mean()\n", + "\n", + "print('loss after update:', loss.item())\n", + "# It is slightly lower" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's train properly now" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples: 228146\n" + ] + } + ], + "source": [ + "# Create the dataset\n", + "xs, ys = [], []\n", + "\n", + "for w in words:\n", + " chs = ['.'] + list(w) + ['.']\n", + " for ch1, ch2 in zip(chs, chs[1:]):\n", + " ix1 = stoi[ch1]\n", + " ix2 = stoi[ch2]\n", + " xs.append(ix1)\n", + " ys.append(ix2)\n", + " #print(f\"{ch1}{ch2}: {ix1} {ix2}\")\n", + "\n", + "xs = torch.tensor(xs)\n", + "ys = torch.tensor(ys)\n", + "num = xs.nelement()\n", + "\n", + "print(\"Number of examples: \", num)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", + "W = torch.zeros((27, 27), requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 3.295837640762329\n", + "loss: 2.52099871635437\n", + "loss: 2.4878063201904297\n", + "loss: 2.4761619567871094\n", + "loss: 2.4702982902526855\n", + "loss: 2.466809034347534\n", + "loss: 2.4645133018493652\n", + "loss: 2.4628946781158447\n", + "loss: 2.4616944789886475\n", + "loss: 2.4607694149017334\n", + "loss: 2.4600346088409424\n", + "loss: 2.459437370300293\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 50\n", + "\n", + "for k in range(300):\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + " logits = xenc @ W # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", + " loss = -probs[torch.arange(num), ys].log().mean()\n", + " if k % 25 == 0:\n", + " print('loss:', loss.item())\n", + "\n", + " # Backward pass\n", + " W.grad = None # set gradient to zero\n", + " loss.backward()\n", + "\n", + " # update\n", + " W.data += -lr * W.grad # gradient descent\n", + "\n", + "# The loss is same as calculated with probability distrubuted\n", + "# We did the same thing but it is way more flexible\n", + "# static approach is not scalable but NN one is" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regularisation" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly initialize 27 neurons weights. Each neuron recieves 27 inputs\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "#W = torch.randn((27, 27), generator=g, requires_grad=True) # We created 27 neurons\n", + "W = torch.zeros((27, 27), requires_grad=True) # We created 27 neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 3.295837640762329\n", + "loss: 2.529900074005127\n", + "loss: 2.500406265258789\n", + "loss: 2.491044044494629\n", + "loss: 2.4867959022521973\n", + "loss: 2.4845311641693115\n", + "loss: 2.4832026958465576\n", + "loss: 2.482370615005493\n", + "loss: 2.4818227291107178\n", + "loss: 2.4814486503601074\n", + "loss: 2.4811856746673584\n", + "loss: 2.4809961318969727\n" + ] + } + ], + "source": [ + "# gradient descent in a loop -> training\n", + "\n", + "lr = 50\n", + "alpha = 0.01\n", + "\n", + "for k in range(300):\n", + " # Forwards pass\n", + " xenc = F.one_hot(xs, num_classes=27).float() # input is one-hot encoded\n", + " logits = xenc @ W # log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " probs = counts / counts.sum(1, keepdim=True) # probabilities for next character \n", + " \n", + " # regularised loss\n", + " loss = -probs[torch.arange(num), ys].log().mean() + (alpha * (W**2).mean())\n", + " if k % 25 == 0:\n", + " print('loss:', loss.item())\n", + "\n", + " # Backward pass\n", + " W.grad = None # set gradient to zero\n", + " loss.backward()\n", + "\n", + " # update\n", + " W.data += -lr * W.grad # gradient descent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Effect of regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "# regularisation here will want to make W elements smaller (0 best case for it)\n", + "# the regular loss will try to make the probility distribution according to training data\n", + "# regularisation is similar to smoothening that we did before\n", + "# Increasing counts: (N + 1) or even (N + 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample from neural net model" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mor.\n", + "axx.\n", + "minaymoryles.\n", + "kondlaisah.\n", + "anchthizarie.\n" + ] + } + ], + "source": [ + "# finally, sample from the 'neural net' model\n", + "g = torch.Generator().manual_seed(2147483647)\n", + "\n", + "for i in range(5):\n", + " \n", + " out = []\n", + " ix = 0\n", + " while True:\n", + " \n", + " # ----------\n", + " # BEFORE:\n", + " #p = P[ix]\n", + " # ----------\n", + " # NOW:\n", + " xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float()\n", + " logits = xenc @ W # predict log-counts\n", + " counts = logits.exp() # counts, equivalent to N\n", + " p = counts / counts.sum(1, keepdims=True) # probabilities for next character\n", + " # ----------\n", + " \n", + " ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n", + " out.append(itos[ix])\n", + " if ix == 0:\n", + " break\n", + " print(''.join(out))\n", + "\n", + "# same output as before exactly.\n", + "# identical models and acheive the same loss\n", + "# mor.\n", + "# axx.\n", + "# minaymoryles.\n", + "# kondlaisah.\n", + "# anchshizarie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/makemore/2_makemore_mlp.ipynb b/my_code/makemore/2_makemore_mlp.ipynb new file mode 100644 index 00000000..952bd909 --- /dev/null +++ b/my_code/makemore/2_makemore_mlp.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open(\"names.txt\", \"r\") as f:\n", + " words = f.read().splitlines()\n", + "\n", + "words[:8]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total names: 32033\n", + "Minimum name length: 2\n", + "Maximum name length: 15\n" + ] + } + ], + "source": [ + "# total words\n", + "print(\"Total names: \", len(words))\n", + "\n", + "print(\"Minimum name length: \", min(len(w) for w in words))\n", + "print(\"Maximum name length: \", max(len(w) for w in words))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stoi: {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26, '.': 0}\n", + "Itos: {1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n" + ] + } + ], + "source": [ + "# making vocabulary mappings\n", + "chars = sorted(list(set(\"\".join(words))))\n", + "stoi = {c: i+1 for i, c in enumerate(chars)}\n", + "stoi[\".\"] = 0\n", + "itos = {i: c for c, i in stoi.items()}\n", + "print(\"Stoi: \", stoi)\n", + "print(\"Itos: \", itos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### build the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "block_size = 3 # context length: how many characters do we take to predict the next one?\n", + "X, Y = [], []\n", + "for w in words[:5]:\n", + " \n", + " #print(w)\n", + " context = [0] * block_size\n", + " for ch in w + '.':\n", + " ix = stoi[ch]\n", + " X.append(context)\n", + " Y.append(ix)\n", + " #print(''.join(itos[i] for i in context), '--->', itos[ix])\n", + " context = context[1:] + [ix] # crop and append\n", + " \n", + "X = torch.tensor(X)\n", + "Y = torch.tensor(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([32, 3]), torch.Size([32]), torch.int64, torch.int64)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape, Y.shape, X.dtype, Y.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# In the paper it has 3 words as inputs\n", + "# And 17000 words with 30 dimensions\n", + "# We only have 27 chars here, let's start with small dimensions for look-up matrix C" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([27, 2])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C = torch.randn(27, 2)\n", + "C.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.9258, 0.9268])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let' embed a single character first\n", + "C[5]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([32, 3, 2])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's embed all\n", + "emb = C[X]\n", + "emb.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's make the hidden layer\n", + "W1 = torch.randn((6, 100)) # 3*2 inputs and 100 hidden units\n", + "b1 = torch.randn(100)" + ] + } + ], + "metadata": { + 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+ "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " return 3*x**2 - 4*x + 5" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f(3.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.arange(-5, 5, 0.25)\n", + "ys = f(xs)\n", + "plt.plot(xs, ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.00000009255109" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now the derivative at a particular point\n", + "# from the definition of derivate\n", + "h = 0.00000001 # lim h->0\n", + "x = 3\n", + "\n", + "f(x+h) # it should be greater than f(x)\n", + "\n", + "# Now Slope\n", + "\n", + "slope = (f(x+h) - f(x)) / h\n", + "slope\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-22.00000039920269" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.00000001 # lim h->0\n", + "x = -3\n", + "\n", + "f(x+h) # it should be greater than f(x)\n", + "\n", + "# Now Slope\n", + "\n", + "slope = (f(x+h) - f(x)) / h\n", + "slope # here it will be negative" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 0.00000001 # lim h->0\n", + "x = 2/3\n", + "\n", + "f(x+h) # it should be greater than f(x)\n", + "\n", + "# Now Slope\n", + "\n", + "slope = (f(x+h) - f(x)) / h\n", + "slope # here it will be zero" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.0\n" + ] + } + ], + "source": [ + "# Now more complex function\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d = a*b + c\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d1 4.0\n", + "d2 3.999699999999999\n", + "slope -3.000000000010772\n" + ] + } + ], + "source": [ + "h = 0.0001\n", + "\n", + "# inputs\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d1 = a*b +c\n", + "\n", + "a += h # increment a by h\n", + "d2 = a*b + c\n", + "\n", + "print(\"d1\", d1)\n", + "print(\"d2\", d2)\n", + "print(\"slope\", (d2-d1)/h)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d1 4.0\n", + "d2 4.0002\n", + "slope 2.0000000000042206\n" + ] + } + ], + "source": [ + "h = 0.0001\n", + "\n", + "# inputs\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d1 = a*b +c\n", + "\n", + "b += h # increment b by h\n", + "d2 = a*b + c\n", + "\n", + "print(\"d1\", d1)\n", + "print(\"d2\", d2)\n", + "print(\"slope\", (d2-d1)/h)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "d1 4.0\n", + "d2 4.0001\n", + "slope 0.9999999999976694\n" + ] + } + ], + "source": [ + "h = 0.0001\n", + "\n", + "# inputs\n", + "a = 2.0\n", + "b = -3.0\n", + "c = 10.0\n", + "\n", + "d1 = a*b +c\n", + "\n", + "c += h # increment c by h\n", + "d2 = a*b + c\n", + "\n", + "print(\"d1\", d1)\n", + "print(\"d2\", d2)\n", + "print(\"slope\", (d2-d1)/h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Value() Object Building" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data):\n", + " self.data = data\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=2.0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# # Now we want to add 2 value objects\n", + "# a = Value(2.0)\n", + "# b = Value(-3.0)\n", + "# c = a + b # This operating is not defined yet\n", + "# c" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\"):\n", + " self.data = data\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " return Value(self.data + other.data, (self, other), \"+\")\n", + "\n", + " def __mul__(self, other):\n", + " return Value(self.data * other.data, (self, other), \"*\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=-1.0)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now we want to add 2 value objects\n", + "a = Value(2.0)\n", + "b = Value(-3.0)\n", + "c = a + b # This operating is not defined yet\n", + "c\n", + "\n", + "# a +b \n", + "# python will do\n", + "# a.