Backpropog.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "expressed-customer",
   "metadata": {},
   "source": [
    "# Backprogation Algorithm (Adim Adim anlatimi)\n",
    "\n",
    "# Dr.Öğr.Üyesi YILMAZ ATAY \n",
    "ylmzatay@gmail.com\n",
    "\n",
    "# ULANBEK TOKTOGULOV\n",
    "ulanbek.toktogulov@gmail.com\n",
    "\n",
    "# Kaynaklar\n",
    "https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/\n",
    "https://github.com/mattm/simple-neural-network/blob/master/neural-network.py\n",
    "https://www.youtube.com/watch?v=sVjBqjDWoQA&ab_channel=CloudxLabOfficial\n",
    "https://www.youtube.com/watch?v=4Cu9fizAtAo&ab_channel=CloudxLabOfficial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "deadly-spring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 8]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def double_it(x):\n",
    "    return 2*x\n",
    "\n",
    "def apply_transformation(f, arr):\n",
    "    result = []\n",
    "    for a in arr:\n",
    "        v = f(a)\n",
    "        result.append(v)\n",
    "    return result\n",
    "apply_transformation(double_it, [1,2,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "packed-europe",
   "metadata": {},
   "source": [
    "# Differentiation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "christian-binding",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sq(x):\n",
    "    return x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "happy-blowing",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff(fn, x):\n",
    "    delta = 0.000000000001\n",
    "    y = fn(x)\n",
    "    x1 = x+delta\n",
    "    y1 = fn(x1)\n",
    "    return (y1-y)/(x1-x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "three-garbage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sq,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "greek-hayes",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sq,4)\n",
    "# x*x 2x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "recorded-contrast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sq,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "employed-mexican",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sq,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "failing-cherry",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cb(x):\n",
    "    \n",
    "    return x*x*x\n",
    "# 3x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "applied-singapore",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(cb,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "featured-administrator",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.00177619893428"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(cb,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "radical-october",
   "metadata": {},
   "source": [
    "# Partial Derivative (Kısmi Türev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "genuine-democrat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.002220859751105\n"
     ]
    }
   ],
   "source": [
    "#burada basit bir fonksiyon olusur\n",
    "def fn(x1,x2):\n",
    "    return x1*5+x2*x2\n",
    "#bu fonksiyonun x1 ciktisini hesaplayalim\n",
    "\n",
    "x1 = 10\n",
    "x2 = 20\n",
    "delta_x = 0.000000000001\n",
    "\n",
    "delta_y = fn(x1+delta_x,x2)-fn(x1,x2)\n",
    "df_dx1= delta_y/delta_x\n",
    "print(df_dx1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "impaired-price",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.960923459148034\n"
     ]
    }
   ],
   "source": [
    "#simdi ise x2 ciktisini hesaplayalim\n",
    "delta_y = fn(x1,x2+delta_x)-fn(x1,x2)\n",
    "df_dx2 = delta_y/delta_x\n",
    "print(df_dx2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "subject-investigation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Genellestirelim\n",
    "import numpy as np \n",
    "# fn fonksiyonu\n",
    "# idx turev girdisi\n",
    "# *args degisken, degerler degisebilir. fonksiyon ile cagirilir\n",
    "def diffp(fn,idx, *args):\n",
    "    delta = 0.000000000001\n",
    "    y= fn(*args)\n",
    "    args= list(args)\n",
    "    args[idx]+=delta\n",
    "    y1 = fn(*args)\n",
    "    return (y1-y)/delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cheap-camera",
   "metadata": {},
   "outputs": [],
   "source": [
    "def line_y(m,c,x):\n",
    "    return m*x+c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "independent-robertson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.000266701747023"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(line_y, 0 , 1 , 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loving-paris",
   "metadata": {},
   "source": [
    "# Chain rule(Zincir Kurali)\n",
    "\n",
    "Calculus-II : Kısmi Türevde Zincir Kuralı (Chain Rule)\n",
    "#https://www.youtube.com/watch?v=F13ypBH4h6o&ab_channel=BUdersBo%C4%9Fazi%C3%A7iliden%C3%96zelDers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "official-thunder",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sq(x):\n",