__add__(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=4.0)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0)\n", + "b = Value(-3.0)\n", + "c = Value(10.0)\n", + "\n", + "d = a*b +c # (a.__mul__(b)).__add__(c)\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{Value(data=-6.0), Value(data=10.0)}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We want to keep a track of relationship between a, b, c, d\n", + "d._prev # we know the children of the value" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'+'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now we also want to know what operations created them\n", + "d._op" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### These expressions will get very large, we would like to visualise them with this code" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install graphviz\n", + "from graphviz import Digraph\n", + "\n", + "def trace(root):\n", + " # builds a set of all nodes and edges in a graph\n", + " nodes, edges = set(), set()\n", + " def build(v):\n", + " if v not in nodes:\n", + " nodes.add(v)\n", + " for child in v._prev:\n", + " edges.add((child, v))\n", + " build(child)\n", + " build(root)\n", + " return nodes, edges\n", + "\n", + "def draw_dot(root):\n", + " dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n", + " \n", + " nodes, edges = trace(root)\n", + " for n in nodes:\n", + " uid = str(id(n))\n", + " # for any value in the graph, create a rectangular ('record') node for it\n", + " dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n", + " if n._op:\n", + " # if this value is a result of some operation, create an op node for it\n", + " dot.node(name = uid + n._op, label = n._op)\n", + " # and connect this node to it\n", + " dot.edge(uid + n._op, uid)\n", + "\n", + " for n1, n2 in edges:\n", + " # connect n1 to the op node of n2\n", + " dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n", + "\n", + " return dot" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " return Value(self.data + other.data, (self, other), \"+\")\n", + "\n", + " def __mul__(self, other):\n", + " return Value(self.data * other.data, (self, other), \"*\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=-8.0)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0, label=\"a\")\n", + "b = Value(-3.0, label=\"b\")\n", + "c = Value(10.0, label=\"c\")\n", + "e = a*b; e.label=\"e\"\n", + "d = e+c; d.label=\"d\"\n", + "f = Value(-2.0, label=\"f\")\n", + "L = d*f; L.label=\"L\"\n", + "L" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backpropagation" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using this we can visulize the operations\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Now let's calculate gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.000000000000668\n" + ] + } + ], + "source": [ + "def lol():\n", + " h = 0.001\n", + " a = Value(2.0, label=\"a\")\n", + " b = Value(-3.0, label=\"b\")\n", + " c = Value(10.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-2.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + " L1 = L.data\n", + "\n", + " a = Value(2.0, label=\"a\")\n", + " b = Value(-3.0, label=\"b\")\n", + " c = Value(10.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " d = e+c; d.label=\"d\"\n", + " d.data += h\n", + "\n", + " f = Value(-2.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + "\n", + " L2 = L.data #+ h\n", + "\n", + " print((L2-L1)/h)\n", + " \n", + "lol()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "L.grad = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 0.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using this we can visulize the operations\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L = d*f\n", + "\n", + "We want to know\n", + "\n", + "dL/dd = ? => f\n", + "\n", + "(f(x+h) - f(x)) / h\n", + "\n", + "proof:\n", + "(f(x+h) - f(x)) / h\n", + "\n", + "((d+h)*f - d*f) / h\n", + "\n", + "(d*f + h*f - d*f) / h\n", + "\n", + "h*f / h\n", + "\n", + "= f\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "f.grad = 4.0 # value of d\n", + "d.grad = -2.0 # value of f" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad 0.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-3.9999999999995595\n" + ] + } + ], + "source": [ + "def lol():\n", + " \n", + " h = 0.001\n", + " \n", + " a = Value(2.0, label='a')\n", + " b = Value(-3.0, label='b')\n", + " c = Value(10.0, label='c')\n", + " e = a*b; e.label = 'e'\n", + " d = e + c; d.label = 'd'\n", + " f = Value(-2.0, label='f')\n", + " L = d * f; L.label = 'L'\n", + " \n", + " L1 = L.data\n", + " \n", + " a = Value(2.0, label='a')\n", + " b = Value(-3.0+h, label='b')\n", + " c = Value(10.0, label='c')\n", + " \n", + " e = a*b; e.label = 'e'\n", + " d = e + c; d.label = 'd'\n", + " f = Value(-2.0, label='f')\n", + " L = d * f; L.label = 'L'\n", + " \n", + " L2 = L.data\n", + " \n", + " print((L2 - L1)/h)\n", + " \n", + "lol()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we would want to find dL/dc\n", + "\n", + "We know d = c + e which when differenciated gives dd/dc = 1.0\n", + "\n", + "also, dd/de = 1.0 too\n", + "\n", + "now, dL/dc can calculated with chain rule\n", + "\n", + "The chain rule may also be expressed in Leibniz's notation. If a variable z depends on the variable y, which itself depends on the variable x (that is, y and z are dependent variables), then z depends on x as well, via the intermediate variable y. In this case, the chain rule is expressed as:\n", + "\n", + "\n", + "dz/dx = (dz/dy) * (dy/dx) # relative rate of change \n", + "\n", + "Now, we want:\n", + "dL/dc =\n", + "\n", + "We know:\n", + "dL/dd\n", + "dd/dc\n", + "\n", + "So, \n", + "dL/dc = (dL/dd) * (dd/dc)\n", + "\n", + "The + node just directly routes the rate of change." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Therefore:\n", + "c.grad = -2.0\n", + "e.grad = -2.0\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we want:\n", + "\n", + "dL/da = (dL/de) * (de/da) \n", + "\n", + "and\n", + "\n", + "dL/db = dL/de) * (de/db)\n", + "\n", + "e = a*b\n", + "\n", + "de/da = b\n", + "\n", + "de/db = a" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970777680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596970779552*\n\n*\n\n\n\n140596970777680->140596970779552*\n\n\n\n\n\n140596970777680+\n\n+\n\n\n\n140596970777680+->140596970777680\n\n\n\n\n\n140596970774176\n\na\n\ndata 2.0000\n\ngrad 6.0000\n\n\n\n140596970779840*\n\n*\n\n\n\n140596970774176->140596970779840*\n\n\n\n\n\n140596970779840\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140596970779840->140596970777680+\n\n\n\n\n\n140596970779840*->140596970779840\n\n\n\n\n\n140596970782576\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140596970782576->140596970777680+\n\n\n\n\n\n140596970776432\n\nb\n\ndata -3.0000\n\ngrad -4.0000\n\n\n\n140596970776432->140596970779840*\n\n\n\n\n\n140596970778496\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596970778496->140596970779552*\n\n\n\n\n\n140596970779552\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596970779552*->140596970779552\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.grad = (-2.0 * -3.0)\n", + "b.grad = (-2.0 * 2.0)\n", + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is exactly what backpropagation is:\n", + "Backproagation is recursive application of chain rule throught the computation graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### My own little exercise to see if i really understood everything correctly" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=145.0)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(4.0, label=\"a\")\n", + "b = Value(-8.0, label=\"b\")\n", + "c = Value(3.0, label=\"c\")\n", + "e = a*b; e.label=\"e\"\n", + "d = e+c; d.label=\"d\"\n", + "f = Value(-5.0, label=\"f\")\n", + "L = d*f; L.label=\"L\"\n", + "L" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970779264\n\nd\n\ndata -29.0000\n\ngrad 0.0000\n\n\n\n140596970780128*\n\n*\n\n\n\n140596970779264->140596970780128*\n\n\n\n\n\n140596970779264+\n\n+\n\n\n\n140596970779264+->140596970779264\n\n\n\n\n\n140596970777296\n\na\n\ndata 4.0000\n\ngrad 0.0000\n\n\n\n140596970775808*\n\n*\n\n\n\n140596970777296->140596970775808*\n\n\n\n\n\n140596970775808\n\ne\n\ndata -32.0000\n\ngrad 0.0000\n\n\n\n140596970775808->140596970779264+\n\n\n\n\n\n140596970775808*->140596970775808\n\n\n\n\n\n140596970775856\n\nc\n\ndata 3.0000\n\ngrad 0.0000\n\n\n\n140596970775856->140596970779264+\n\n\n\n\n\n140596970774944\n\nf\n\ndata -5.0000\n\ngrad 0.0000\n\n\n\n140596970774944->140596970780128*\n\n\n\n\n\n140596970780080\n\nb\n\ndata -8.0000\n\ngrad 0.0000\n\n\n\n140596970780080->140596970775808*\n\n\n\n\n\n140596970780128\n\nL\n\ndata 145.0000\n\ngrad 0.0000\n\n\n\n140596970780128*->140596970780128\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "L.grad = 1.0\n", + "\n", + "f.grad = -29.0\n", + "d.grad = -5.0\n", + "\n", + "e.grad = -5 * 1\n", + "c.grad = -5 * 1\n", + "\n", + "b.grad = -5 * 4.0\n", + "a.grad = -5 * -8.0\n", + "\n", + "# All correct and verified\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.000000000020464\n" + ] + } + ], + "source": [ + "# verfiying the gradient\n", + "def lol():\n", + "\n", + " h = 0.001\n", + "\n", + " a = Value(4.0, label=\"a\")\n", + " b = Value(-8.0, label=\"b\")\n", + " c = Value(3.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-5.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + "\n", + " L1 = L.data\n", + "\n", + " a = Value(4.0 + h, label=\"a\")\n", + " b = Value(-8.0, label=\"b\")\n", + " c = Value(3.0, label=\"c\")\n", + " e = a*b; e.label=\"e\"\n", + " \n", + " d = e+c; d.label=\"d\"\n", + " f = Value(-5.0, label=\"f\")\n", + " L = d*f; L.label=\"L\"\n", + " L2 = L.data\n", + "\n", + " print((L2 - L1)/h)\n", + " \n", + "lol()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single Optimisation Step\n", + "\n", + "We will nudge our inputs to make L go up" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "a = Value(2.0, label=\"a\")\n", + "b = Value(-3.0, label=\"b\")\n", + "c = Value(10.0, label=\"c\")\n", + "e = a*b; e.label=\"e\"\n", + "d = e+c; d.label=\"d\"\n", + "f = Value(-2.0, label=\"f\")\n", + "L = d*f; L.label=\"L\"\n", + "\n", + "L.grad = 1.0\n", + "f.grad = 4.0 # value of d\n", + "d.grad = -2.0 # value of f\n", + "\n", + "c.grad = -2.0\n", + "e.grad = -2.0\n", + "\n", + "a.grad = (-2.0 * -3.0)\n", + "b.grad = (-2.0 * 2.0)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970522656\n\nb\n\ndata -3.0000\n\ngrad -4.0000\n\n\n\n140596970523424*\n\n*\n\n\n\n140596970522656->140596970523424*\n\n\n\n\n\n140596970523808\n\na\n\ndata 2.0000\n\ngrad 6.0000\n\n\n\n140596970523808->140596970523424*\n\n\n\n\n\n140596971724448\n\nf\n\ndata -2.0000\n\ngrad 4.0000\n\n\n\n140596971721472*\n\n*\n\n\n\n140596971724448->140596971721472*\n\n\n\n\n\n140596971721472\n\nL\n\ndata -8.0000\n\ngrad 1.0000\n\n\n\n140596971721472*->140596971721472\n\n\n\n\n\n140596971729680\n\nd\n\ndata 4.0000\n\ngrad -2.0000\n\n\n\n140596971729680->140596971721472*\n\n\n\n\n\n140596971729680+\n\n+\n\n\n\n140596971729680+->140596971729680\n\n\n\n\n\n140596970523424\n\ne\n\ndata -6.0000\n\ngrad -2.0000\n\n\n\n140596970523424->140596971729680+\n\n\n\n\n\n140596970523424*->140596970523424\n\n\n\n\n\n140596970522560\n\nc\n\ndata 10.0000\n\ngrad -2.0000\n\n\n\n140596970522560->140596971729680+\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-7.286496\n" + ] + } + ], + "source": [ + "step_size = 0.01\n", + "\n", + "# We are chaning the leaf nodes\n", + "a.data += step_size*a.grad\n", + "b.data += step_size*b.grad\n", + "c.data += step_size*c.grad\n", + "f.data += step_size*f.grad\n", + "\n", + "# Let's do the forward pass again\n", + "e = a * b\n", + "d = e + c\n", + "L = d * f\n", + "\n", + "# We expect a greater value of L here\n", + "print(L.data)\n", + "\n", + "# This can be used to optimise as we can control the value of L" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970523664\n\n \n\ndata -6.2624\n\ngrad 0.0000\n\n\n\n140596971724016+\n\n+\n\n\n\n140596970523664->140596971724016+\n\n\n\n\n\n140596970523664*\n\n*\n\n\n\n140596970523664*->140596970523664\n\n\n\n\n\n140596970522656\n\nb\n\ndata -3.0400\n\ngrad -4.0000\n\n\n\n140596970522656->140596970523664*\n\n\n\n\n\n140596971722336\n\n \n\ndata -7.2865\n\ngrad 0.0000\n\n\n\n140596971722336*\n\n*\n\n\n\n140596971722336*->140596971722336\n\n\n\n\n\n140596971724448\n\nf\n\ndata -1.9600\n\ngrad 4.0000\n\n\n\n140596971724448->140596971722336*\n\n\n\n\n\n140596970523808\n\na\n\ndata 2.0600\n\ngrad 6.0000\n\n\n\n140596970523808->140596970523664*\n\n\n\n\n\n140596971724016\n\n \n\ndata 3.7176\n\ngrad 0.0000\n\n\n\n140596971724016->140596971722336*\n\n\n\n\n\n140596971724016+->140596971724016\n\n\n\n\n\n140596970522560\n\nc\n\ndata 9.9800\n\ngrad -2.0000\n\n\n\n140596970522560->140596971724016+\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(L)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual Backproagation through a Neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "tanh fucntion, activatiom" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.arange(-5, 5, 0.2)\n", + "y = np.tanh(np.arange(-5, 5, 0.2))\n", + "plt.plot(x, y)\n", + "plt.grid()\n", + "\n", + "# It squashes the input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "o = tanh(n)\n", + "\n", + "dervate of tanh = 1 - tanh²\n", + "\n", + "do/dn = 1 - o²\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " return Value(self.data + other.data, (self, other), \"+\")\n", + "\n", + " def __mul__(self, other):\n", + " return Value(self.data * other.data, (self, other), \"*\")\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# see neuron_model.jpeg\n", + "\n", + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936027168\n\nb\n\ndata 6.8814\n\ngrad 0.0000\n\n\n\n140596936027696+\n\n+\n\n\n\n140596936027168->140596936027696+\n\n\n\n\n\n140596936027696\n\nn\n\ndata 0.8814\n\ngrad 0.0000\n\n\n\n140596936028560tanh\n\ntanh\n\n\n\n140596936027696->140596936028560tanh\n\n\n\n\n\n140596936027696+->140596936027696\n\n\n\n\n\n140596936021648\n\nx1\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596936021264*\n\n*\n\n\n\n140596936021648->140596936021264*\n\n\n\n\n\n140596936025296\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596936024816+\n\n+\n\n\n\n140596936025296->140596936024816+\n\n\n\n\n\n140596936025296*\n\n*\n\n\n\n140596936025296*->140596936025296\n\n\n\n\n\n140596936024816\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596936024816->140596936027696+\n\n\n\n\n\n140596936024816+->140596936024816\n\n\n\n\n\n140596936021264\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596936021264->140596936024816+\n\n\n\n\n\n140596936021264*->140596936021264\n\n\n\n\n\n140596936031536\n\nw1\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596936031536->140596936021264*\n\n\n\n\n\n140596936028560\n\noutput\n\ndata 0.7071\n\ngrad 0.0000\n\n\n\n140596936028560tanh->140596936028560\n\n\n\n\n\n140596936029664\n\nx2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596936029664->140596936025296*\n\n\n\n\n\n140596936025584\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936025584->140596936025296*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we would care more about gradients wrt weights w1 and w2.\n", + "\n", + "\n", + "Now we calculate gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936027168\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596936027696+\n\n+\n\n\n\n140596936027168->140596936027696+\n\n\n\n\n\n140596936027696\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596936028560tanh\n\ntanh\n\n\n\n140596936027696->140596936028560tanh\n\n\n\n\n\n140596936027696+->140596936027696\n\n\n\n\n\n140596936021648\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596936021264*\n\n*\n\n\n\n140596936021648->140596936021264*\n\n\n\n\n\n140596936025296\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936024816+\n\n+\n\n\n\n140596936025296->140596936024816+\n\n\n\n\n\n140596936025296*\n\n*\n\n\n\n140596936025296*->140596936025296\n\n\n\n\n\n140596936024816\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936024816->140596936027696+\n\n\n\n\n\n140596936024816+->140596936024816\n\n\n\n\n\n140596936021264\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936021264->140596936024816+\n\n\n\n\n\n140596936021264*->140596936021264\n\n\n\n\n\n140596936031536\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596936031536->140596936021264*\n\n\n\n\n\n140596936028560\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596936028560tanh->140596936028560\n\n\n\n\n\n140596936029664\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936029664->140596936025296*\n\n\n\n\n\n140596936025584\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936025584->140596936025296*\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o.grad = 1.0\n", + "n.grad = 1 - o.data**2\n", + "\n", + "b.grad = 0.5\n", + "x1w1x2w2.grad = 0.5\n", + "\n", + "x1w1.grad = 0.5\n", + "x2w2.grad = 0.5\n", + "\n", + "w1.grad = x1w1.grad * x1.data\n", + "x1.grad = x1w1.grad * w1.data\n", + "\n", + "w2.grad = x2w2.grad * x2.data\n", + "x2.grad = x2w2.grad * w2.data\n", + "\n", + "draw_dot(o)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Backpropagation function" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad = 1.0 * out.grad\n", + " other.grad = 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad = other.data * out.grad\n", + " other.grad = self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad = (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970775568\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934156128*\n\n*\n\n\n\n140596970775568->140596934156128*\n\n\n\n\n\n140596970780752\n\nx1\n\ndata 2.0000\n\ngrad 0.0000\n\n\n\n140596934160736*\n\n*\n\n\n\n140596970780752->140596934160736*\n\n\n\n\n\n140596970777248\n\nb\n\ndata 6.8814\n\ngrad 0.0000\n\n\n\n140596934162752+\n\n+\n\n\n\n140596970777248->140596934162752+\n\n\n\n\n\n140596970782384\n\nw1\n\ndata -3.0000\n\ngrad 0.0000\n\n\n\n140596970782384->140596934160736*\n\n\n\n\n\n140596934162752\n\nn\n\ndata 0.8814\n\ngrad 0.0000\n\n\n\n140596934158672tanh\n\ntanh\n\n\n\n140596934162752->140596934158672tanh\n\n\n\n\n\n140596934162752+->140596934162752\n\n\n\n\n\n140596934158672\n\noutput\n\ndata 0.7071\n\ngrad 0.0000\n\n\n\n140596934158672tanh->140596934158672\n\n\n\n\n\n140596934156128\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596934156224+\n\n+\n\n\n\n140596934156128->140596934156224+\n\n\n\n\n\n140596934156128*->140596934156128\n\n\n\n\n\n140596970775376\n\nx2\n\ndata 0.0000\n\ngrad 0.0000\n\n\n\n140596970775376->140596934156128*\n\n\n\n\n\n140596934160736\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596934160736->140596934156224+\n\n\n\n\n\n140596934160736*->140596934160736\n\n\n\n\n\n140596934156224\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.0000\n\n\n\n140596934156224->140596934162752+\n\n\n\n\n\n140596934156224+->140596934156224\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We don't have to do it manually now. We just have to call backward in the right order" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# but we will have initialise o.grad first\n", + "o.grad = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "o._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "n._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "b._backward() # nothing will happen. None" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "x1w1x2w2._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "x1w1._backward()\n", + "x2w2._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970775568\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934156128*\n\n*\n\n\n\n140596970775568->140596934156128*\n\n\n\n\n\n140596970780752\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596934160736*\n\n*\n\n\n\n140596970780752->140596934160736*\n\n\n\n\n\n140596970777248\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596934162752+\n\n+\n\n\n\n140596970777248->140596934162752+\n\n\n\n\n\n140596970782384\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596970782384->140596934160736*\n\n\n\n\n\n140596934162752\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596934158672tanh\n\ntanh\n\n\n\n140596934162752->140596934158672tanh\n\n\n\n\n\n140596934162752+->140596934162752\n\n\n\n\n\n140596934158672\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596934158672tanh->140596934158672\n\n\n\n\n\n140596934156128\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934156224+\n\n+\n\n\n\n140596934156128->140596934156224+\n\n\n\n\n\n140596934156128*->140596934156128\n\n\n\n\n\n140596970775376\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596970775376->140596934156128*\n\n\n\n\n\n140596934160736\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934160736->140596934156224+\n\n\n\n\n\n140596934160736*->140596934160736\n\n\n\n\n\n140596934156224\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934156224->140596934162752+\n\n\n\n\n\n140596934156224+->140596934156224\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementing the backward function for a whole expression graph\n", + "Now we want to eliminate calling backward pass manually\n", + "\n", + "We want to calculate backward for a node only when backward for all the forward nodes have been calculated.