    "    return x*x\n",
    "\n",
    "def cb(t):\n",
    "    return t*t*t\n",
    "\n",
    "def sqcb(x):\n",
    "    t = sq(x)\n",
    "    y = cb(t)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "likely-linux",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192.01706891180947"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffp(sqcb,0,2)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "recovered-syntax",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= 2\n",
    "dt_dx = diffp(sq,0, x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "structural-expense",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = sq(2)\n",
    "dy_dt = diffp(cb, 0, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "alike-apartment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192.03413934105515"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_dx*dy_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distributed-hamburg",
   "metadata": {},
   "source": [
    "#  Gradient desent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "modified-amazon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# oncelikle veri olusturalim\n",
    "\n",
    "import numpy as np\n",
    "X = 2*np.random.random((100,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "personalized-miami",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 4 + 3*X + .3*np.random.randn(100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "metallic-magnitude",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.19062959],\n",
       "       [5.60103684],\n",
       "       [9.44449882]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "impressed-blair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.12502784],\n",
       "       [0.53421405],\n",
       "       [1.9968921 ]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "southwest-bunny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.37508351],\n",
       "       [5.60264214],\n",
       "       [9.99067629]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4+3*X[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "complimentary-integer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(11,5))\n",
    "plt.scatter(X,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bored-mounting",
   "metadata": {},
   "outputs": [],
   "source": [
    "def error(m,c,X,y):\n",
    "    ypred= m*X+c\n",
    "    err= ypred-y\n",
    "    return np.sum(err*err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "organic-secretary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error(3,4,np.array([1,2,3]),np.array([1,2,3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "solar-leadership",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grad_fit(X,y):\n",
    "    m = np.random.random()\n",
    "    c = np.random.random()\n",
    "    learning_rate = 0.001\n",
    "    for i in range(100):\n",
    "        print(\"i: \", i, \"Error: \",error(m, c, X, y))\n",
    "        dE_dm = diffp(error, 0, m, c, X, y)\n",
    "        dE_dc = diffp(error, 1, m, c, X, y)\n",
    "        m= m-learning_rate*dE_dm\n",
    "        c= c-learning_rate*dE_dc\n",
    "    return (m,c)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "absent-presentation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i:  0 Error:  2884.4895540590524\n",
      "i:  1 Error:  981.1886955830862\n",
      "i:  2 Error:  341.4382790809532\n",
      "i:  3 Error:  126.10843926707108\n",
      "i:  4 Error:  53.335798143733584\n",
      "i:  5 Error:  28.510470142500495\n",
      "i:  6 Error:  19.831182790772168\n",
      "i:  7 Error:  16.599599162603234\n",
      "i:  8 Error:  15.221491252735483\n",
      "i:  9 Error:  14.486776649845696\n",
      "i:  10 Error:  13.987283168658019\n",
      "i:  11 Error:  13.58453805402028\n",
      "i:  12 Error:  13.230917787633757\n",
      "i:  13 Error:  12.908864888211069\n",
      "i:  14 Error:  12.61179766844338\n",
      "i:  15 Error:  12.336328766155345\n",
      "i:  16 Error:  12.080486622086298\n",
      "i:  17 Error:  11.842636095255067\n",
      "i:  18 Error:  11.621521623207913\n",
      "i:  19 Error:  11.415934549817548\n",
      "i:  20 Error:  11.224729841311106\n",
      "i:  21 Error:  11.046970808172695\n",
      "i:  22 Error:  10.881667665788175\n",
      "i:  23 Error:  10.728026261768957\n",
      "i:  24 Error:  10.585053090182733\n",
      "i:  25 Error:  10.452096280833052\n",
      "i:  26 Error:  10.32855532740023\n",
      "i:  27 Error:  10.21364656232112\n",
      "i:  28 Error:  10.106856656240572\n",
      "i:  29 Error:  10.007458507219846\n",
      "i:  30 Error:  9.915059335039137\n",
      "i:  31 Error:  9.829092708360228\n",
      "i:  32 Error:  9.749190817815823\n",
      "i:  33 Error:  9.674869191848684\n",
      "i:  34 Error:  9.60580313103314\n",
      "i:  35 Error:  9.541576012230687\n",
      "i:  36 Error:  9.481828870055159\n",