\n", + "\n", + "We calculate backward when backward for all dependencies has been calculated.\n", + "\n", + "This ordering of graphs can be achieved by something called **Toplogical Sort**" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "topo = []\n", + "visited = set()\n", + "\n", + "def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "o.grad = 1.0\n", + "\n", + "build_topo(o)\n", + "\n", + "for node in reversed(topo):\n", + " node._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140598171427376\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934161888*\n\n*\n\n\n\n140598171427376->140596934161888*\n\n\n\n\n\n140596934158432\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934162080+\n\n+\n\n\n\n140596934158432->140596934162080+\n\n\n\n\n\n140596934158432*\n\n*\n\n\n\n140596934158432*->140596934158432\n\n\n\n\n\n140596934149216\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596934155696+\n\n+\n\n\n\n140596934149216->140596934155696+\n\n\n\n\n\n140596934162080\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596934162080->140596934155696+\n\n\n\n\n\n140596934162080+->140596934162080\n\n\n\n\n\n140596970774272\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596970774272->140596934158432*\n\n\n\n\n\n140596934163424\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596934163424tanh\n\ntanh\n\n\n\n140596934163424tanh->140596934163424\n\n\n\n\n\n140596970779984\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596970779984->140596934158432*\n\n\n\n\n\n140596934155696\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596934155696->140596934163424tanh\n\n\n\n\n\n140596934155696+->140596934155696\n\n\n\n\n\n140596934149072\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596934149072->140596934161888*\n\n\n\n\n\n140596934161888\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596934161888->140596934162080+\n\n\n\n\n\n140596934161888*->140596934161888\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here we have it correctly\n", + "draw_dot(o)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's add this to value class\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad = 1.0 * out.grad\n", + " other.grad = 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad = other.data * out.grad\n", + " other.grad = self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad = (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's see if everything works\n", + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "o = n.tanh(); o.label = \"output\"" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936162848\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140596936160880+\n\n+\n\n\n\n140596936162848->140596936160880+\n\n\n\n\n\n140596936157232\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936158288+\n\n+\n\n\n\n140596936157232->140596936158288+\n\n\n\n\n\n140596936157232*\n\n*\n\n\n\n140596936157232*->140596936157232\n\n\n\n\n\n140596936158288\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936158288->140596936160880+\n\n\n\n\n\n140596936158288+->140596936158288\n\n\n\n\n\n140596936160880\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140596936156032tanh\n\ntanh\n\n\n\n140596936160880->140596936156032tanh\n\n\n\n\n\n140596936160880+->140596936160880\n\n\n\n\n\n140596936162944\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140596936162944->140596936157232*\n\n\n\n\n\n140596936162464\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140596936162464->140596936158288+\n\n\n\n\n\n140596936162464*\n\n*\n\n\n\n140596936162464*->140596936162464\n\n\n\n\n\n140596936162992\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140596936162992->140596936162464*\n\n\n\n\n\n140596936159968\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140596936159968->140596936162464*\n\n\n\n\n\n140596936156992\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140596936156992->140596936157232*\n\n\n\n\n\n140596936156032\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140596936156032tanh->140596936156032\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o.backward()\n", + "\n", + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now that's backprop for 1 Neuron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is still a bug, we haven't surfaced yet" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596936162704\n\na\n\ndata 3.0000\n\ngrad 1.0000\n\n\n\n140596936159344+\n\n+\n\n\n\n140596936162704->140596936159344+\n\n\n\n\n\n140596936159344\n\nb\n\ndata 6.0000\n\ngrad 1.0000\n\n\n\n140596936159344+->140596936159344\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(3.0, label=\"a\")\n", + "b = a + a; b.label=\"b\"\n", + "\n", + "b.backward()\n", + "\n", + "draw_dot(b)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596934151184\n\nf\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934151184*\n\n*\n\n\n\n140596934151184*->140596934151184\n\n\n\n\n\n140596936160784\n\na\n\ndata -2.0000\n\ngrad -6.0000\n\n\n\n140596934151424+\n\n+\n\n\n\n140596936160784->140596934151424+\n\n\n\n\n\n140596934159776*\n\n*\n\n\n\n140596936160784->140596934159776*\n\n\n\n\n\n140596936162416\n\nb\n\ndata 3.0000\n\ngrad -6.0000\n\n\n\n140596936162416->140596934151424+\n\n\n\n\n\n140596936162416->140596934159776*\n\n\n\n\n\n140596934151424\n\ne\n\ndata 1.0000\n\ngrad -6.0000\n\n\n\n140596934151424->140596934151184*\n\n\n\n\n\n140596934151424+->140596934151424\n\n\n\n\n\n140596934159776\n\nd\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934159776->140596934151184*\n\n\n\n\n\n140596934159776*->140596934159776\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Another example for this bug\n", + "a = Value(-2.0, label='a')\n", + "b = Value(3.0, label='b')\n", + "d = a * b ; d.label = 'd'\n", + "e = a + b ; e.label = 'e'\n", + "f = d * e ; f.label = 'f'\n", + "\n", + "f.backward()\n", + "\n", + "draw_dot(f)\n", + "\n", + "# There will be an issue when we are using same variable twice\n", + "# before we were using one variable once\n", + "\n", + "# If you see the multivariate chain rule you will see we need to accumulate the gradients\n", + "# That's what we will do now\n", + "# Any contrubution that flows backward will simply add" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's add this to value class\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596970778256\n\na\n\ndata -2.0000\n\ngrad -3.0000\n\n\n\n140596934149792+\n\n+\n\n\n\n140596970778256->140596934149792+\n\n\n\n\n\n140596934148880*\n\n*\n\n\n\n140596970778256->140596934148880*\n\n\n\n\n\n140596934149792\n\ne\n\ndata 1.0000\n\ngrad -6.0000\n\n\n\n140596934162368*\n\n*\n\n\n\n140596934149792->140596934162368*\n\n\n\n\n\n140596934149792+->140596934149792\n\n\n\n\n\n140596934148880\n\nd\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934148880->140596934162368*\n\n\n\n\n\n140596934148880*->140596934148880\n\n\n\n\n\n140596970780560\n\nb\n\ndata 3.0000\n\ngrad -8.0000\n\n\n\n140596970780560->140596934149792+\n\n\n\n\n\n140596970780560->140596934148880*\n\n\n\n\n\n140596934162368\n\nf\n\ndata -6.0000\n\ngrad 1.0000\n\n\n\n140596934162368*->140596934162368\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Another example for this bug\n", + "a = Value(-2.0, label='a')\n", + "b = Value(3.0, label='b')\n", + "d = a * b ; d.label = 'd'\n", + "e = a + b ; e.label = 'e'\n", + "f = d * e ; f.label = 'f'\n", + "\n", + "f.backward()\n", + "\n", + "draw_dot(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140596934162848\n\na\n\ndata 3.0000\n\ngrad 2.0000\n\n\n\n140596934163808+\n\n+\n\n\n\n140596934162848->140596934163808+\n\n\n\n\n\n140596934163808\n\nb\n\ndata 6.0000\n\ngrad 1.0000\n\n\n\n140596934163808+->140596934163808\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(3.0, label=\"a\")\n", + "b = a + a; b.label=\"b\"\n", + "\n", + "b.backward()\n", + "\n", + "draw_dot(b)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now everything is correct" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/micrograd/2_activation_optmization_grad_descent.ipynb b/my_code/micrograd/2_activation_optmization_grad_descent.ipynb new file mode 100644 index 00000000..22dc4327 --- /dev/null +++ b/my_code/micrograd/2_activation_optmization_grad_descent.ipynb @@ -0,0 +1,1610 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "random.seed(1993) # For consistency" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def f(x):\n", + " return 3*x**2 - 4*x + 5\n", + "\n", + "f(3.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xs = np.arange(-5, 5, 0.25)\n", + "ys = f(xs)\n", + "plt.plot(xs, ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install graphviz\n", + "from graphviz import Digraph\n", + "\n", + "def trace(root):\n", + " # builds a set of all nodes and edges in a graph\n", + " nodes, edges = set(), set()\n", + " def build(v):\n", + " if v not in nodes:\n", + " nodes.add(v)\n", + " for child in v._prev:\n", + " edges.add((child, v))\n", + " build(child)\n", + " build(root)\n", + " return nodes, edges\n", + "\n", + "def draw_dot(root):\n", + " dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n", + " \n", + " nodes, edges = trace(root)\n", + " for n in nodes:\n", + " uid = str(id(n))\n", + " # for any value in the graph, create a rectangular ('record') node for it\n", + " dot.node(name = uid, label = \"{ %s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad), shape='record')\n", + " if n._op:\n", + " # if this value is a result of some operation, create an op node for it\n", + " dot.node(name = uid + n._op, label = n._op)\n", + " # and connect this node to it\n", + " dot.edge(uid + n._op, uid)\n", + "\n", + " for n1, n2 in edges:\n", + " # connect n1 to the op node of n2\n", + " dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n", + "\n", + " return dot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def trace(root):\n", + " # builds a set of all nodes and edges in a graph\n", + " nodes, edges = set(), set()\n", + " def build(v):\n", + " if v not in nodes:\n", + " nodes.add(v)\n", + " for child in v._prev:\n", + " edges.add((child, v))\n", + " build(child)\n", + " build(root)\n", + " return nodes, edges" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's add this to value class\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " def __add__(self, other):\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def __mul__(self, other):\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + "\n", + " self.grad = 1.0 \n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Currently we cannot do integer addition directly, so, we will make change now\n", + "# We will do this for multiply\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " \n", + " def __add__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other) # now, we can add integer directly\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + " def __radd__(self, other):\n", + " return self + other\n", + "\n", + " \n", + " def __mul__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + "\n", + " def __pow__(self, other):\n", + " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", + " out = Value(self.data**other, (self,), f'**{other}')\n", + "\n", + " def _backward():\n", + " self.grad += (other*self.data**(other - 1)) * out.grad\n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + "\n", + " def __rmul__(self, other): #other * self\n", + " return self * other\n", + "\n", + " \n", + " def __truediv__(self, other):\n", + " return self * (other**-1)\n", + "\n", + " \n", + " def __sub__(self, other):\n", + " return self + (-other) \n", + " \n", + " \n", + " def exp(self):\n", + " x = self.data\n", + " out = Value(math.exp(x), (self, ), \"exp\")\n", + "\n", + " def _backward():\n", + " self.grad += out.data * out.grad # exp(x) * out.grad\n", + " \n", + " out._backward = _backward\n", + " \n", + " return out\n", + "\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value(data=3.0)\n", + "Value(data=3.0)\n", + "Value(data=4.0)\n", + "Value(data=1.0)\n", + "Value(data=0.5)\n", + "Value(data=16.0)\n", + "Value(data=54.598150033144236)\n" + ] + } + ], + "source": [ + "a = Value(2.0)\n", + "b = Value(4.0)\n", + "\n", + "print(a+1)\n", + "print(1+a)\n", + "\n", + "print(a*2)\n", + "print(a/2)\n", + "print(a/b)\n", + "\n", + "print(b**2)\n", + "\n", + "print(b.exp())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Division**\n", + "\n", + "a / b\n", + "\n", + "a * (1/b)\n", + "\n", + "a * (b**-1)\n", + "\n", + "Let's make:\n", + "x**k\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=0.5)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = Value(2.0)\n", + "b = Value(4.0)\n", + "\n", + "a/b\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Breaking down tanh" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's see if everything works\n", + "# inputs\n", + "x1 = Value(2.0, label=\"x1\")\n", + "x2 = Value(0.0, label=\"x2\")\n", + "\n", + "# Weights\n", + "w1 = Value(-3.0, label=\"w1\")\n", + "w2 = Value(1.0, label=\"w2\")\n", + "\n", + "# Bias of a neuron\n", + "b = Value(6.88137358701954432, label=\"b\")\n", + "\n", + "# from figure we can see\n", + "# x1*w1 + x2*w2 + b\n", + "\n", + "x1w1 = x1*w1; x1w1.label =\"x1*w1\"\n", + "x2w2 = x2*w2; x2w2.label =\"x2*w2\"\n", + "\n", + "x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = \"x1w1 + x2w2\"\n", + "\n", + "n = x1w1x2w2 + b; n.label = \"n\" # Without activation function now\n", + "\n", + "#o = n.tanh(); o.label = \"output\"\n", + "# ---------- tanh break-up ----------\n", + "e = (2*n).exp()\n", + "o = (e - 1) / (e + 1); o.label = \"output\"\n", + "\n", + "# ---------- tanh break-up ----------\n", + "\n", + "\n", + "o.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n140679288691712\n\noutput\n\ndata 0.7071\n\ngrad 1.0000\n\n\n\n140679288691712*\n\n*\n\n\n\n140679288691712*->140679288691712\n\n\n\n\n\n140679288689696\n\n \n\ndata 1.7627\n\ngrad 0.2500\n\n\n\n140679288689552exp\n\nexp\n\n\n\n140679288689696->140679288689552exp\n\n\n\n\n\n140679288689696*\n\n*\n\n\n\n140679288689696*->140679288689696\n\n\n\n\n\n140679288695840\n\nx1w1 + x2w2\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140679288695984+\n\n+\n\n\n\n140679288695840->140679288695984+\n\n\n\n\n\n140679288695840+\n\n+\n\n\n\n140679288695840+->140679288695840\n\n\n\n\n\n140679288687680\n\nw2\n\ndata 1.0000\n\ngrad 0.0000\n\n\n\n140679288698144*\n\n*\n\n\n\n140679288687680->140679288698144*\n\n\n\n\n\n140679288689840\n\n \n\ndata 4.8284\n\ngrad 0.1464\n\n\n\n140679288689840->140679288691712*\n\n\n\n\n\n140679288689840+\n\n+\n\n\n\n140679288689840+->140679288689840\n\n\n\n\n\n140679288692912\n\n \n\ndata 2.0000\n\ngrad 0.2203\n\n\n\n140679288692912->140679288689696*\n\n\n\n\n\n140679288695984\n\nn\n\ndata 0.8814\n\ngrad 0.5000\n\n\n\n140679288695984->140679288689696*\n\n\n\n\n\n140679288695984+->140679288695984\n\n\n\n\n\n140679288687824\n\nw1\n\ndata -3.0000\n\ngrad 1.0000\n\n\n\n140679288700160*\n\n*\n\n\n\n140679288687824->140679288700160*\n\n\n\n\n\n140679288688880\n\n \n\ndata 6.8284\n\ngrad -0.1036\n\n\n\n140679288691568**-1\n\n**-1\n\n\n\n140679288688880->140679288691568**-1\n\n\n\n\n\n140679288688880+\n\n+\n\n\n\n140679288688880+->140679288688880\n\n\n\n\n\n140679288700160\n\nx1*w1\n\ndata -6.0000\n\ngrad 0.5000\n\n\n\n140679288700160->140679288695840+\n\n\n\n\n\n140679288700160*->140679288700160\n\n\n\n\n\n140679288690944\n\nx2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140679288690944->140679288698144*\n\n\n\n\n\n140679288698144\n\nx2*w2\n\ndata 0.0000\n\ngrad 0.5000\n\n\n\n140679288698144->140679288695840+\n\n\n\n\n\n140679288698144*->140679288698144\n\n\n\n\n\n140679288686384\n\nx1\n\ndata 2.0000\n\ngrad -1.5000\n\n\n\n140679288686384->140679288700160*\n\n\n\n\n\n140679288686432\n\nb\n\ndata 6.8814\n\ngrad 0.5000\n\n\n\n140679288686432->140679288695984+\n\n\n\n\n\n140679288691568\n\n \n\ndata 0.1464\n\ngrad 4.8284\n\n\n\n140679288691568->140679288691712*\n\n\n\n\n\n140679288691568**-1->140679288691568\n\n\n\n\n\n140679288689552\n\n \n\ndata 5.8284\n\ngrad 0.0429\n\n\n\n140679288689552->140679288689840+\n\n\n\n\n\n140679288689552->140679288688880+\n\n\n\n\n\n140679288689552exp->140679288689552\n\n\n\n\n\n140679288689600\n\n \n\ndata -1.0000\n\ngrad 0.1464\n\n\n\n140679288689600->140679288689840+\n\n\n\n\n\n140679288689120\n\n \n\ndata 1.0000\n\ngrad -0.1036\n\n\n\n140679288689120->140679288688880+\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_dot(o)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The outputs of this resembles exactly the output if we directly use tanh fucntion. So, it doesn't matter how we implement operations. \n", + "\n", + "All that matters is we can correctly calulate the forward and backward pass." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now the same thing is PyTorch" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7071066904050358\n", + "---\n", + "x2 0.5000001283844369\n", + "w2 0.0\n", + "x1 -1.5000003851533106\n", + "w1 1.0000002567688737\n" + ] + } + ], + "source": [ + "x1 = torch.Tensor([2.0]).double() ; x1.requires_grad = True\n", + "x2 = torch.Tensor([0.0]).double() ; x2.requires_grad = True\n", + "w1 = torch.Tensor([-3.0]).double() ; w1.requires_grad = True\n", + "w2 = torch.Tensor([1.0]).double() ; w2.requires_grad = True\n", + "b = torch.Tensor([6.8813735870195432]).double() ; b.requires_grad = True\n", + "n = x1*w1 + x2*w2 + b\n", + "o = torch.tanh(n)\n", + "\n", + "print(o.data.item())\n", + "o.backward()\n", + "\n", + "print('---')\n", + "print('x2', x2.grad.item())\n", + "print('w2', w2.grad.item())\n", + "print('x1', x1.grad.item())\n", + "print('w1', w1.grad.item())\n", + "\n", + "# The result is exactly the same" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building Neural Net(s) now" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class Neuron:\n", + "\n", + " def __init__(self, nin):\n", + " self.w = [Value(random.uniform(-1,1)) for _ in range(nin)]\n", + " self.b = Value(random.uniform(-1,1))\n", + "\n", + " def __call__(self, x):\n", + " # w*x +b # w*x is a dot product\n", + " act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b )\n", + " out = act.tanh()\n", + " return out\n", + "\n", + " def parameters(self):\n", + " return self.w + [self.b]\n", + "\n", + "\n", + "class Layer:\n", + "\n", + " def __init__(self, nin, nout):\n", + " self.neurons = [Neuron(nin) for _ in range(nout)]\n", + "\n", + " def __call__(self, x):\n", + " outs = [n(x) for n in self.neurons]\n", + " return outs[0] if len(outs) == 1 else outs\n", + "\n", + " def parameters(self): \n", + " return [p for n in self.neurons for p in n.parameters()]\n", + "\n", + " # for neuron in self.neurons:\n", + " # ps = neuron.parameters()\n", + " # params.extend(ps)\n", + " # return params\n", + "\n", + "\n", + "class MLP:\n", + " \n", + " def __init__(self, nin, nouts):\n", + " sz = [nin] + nouts\n", + " self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))]\n", + "\n", + " def __call__(self, x):\n", + " for layer in self.layers:\n", + " x = layer(x)\n", + " return x\n", + "\n", + " def parameters(self):\n", + " return [p for layer in self.layers for p in layer.parameters()]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=0.007657120625740258)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = [2.0, 3.0, -1.0]\n", + "n = MLP(3, [4, 4, 1])\n", + "n(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "41" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(n.parameters())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#draw_dot(n(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Examples for training\n", + "xs = [\n", + " [2.0, 3.0, -1.0],\n", + " [3.0, -1.0, 0.5],\n", + " [0.5, 1.0, 1.0],\n", + " [1.0, 1.0, -1.0],\n", + "]\n", + "ys = [1.0, -1.0, -1.0, 1.0] # desired targets" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Value(data=0.007657120625740258),\n", + " Value(data=0.9629698232963938),\n", + " Value(data=0.6722963345252638),\n", + " Value(data=-0.24414394993705751)]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ypred =[n(x) for x in xs]\n", + "ypred" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=9.182464116048688)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We wil calculate the loss to measure the correcteness of the prediction: how well the neural net is performing\n", + "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + "loss" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# We want the loss to be low\n", + "loss.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.17473429586587344" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n.layers[0].neurons[0].w[0].grad # If the gradient is negative, that means by increasing the weight of this neuron, the loss will go down\n", + "\n", + "# We have this information for all of our neurons and their parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w1 grad 0.17473429586587344\n", + "w1 data -0.03862055212227311\n" + ] + } + ], + "source": [ + "print('w1 grad', n.layers[0].neurons[0].w[0].grad)\n", + "print('w1 data', n.layers[0].neurons[0].w[0].data)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# we will itirate through all the parameters\n", + "\n", + "for p in n.parameters():\n", + " p.data += -0.01 * p.grad # if gradient is negative, we increase the weight, if it is positive, we decrease the weight" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w1 grad 0.17473429586587344\n", + "w1 data -0.040367895080931845\n" + ] + } + ], + "source": [ + "# after the change\n", + "print('w1 grad', n.layers[0].neurons[0].w[0].grad)\n", + "print('w1 data', n.layers[0].neurons[0].w[0].data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=8.607967674232228)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now we recalculate the loss # we expecct it to be lower\n", + "ypred =[n(x) for x in xs]\n", + "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + "loss\n", + "\n", + "# It is lower\n", + "\n", + "# You won't get the exact same results as I did, but you should get a loss that is lower than the initial loss (it was randomly initialized) : \n", + "# but now you will do because i seeded the random number generator" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Now we will do backward pass\n", + "loss.backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "for p in n.parameters():\n", + " p.data += -0.1 * p.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=6.247012816219952)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ypred =[n(x) for x in xs]\n", + "loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + "loss\n", + "\n", + "# Now the loss should decrease more" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is gradient descent, we are doing forward pass, backward pass and then update. As a result neural net is improving its predictions<>" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Value(data=0.8009931947462453),\n", + " Value(data=-0.6030405624037342),\n", + " Value(data=0.7932559750217888),\n", + " Value(data=0.7905048504993913)]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's now do this a few more time with an increased learning rate\n", + "\n", + "for e in range(5):\n", + " ypred =[n(x) for x in xs]\n", + " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + " loss.backward()\n", + " for p in n.parameters():\n", + " p.data += -0.1 * p.grad\n", + "\n", + "ypred\n", + "\n", + "# You can see the output is getting closer to the desired output" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Value(data=3.4568357132496788)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the loss here is:\n", + "loss\n", + "\n", + "# see it is very low now\n", + "# loss = Value(data=3.1391947868174866e-07)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's look at the parameters\n", + "# There are a set of trained neural net parameters that will give us the desired output\n", + "\n", + "#n.parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's do it in a proper way from begining like we do in regular trainings" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Data\n", + "xs = [\n", + " [2.0, 3.0, -1.0],\n", + " [3.0, -1.0, 0.5],\n", + " [0.5, 1.0, 1.0],\n", + " [1.0, 1.0, -1.0],\n", + "]\n", + "ys = [1.0, -1.0, -1.0, 1.0] # desired targets" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current step: 0 Loss: 8.590244752121581\n", + "Current step: 2 Loss: 3.251279565120799\n", + "Current step: 4 Loss: 0.4314166532029969\n", + "Current step: 6 Loss: 0.008903970131844198\n", + "Current step: 8 Loss: 0.00017030867419075724\n", + "Current step: 10 Loss: 6.909809892444565e-06\n", + "Current step: 12 Loss: 3.79564352360394e-07\n", + "Current step: 14 Loss: 2.3918872973270876e-08\n", + "Current step: 16 Loss: 1.6092511096719524e-09\n", + "Current step: 18 Loss: 1.3445988501866946e-06\n", + "[Value(data=0.9999971000178582), Value(data=-0.9999999999981305), Value(data=-0.999999999998144), Value(data=0.9999981856078193)]\n" + ] + } + ], + "source": [ + "x = [2.0, 3.0, -1.0]\n", + "n = MLP(3, [4, 4, 1])\n", + "\n", + "learning_rate = 0.05\n", + "\n", + "for e in range(20):\n", + " # forward pass\n", + " ypred =[n(x) for x in xs]\n", + " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + " \n", + " # backward pass\n", + " loss.backward()\n", + "\n", + " # update\n", + " for p in n.parameters():\n", + " p.data += -learning_rate * p.grad\n", + " \n", + " # print every\n", + " if e % 2 == 0:\n", + " print(\"Current step: \", e, \"Loss: \", loss.data)\n", + "\n", + "# After last step of training let's predict again\n", + "ypred =[n(x) for x in xs]\n", + "print(ypred)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current step: 0 Loss: 3.0671681764088587\n", + "Current step: 2 Loss: 0.7522797144376534\n", + "Current step: 4 Loss: 0.21049320414541034\n", + "Current step: 6 Loss: 0.12931817259939735\n", + "Current step: 8 Loss: 0.09173860850980789\n", + "Current step: 10 Loss: 0.07046366092766423\n", + "Current step: 12 Loss: 0.05690191664948921\n", + "Current step: 14 Loss: 0.04755552992867856\n", + "Current step: 16 Loss: 0.04074910543584072\n", + "Current step: 18 Loss: 0.03558485523377747\n", + "Current step: 20 Loss: 0.03154041675170391\n", + "Current step: 22 Loss: 0.028292096705550128\n", + "Current step: 24 Loss: 0.025629064823938723\n", + "Current step: 26 Loss: 0.023408339828180122\n", + "Current step: 28 Loss: 0.021529657545075617\n", + "Current step: 30 Loss: 0.019920702795380423\n", + "Current step: 32 Loss: 0.018528051621906523\n", + "Current step: 34 Loss: 0.01731141003069202\n", + "Current step: 36 Loss: 0.016239832977886108\n", + "Current step: 38 Loss: 0.015289174466104255\n", + "Current step: 40 Loss: 0.014440326294789117\n", + "Current step: 42 Loss: 0.013677975530100375\n", + "Current step: 44 Loss: 0.012989711231352619\n", + "Current step: 46 Loss: 0.012365371303898295\n", + "Current step: 48 Loss: 0.011796557581364723\n", + "Current step: 50 Loss: 0.01127627078763775\n", + "Current step: 52 Loss: 0.01079863225421862\n", + "Current step: 54 Loss: 0.01035866931227851\n", + "Current step: 56 Loss: 0.0099521480265217\n", + "Current step: 58 Loss: 0.009575441547888152\n", + "Current step: 60 Loss: 0.009225425560284873\n", + "Current step: 62 Loss: 0.008899394546933826\n", + "Current step: 64 Loss: 0.008594994206319886\n", + "Current step: 66 Loss: 0.008310166505547124\n", + "Current step: 68 Loss: 0.008043104703973538\n", + "Current step: 70 Loss: 0.0077922163033184435\n", + "Current step: 72 Loss: 0.007556092344763569\n", + "Current step: 74 Loss: 0.00733348182266993\n", + "Current step: 76 Loss: 0.00712327024928288\n", + "Current step: 78 Loss: 0.006924461607232828\n", + "Current step: 80 Loss: 0.0067361630826139815\n", + "Current step: 82 Loss: 0.006557572092482956\n", + "Current step: 84 Loss: 0.00638796521522437\n", + "Current step: 86 Loss: 0.006226688706644633\n", + "Current step: 88 Loss: 0.006073150343552104\n", + "Current step: 90 Loss: 0.005926812383468344\n", + "Current step: 92 Loss: 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step: 848 Loss: 0.0005505972082743946\n", + "Current step: 850 Loss: 0.000549250158013283\n", + "Current step: 852 Loss: 0.0005479095966635781\n", + "Current step: 854 Loss: 0.0005465754777164927\n", + "Current step: 856 Loss: 0.0005452477551052005\n", + "Current step: 858 Loss: 0.0005439263831995968\n", + "Current step: 860 Loss: 0.0005426113168011282\n", + "Current step: 862 Loss: 0.0005413025111377554\n", + "Current step: 864 Loss: 0.0005399999218589141\n", + "Current step: 866 Loss: 0.0005387035050305834\n", + "Current step: 868 Loss: 0.0005374132171304164\n", + "Current step: 870 Loss: 0.0005361290150429557\n", + "Current step: 872 Loss: 0.0005348508560548836\n", + "Current step: 874 Loss: 0.0005335786978503574\n", + "Current step: 876 Loss: 0.0005323124985063925\n", + "Current step: 878 Loss: 0.0005310522164883819\n", + "Current step: 880 Loss: 0.0005297978106455287\n", + "Current step: 882 Loss: 0.0005285492402065093\n", + "Current step: 884 Loss: 0.0005273064647750916\n", + "Current step: 886 Loss: 0.0005260694443258183\n", + "Current step: 888 Loss: 0.0005248381391998004\n", + "Current step: 890 Loss: 0.0005236125101005433\n", + "Current step: 892 Loss: 0.0005223925180898052\n", + "Current step: 894 Loss: 0.0005211781245835422\n", + "Current step: 896 Loss: 0.000519969291347889\n", + "Current step: 898 Loss: 0.0005187659804952347\n", + "Current step: 900 Loss: 0.0005175681544802769\n", + "Current step: 902 Loss: 0.0005163757760961985\n", + "Current step: 904 Loss: 0.0005151888084708758\n", + "Current step: 906 Loss: 0.0005140072150631076\n", + "Current step: 908 Loss: 0.0005128309596589425\n", + "Current step: 910 Loss: 0.0005116600063680186\n", + "Current step: 912 Loss: 0.0005104943196199775\n", + "Current step: 914 Loss: 0.0005093338641608848\n", + "Current step: 916 Loss: 0.000508178605049754\n", + "Current step: 918 Loss: 0.0005070285076551034\n", + "Current step: 920 Loss: 0.0005058835376514743\n", + "Current step: 922 Loss: 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Loss: 0.00048401060142870723\n", + "Current step: 962 Loss: 0.00048296588553460487\n", + "Current step: 964 Loss: 0.0004819256129371093\n", + "Current step: 966 Loss: 0.0004808897555041026\n", + "Current step: 968 Loss: 0.0004798582853397218\n", + "Current step: 970 Loss: 0.0004788311747818617\n", + "Current step: 972 Loss: 0.0004778083963997543\n", + "Current step: 974 Loss: 0.00047678992299153924\n", + "Current step: 976 Loss: 0.000475775727581909\n", + "Current step: 978 Loss: 0.0004747657834197483\n", + "Current step: 980 Loss: 0.0004737600639758131\n", + "Current step: 982 Loss: 0.00047275854294045745\n", + "Current step: 984 Loss: 0.0004717611942213274\n", + "Current step: 986 Loss: 0.0004707679919411882\n", + "Current step: 988 Loss: 0.0004697789104356645\n", + "Current step: 990 Loss: 0.0004687939242510981\n", + "Current step: 992 Loss: 0.0004678130081423438\n", + "Current step: 994 Loss: 0.00046683613707072206\n", + "Current step: 996 Loss: 0.00046586328620183877\n", + "Current step: 998 Loss: 0.00046489443090355924\n", + "[Value(data=0.9920687382052715), Value(data=-0.9920858581792807), Value(data=-0.9881691234740339), Value(data=0.9859137885520525)]\n" + ] + } + ], + "source": [ + "# There is a very common bug in the code above. We are not setting the gradients back to zero. lol\n", + "# The only reason previous one was working because the data is very simple.\n", + "# Also you can see the number of epochs has increased by a lot. Gradients are being set back to zero.\n", + "# Previously they were being added.\n", + "# Let's fix that!\n", + "\n", + "x = [2.0, 3.0, -1.0]\n", + "n = MLP(3, [4, 4, 1])\n", + "\n", + "learning_rate = 0.05\n", + "\n", + "# We are doing gradient descent here\n", + "for e in range(1000):\n", + " # forward pass\n", + " ypred =[n(x) for x in xs]\n", + " loss = sum([(yout - ygt)**2 for ygt, yout in zip(ys, ypred)])\n", + " \n", + " # zero grad\n", + " for p in n.parameters():\n", + " p.grad = 0.0 # we need to set the gradients back to zero, otherwise they will accumulate\n", + " \n", + " # backward pass\n", + " loss.backward()\n", + "\n", + " # update\n", + " for p in n.parameters():\n", + " p.data += -learning_rate * p.grad\n", + " \n", + " # print every\n", + " if e % 2 == 0:\n", + " print(\"Current step: \", e, \"Loss: \", loss.data)\n", + "\n", + "# After last step of training let's predict again\n", + "ypred =[n(x) for x in xs]\n", + "print(ypred)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/my_code/micrograd/micrograd_demo.ipynb b/my_code/micrograd/micrograd_demo.ipynb new file mode 100644 index 00000000..217da7a5 --- /dev/null +++ b/my_code/micrograd/micrograd_demo.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#%pip install micrograd --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from micrograd.engine import Value\n", + "from micrograd.nn import Neuron, Layer, MLP" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1337)\n", + "random.seed(1337)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make up a dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_moons, make_blobs\n", + "X, y = make_moons(n_samples=100, noise=0.1)\n", + "\n", + "y = y*2 - 1 # make y be -1 or 1\n", + "# visualize in 2D\n", + "plt.figure(figsize=(5,5))\n", + "plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize a model " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MLP of [Layer of [ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2)], Layer of [ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16)], Layer of [LinearNeuron(16)]]\n", + "number of parameters 337\n" + ] + } + ], + "source": [ + "model = MLP(2, [16, 16, 1]) # 2-layer neural network\n", + "print(model)\n", + "print(\"number of parameters\", len(model.parameters()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Loss function " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value(data=0.9219220440708354, grad=0) 0.5\n" + ] + } + ], + "source": [ + "def loss(batch_size=None):\n", + " \n", + " # inline DataLoader :)\n", + " if batch_size is None:\n", + " Xb, yb = X, y\n", + " else:\n", + " ri = np.random.permutation(X.shape[0])[:batch_size]\n", + " Xb, yb = X[ri], y[ri]\n", + " inputs = [list(map(Value, xrow)) for xrow in Xb]\n", + " \n", + " # forward the model to get scores\n", + " scores = list(map(model, inputs))\n", + " \n", + " # svm \"max-margin\" loss\n", + " losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(yb, scores)]\n", + " data_loss = sum(losses) * (1.0 / len(losses))\n", + " \n", + " # L2 regularization\n", + " alpha = 1e-4\n", + " reg_loss = alpha * sum((p*p for p in model.parameters()))\n", + " total_loss = data_loss + reg_loss\n", + " \n", + " # also get accuracy\n", + " accuracy = [(yi > 0) == (scorei.data > 0) for yi, scorei in zip(yb, scores)]\n", + " return total_loss, sum(accuracy) / len(accuracy)\n", + "\n", + "total_loss, acc = loss()\n", + "print(total_loss, acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 0 loss 0.19804839220213744, accuracy 94.0%\n", + "step 5 loss 0.24672248029881824, accuracy 90.0%\n", + "step 10 loss 0.11462603871211018, accuracy 97.0%\n", + "step 15 loss 0.16748079173634225, accuracy 93.0%\n", + "step 20 loss 0.061716686945202125, accuracy 99.0%\n", + "step 25 loss 0.06513144508540747, accuracy 98.0%\n", + "step 30 loss 0.04222444942627202, accuracy 99.0%\n", + "step 35 loss 0.04889118476767827, accuracy 99.0%\n", + "step 40 loss 0.024106844141993013, accuracy 99.0%\n", + "step 45 loss 0.02942947332012279, accuracy 100.0%\n", + "step 50 loss 0.024905708104035043, accuracy 99.0%\n", + "step 55 loss 0.019293939608525292, accuracy 100.0%\n", + "step 60 loss 0.01778576221766042, accuracy 100.0%\n", + "step 65 loss 0.015836073894976793, accuracy 100.0%\n", + "step 70 loss 0.014353628187808278, accuracy 100.0%\n", + "step 75 loss 0.014283006307858672, accuracy 100.0%\n", + "step 80 loss 0.013501075237247298, accuracy 100.0%\n", + "step 85 loss 0.013070661438117028, accuracy 100.0%\n", + "step 90 loss 0.012660005324128556, accuracy 100.0%\n", + "step 95 loss 0.01286610384023592, accuracy 100.0%\n" + ] + } + ], + "source": [ + "for k in range(100):\n", + " \n", + " # forward\n", + " total_loss, acc = loss()\n", + " \n", + " # backward\n", + " model.zero_grad()\n", + " total_loss.backward()\n", + " \n", + " # update (sgd)\n", + " learning_rate = 1.0 - 0.9*k/100\n", + " for p in model.parameters():\n", + " p.data -= learning_rate * p.grad\n", + " \n", + " if k % 5 == 0:\n", + " print(f\"step {k} loss {total_loss.data}, accuracy {acc*100}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualize decision boundary\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.7222875713140986, 2.027712428685901)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h = 0.25\n", + "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", + " np.arange(y_min, y_max, h))\n", + "Xmesh = np.c_[xx.ravel(), yy.ravel()]\n", + "inputs = [list(map(Value, xrow)) for xrow in Xmesh]\n", + "scores = list(map(model, inputs))\n", + "Z = np.array([s.data > 0 for s in scores])\n", + "Z = Z.reshape(xx.shape)\n", + "\n", + "fig = plt.figure()\n", + "plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n", + "plt.xlim(xx.min(), xx.max())\n", + "plt.ylim(yy.min(), yy.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + 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f"Value(data={self.data}, grad={self.grad})" \ No newline at end of file diff --git a/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py b/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py new file mode 100644 index 00000000..7ec8e60b --- /dev/null +++ b/my_code/micrograd/micrograd_ir/micrograd_ir/nn.py @@ -0,0 +1,58 @@ +from micrograd_ir.engine import Value +import random + +class Module: + + def zero_grad(self): + for p in self.parameters(): + p.grad = 0 + + def parameters(self): + return [] + + +class Neuron(Module): + + def __init__(self, nin): + self.w = [Value(random.uniform(-1,1)) for _ in range(nin)] + self.b = Value(random.uniform(-1,1)) + + def __call__(self, x): + # w * x + b + act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b) + out = act.tanh() + return out + + def parameters(self): + return self.w + [self.b] + + +class Layer(Module): + + def __init__(self, nin, nout): + self.neurons = [Neuron(nin) for _ in range(nout)] + + def __call__(self, x): + outs = [n(x) for n in self.neurons] + return outs[0] if len(outs) == 1 else outs + + def parameters(self): + return [p for neuron in self.neurons for p in neuron.parameters()] + + +class MLP(Module): + + def __init__(self, nin, nouts): + sz = [nin] + nouts + self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))] + + def __call__(self, x): + for layer in self.layers: + x = layer(x) + return x + + def parameters(self): + return [p for layer in self.layers for p in layer.parameters()] + + def __repr__(self): + return f"MLP of [{', '.join(str(layer) for layer in self.layers)}]" \ No newline at end of file diff --git a/my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc b/my_code/micrograd/micrograd_ir/test/__pycache__/test_engine.cpython-310-pytest-7.1.3.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d951a3e08997fcea2e7dd9003cd1e11fc55b0f9a GIT binary patch literal 4168 zcmb_f&5t8T6|d^H+jjeVJYQi03=s)qq@7vM>=&>T7LjlWVh>@-!9p@^SC7Xtw%e)h 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x + 2 + x + q = z.relu() + z * x + h = (z * z).relu() + y = h + q + q * x + y.backward() + xpt, ypt = x, y + + # forward pass went well + assert ymg.data == ypt.data.item() + # backward pass went well + assert xmg.grad == xpt.grad.item() + +def test_more_ops(): + + a = Value(-4.0) + b = Value(2.0) + c = a + b + d = a * b + b**3 + c += c + 1 + c += 1 + c + (-a) + d += d * 2 + (b + a).relu() + d += 3 * d + (b - a).relu() + e = c - d + f = e**2 + g = f / 2.0 + g += 10.0 / f + g.backward() + amg, bmg, gmg = a, b, g + + a = torch.Tensor([-4.0]).double() + b = torch.Tensor([2.0]).double() + a.requires_grad = True + b.requires_grad = True + c = a + b + d = a * b + b**3 + c = c + c + 1 + c = c + 1 + c + (-a) + d = d + d * 2 + (b + a).relu() + d = d + 3 * d + (b - a).relu() + e = c - d + f = e**2 + g = f / 2.0 + g = g + 10.0 / f + g.backward() + apt, bpt, gpt = a, b, g + + tol = 1e-6 + # forward pass went well + assert abs(gmg.data - gpt.data.item()) < tol + # backward pass went well + assert 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"colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JnGHatCI51JP" + }, + "source": [ + "# micrograd exercises\n", + "\n", + "1. watch the [micrograd video](https://www.youtube.com/watch?v=VMj-3S1tku0) on YouTube\n", + "2. come back and complete these exercises to level up :)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OFt6NKOz6iBZ" + }, + "source": [ + "## section 1: derivatives" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "3Jx9fCXl5xHd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.336362190988558\n" + ] + } + ], + "source": [ + "# here is a mathematical expression that takes 3 inputs and produces one output\n", + "from math import sin, cos\n", + "\n", + "def f(a, b, c):\n", + " return -a**3 + sin(3*b) - 1.0/c + b**2.5 - a**0.5\n", + "\n", + "print(f(2, 3, 4))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "qXaH59eL9zxf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected -12.353553390593273, yours returns -12.353553390593273\n", + "OK for dim 1: expected 10.25699027111255, yours returns 10.25699027111255\n", + "OK for dim 2: expected 0.0625, yours returns 0.0625\n" + ] + } + ], + "source": [ + "# write the function df that returns the analytical gradient of f\n", + "# i.e. use your skills from calculus to take the derivative, then implement the formula\n", + "# if you do not calculus then feel free to ask wolframalpha, e.g.:\n", + "# https://www.wolframalpha.com/input?i=d%2Fda%28sin%283*a%29%29%29\n", + "\n", + "def gradf(a, b, c):\n", + " df_da = -3*a**2 - 0.5*a**-0.5\n", + " df_db = 3*cos(3*b) + 2.5*b**1.5\n", + " df_dc = c**-2\n", + " return [df_da, df_db, df_dc] # todo, return [df/da, df/db, df/dc]\n", + "\n", + "# expected answer is the list of \n", + "ans = [-12.353553390593273, 10.25699027111255, 0.0625]\n", + "yours = gradf(2, 3, 4)\n", + "for dim in range(3):\n", + " ok = 'OK' if abs(yours[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {yours[dim]}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "_27n-KTA9Qla" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected -12.353553390593273, yours returns -12.35355817641448\n", + "OK for dim 1: expected 10.25699027111255, yours returns 10.256986371359744\n", + "OK for dim 2: expected 0.0625, yours returns 0.06250111539429781\n" + ] + } + ], + "source": [ + "# now estimate the gradient numerically without any calculus, using\n", + "# the approximation we used in the video.\n", + "# you should not call the function df from the last cell\n", + "\n", + "# -----------\n", + "h = 1e-10\n", + "df_da = (f(2+h, 3, 4) - f(2, 3, 4)) / h\n", + "df_db = (f(2, 3+h, 4) - f(2, 3, 4)) / h\n", + "df_dc = (f(2, 3, 4+h) - f(2, 3, 4)) / h\n", + "numerical_grad = [df_da, df_db, df_dc] # TODO\n", + "# -----------\n", + "\n", + "for dim in range(3):\n", + " ok = 'OK' if abs(numerical_grad[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad[dim]}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "BUqsGb5o_h2P" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected -12.353553390593273, yours returns -12.353553735522382\n", + "OK for dim 1: expected 10.25699027111255, yours returns 10.256990812251843\n", + "OK for dim 2: expected 0.0625, yours returns 0.06250111539429781\n" + ] + } + ], + "source": [ + "# there is an alternative formula that provides a much better numerical \n", + "# approximation to the derivative of a function.\n", + "# learn about it here: https://en.wikipedia.org/wiki/Symmetric_derivative\n", + "# implement it. confirm that for the same step size h this version gives a\n", + "# better approximation.\n", + "\n", + "# -----------\n", + "h = 1e-10\n", + "df_da = (f(2+h, 3, 4) - f(2-h, 3, 4)) / (2*h)\n", + "df_db = (f(2, 3+h, 4) - f(2, 3-h, 4)) / (2*h)\n", + "df_dc = (f(2, 3, 4+h) - f(2, 3, 4-h)) / (2*h)\n", + "numerical_grad2 = [df_da, df_db, df_dc] # TODO\n", + "# -----------\n", + "\n", + "for dim in range(3):\n", + " ok = 'OK' if abs(numerical_grad2[dim] - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {numerical_grad2[dim]}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tklF9s_4AtlI" + }, + "source": [ + "## section 2: support for softmax" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "nAPe_RVrCTeO" + }, + "outputs": [], + "source": [ + "# Value class starter code, with many functions taken out\n", + "from math import exp, log\n", + "\n", + "class Value:\n", + " \n", + " def __init__(self, data, _children=(), _op='', label=''):\n", + " self.data = data\n", + " self.grad = 0.0\n", + " self._backward = lambda: None\n", + " self._prev = set(_children)\n", + " self._op = _op\n", + " self.label = label\n", + "\n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + " \n", + " def __add__(self, other): # exactly as in the video\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data + other.data, (self, other), '+')\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + " out._backward = _backward\n", + " \n", + " return out\n", + " \n", + " def __radd__(self, other):\n", + " return other + self.data\n", + "\n", + " def __mul__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " def log(self):\n", + " out = Value(log(self.data), (self, ), \"log\")\n", + " def _backward():\n", + " self.grad += (1/out.data) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + " def __rmul__(self, other): #other * self\n", + " return self * other\n", + "\n", + " def __truediv__(self, other):\n", + " return self * (other**-1)\n", + "\n", + " def exp(self):\n", + " out = Value(exp(self.data), (self,), 'exp')\n", + "\n", + " def _backward():\n", + " self.grad += out.data * out.grad\n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " \n", + " # ------\n", + " # re-implement all the other functions needed for the exercises below\n", + " # your code here\n", + " # TODO\n", + " # ------\n", + "\n", + " def backward(self): # exactly as in video \n", + " topo = []\n", + " visited = set()\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " build_topo(self)\n", + " \n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "class Value:\n", + "\n", + " def __init__(self, data, _children=(), _op=\"\", label=\"\"):\n", + " self.data = data\n", + " self.grad = 0.0 # grad represents the derrivative of output (say L) wrt to value (say a or f)\n", + " self._prev = set(_children)\n", + " self._backward = lambda: None\n", + " self._op = _op\n", + " self.label = label\n", + " \n", + " \n", + " def __repr__(self):\n", + " return f\"Value(data={self.data})\"\n", + "\n", + " \n", + " def __add__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other) # now, we can add integer directly\n", + " out = Value(self.data + other.data, (self, other), \"+\")\n", + " \n", + " def _backward():\n", + " self.grad += 1.0 * out.grad\n", + " other.grad += 1.0 * out.grad\n", + "\n", + " # this means the gradient is just copied as it is as \n", + " # we have seen it happening for addition \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + " def __radd__(self, other):\n", + " return self + other\n", + "\n", + " \n", + " def __mul__(self, other):\n", + " other = other if isinstance(other, Value) else Value(other)\n", + " out = Value(self.data * other.data, (self, other), \"*\")\n", + " \n", + " def _backward():\n", + " self.grad += other.data * out.grad\n", + " other.grad += self.data * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + "\n", + " def __pow__(self, other):\n", + " assert isinstance(other, (int, float)), \"only supporting int/float powers for now\"\n", + " out = Value(self.data**other, (self,), f'**{other}')\n", + "\n", + " def _backward():\n", + " self.grad += (other*self.data**(other - 1)) * out.grad\n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + " \n", + "\n", + " def __rmul__(self, other): #other * self\n", + " return self * other\n", + "\n", + " \n", + " def __truediv__(self, other):\n", + " return self * (other**-1)\n", + "\n", + " \n", + " def __sub__(self, other):\n", + " return self + (-other) \n", + " \n", + " def __neg__(self):\n", + " return -1*self\n", + "\n", + " def exp(self):\n", + " x = self.data\n", + " out = Value(math.exp(x), (self, ), \"exp\")\n", + "\n", + " def _backward():\n", + " self.grad += out.data * out.grad # exp(x) * out.grad\n", + " \n", + " out._backward = _backward\n", + " \n", + " return out\n", + "\n", + "\n", + " def tanh(self):\n", + " x = self.data\n", + " t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n", + " out = Value(t, (self, ), \"tanh\")\n", + "\n", + " def _backward():\n", + " self.grad += (1 - t**2) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out \n", + " \n", + " def log(self):\n", + " out = Value(log(self.data), (self, ), \"log\")\n", + " def _backward():\n", + " self.grad += (1/self.data) * out.grad \n", + " \n", + " out._backward = _backward\n", + "\n", + " return out\n", + "\n", + " \n", + " def backward(self):\n", + " topo = []\n", + " visited = set()\n", + "\n", + " def build_topo(v):\n", + " if v not in visited:\n", + " visited.add(v)\n", + " for child in v._prev:\n", + " build_topo(child)\n", + " topo.append(v)\n", + " \n", + " build_topo(self)\n", + "\n", + " self.grad = 1.0\n", + " for node in reversed(topo):\n", + " node._backward()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "VgWvwVQNAvnI" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.1755153626167147\n", + "[Value(data=0.0), Value(data=3.0), Value(data=-2.0), Value(data=1.0)]\n", + "OK for dim 0: expected 0.041772570515350445, yours returns 0.041772570515350445\n", + "OK for dim 1: expected 0.8390245074625319, yours returns 0.8390245074625319\n", + "OK for dim 2: expected 0.005653302662216329, yours returns 0.005653302662216329\n", + "OK for dim 3: expected -0.8864503806400986, yours returns -0.8864503806400986\n" + ] + } + ], + "source": [ + "# without referencing our code/video __too__ much, make this cell work\n", + "# you'll have to implement (in some cases re-implemented) a number of functions\n", + "# of the Value object, similar to what we've seen in the video.\n", + "# instead of the squared error loss this implements the negative log likelihood\n", + "# loss, which is very often used in classification.\n", + "\n", + "# this is the softmax function\n", + "# https://en.wikipedia.org/wiki/Softmax_function\n", + "def softmax(logits):\n", + " counts = [logit.exp() for logit in logits]\n", + " denominator = sum(counts)\n", + " out = [c / denominator for c in counts]\n", + " return out\n", + "\n", + "# this is the negative log likelihood loss function, pervasive in classification\n", + "logits = [Value(0.0), Value(3.0), Value(-2.0), Value(1.0)]\n", + "probs = softmax(logits)\n", + "loss = -probs[3].log() # dim 3 acts as the label for this input example\n", + "loss.backward()\n", + "print(loss.data)\n", + "\n", + "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", + "\n", + "print(logits)\n", + "\n", + "logits_grad_value = []\n", + "for dim in range(4):\n", + " ok = 'OK' if abs(logits[dim].grad - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits[dim].grad}\")\n", + " logits_grad_value.append(logits[dim].grad)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "q7ca1SVAGG1S" + }, + "outputs": [], + "source": [ + "# verify the gradient using the torch library\n", + "# torch should give you the exact same gradient\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.Tensor([0.0]).double() ; a.requires_grad = True\n", + "b = torch.Tensor([3.0]).double() ; b.requires_grad = True\n", + "c = torch.Tensor([-2.0]).double() ; c.requires_grad = True\n", + "d = torch.Tensor([1.0]).double() ; d.requires_grad = True\n", + "\n", + "logits = torch.Tensor([a, b, c, d]); logits.requires_grad = True" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.0418, 0.8390, 0.0057, 0.1135], grad_fn=)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probs = torch.nn.functional.softmax(logits, dim=-1)\n", + "probs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2.1755)\n" + ] + } + ], + "source": [ + "loss = -1*probs[3].log() # dim 3 acts as the label for this input example\n", + "loss.backward()\n", + "print(loss.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK for dim 0: expected 0.041772570515350445, yours returns 0.041772566735744476\n", + "OK for dim 1: expected 0.8390245074625319, yours returns 0.8390244841575623\n", + "OK for dim 2: expected 0.005653302662216329, yours returns 0.005653302650898695\n", + "OK for dim 3: expected -0.8864503806400986, yours returns -0.8864504098892212\n" + ] + } + ], + "source": [ + "ans = [0.041772570515350445, 0.8390245074625319, 0.005653302662216329, -0.8864503806400986]\n", + "logits_grad_tensor = []\n", + "for dim in range(4):\n", + " ok = 'OK' if abs(logits.grad[dim].item() - ans[dim]) < 1e-5 else 'WRONG!'\n", + " print(f\"{ok} for dim {dim}: expected {ans[dim]}, yours returns {logits.grad[dim].item()}\")\n", + " logits_grad_tensor.append(logits.grad[dim].item())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.7796059684791494e-09\n", + "2.330496962077433e-08\n", + "1.1317634329710557e-11\n", + "2.9249122568408836e-08\n" + ] + } + ], + "source": [ + "# Checking the difference between two\n", + "\n", + "for v, t in zip(logits_grad_value, logits_grad_tensor):\n", + " print(v-t)\n", + "\n", + "# As you can see the difference is very small" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.10.4 ('deeply')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "vscode": { + "interpreter": { + "hash": "8ba283c99aab3f9826c9057a6b77e5ed1375b1012fccd26237ed2bb4d681a306" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}