      "i:  37 Error:  9.42629025234112\n",
      "i:  38 Error:  9.37462032053449\n",
      "i:  39 Error:  9.326570043900588\n",
      "i:  40 Error:  9.281908085923002\n",
      "i:  41 Error:  9.24041262924443\n",
      "i:  42 Error:  9.201798882640093\n",
      "i:  43 Error:  9.165887660352555\n",
      "i:  44 Error:  9.132501644472585\n",
      "i:  45 Error:  9.101436660474976\n",
      "i:  46 Error:  9.072566405581313\n",
      "i:  47 Error:  9.045692687813288\n",
      "i:  48 Error:  9.020762312781322\n",
      "i:  49 Error:  8.99753336291945\n",
      "i:  50 Error:  8.975939970053542\n",
      "i:  51 Error:  8.955855995491296\n",
      "i:  52 Error:  8.93718500498453\n",
      "i:  53 Error:  8.919811357819459\n",
      "i:  54 Error:  8.903673049457506\n",
      "i:  55 Error:  8.888673920850296\n",
      "i:  56 Error:  8.874737770004094\n",
      "i:  57 Error:  8.86176850558842\n",
      "i:  58 Error:  8.84970712476291\n",
      "i:  59 Error:  8.838476560043494\n",
      "i:  60 Error:  8.828060418890887\n",
      "i:  61 Error:  8.818354445137564\n",
      "i:  62 Error:  8.809324903373863\n",
      "i:  63 Error:  8.800939093108825\n",
      "i:  64 Error:  8.793145441657046\n",
      "i:  65 Error:  8.78589630105217\n",
      "i:  66 Error:  8.779154091692961\n",
      "i:  67 Error:  8.772880273175506\n",
      "i:  68 Error:  8.767047435927072\n",
      "i:  69 Error:  8.761635645049427\n",
      "i:  70 Error:  8.756578190836638\n",
      "i:  71 Error:  8.751873399972556\n",
      "i:  72 Error:  8.747513352101558\n",
      "i:  73 Error:  8.743454760715911\n",
      "i:  74 Error:  8.73968201925218\n",
      "i:  75 Error:  8.736173731749567\n",
      "i:  76 Error:  8.7329168209453\n",
      "i:  77 Error:  8.729880350588672\n",
      "i:  78 Error:  8.727055815705466\n",
      "i:  79 Error:  8.724434854587672\n",
      "i:  80 Error:  8.72199956535401\n",
      "i:  81 Error:  8.719729081114192\n",
      "i:  82 Error:  8.71761791893537\n",
      "i:  83 Error:  8.71565996480737\n",
      "i:  84 Error:  8.713829018524857\n",
      "i:  85 Error:  8.712139204494505\n",
      "i:  86 Error:  8.710562630649962\n",
      "i:  87 Error:  8.709096139817198\n",
      "i:  88 Error:  8.707734682199641\n",
      "i:  89 Error:  8.706461444994058\n",
      "i:  90 Error:  8.705284958102133\n",
      "i:  91 Error:  8.704195238252112\n",
      "i:  92 Error:  8.703178893785866\n",
      "i:  93 Error:  8.702228792239696\n",
      "i:  94 Error:  8.701347435590572\n",
      "i:  95 Error:  8.700523710832208\n",
      "i:  96 Error:  8.699760984042744\n",
      "i:  97 Error:  8.69905201542252\n",
      "i:  98 Error:  8.698392861281274\n",
      "i:  99 Error:  8.697782778456547\n"
     ]
    }
   ],
   "source": [
    "m, c = grad_fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "brief-federation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.036910388198379, 3.9651833486127748)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m, c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "aging-azerbaijan",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x1 = 0\n",
    "x2 = 2\n",
    "\n",
    "y1 = m*x1 + c\n",
    "y2 = m*x2 + c\n",
    "\n",
    "ycap = m*X + c\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot()\n",
    "\n",
    "# plt.scatter(X, ycap, color='r')\n",
    "plt.plot([x1, x2], [y1, y2], color = 'red')\n",
    "plt.subplot()\n",
    "plt.scatter(X, y, color='grey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standard-salvation",
   "metadata": {},
   "source": [
    "# Backpropagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "referenced-clinic",
   "metadata": {},
   "outputs": [],
   "source": [
    "#veri olusturalim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "X = np.random.random((100,1))\n",
    "y = 4+3*X+.3*np.random.randn(100,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "floral-world",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neuron(w11,w12,x):\n",
    "    r = w11*x+w12\n",
    "    return r\n",
    "def forward_nn(w11,w12,w21,w22,x):\n",
    "    h1 = neuron(w11,w12,x)\n",
    "    o1 = neuron(w21,w22,h1)\n",
    "    return o1\n",
    "def sq_error(o1,yy):\n",
    "    e = o1-yy\n",
    "    return e*e\n",
    "def error_nn(w11, w12, w21, w22, x, yy):\n",
    "    o1 = forward_nn(w11, w12, w21, w22, x)\n",
    "    e = o1 - yy\n",
    "    return e*e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "emerging-exposure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [9.06670066]\n",
      "1 [31.38405004]\n",
      "2 [23.83635708]\n",
      "3 [20.85108344]\n",
      "4 [38.51811296]\n",
      "5 [22.22738563]\n",
      "6 [13.58130492]\n",
      "7 [32.3510708]\n",
      "8 [14.46153933]\n",
      "9 [25.58698871]\n",
      "10 [45.41334481]\n",
      "11 [23.1009294]\n",
      "12 [25.96489963]\n",
      "13 [23.74887521]\n",
      "14 [30.9894255]\n",
      "15 [26.64007237]\n",
      "16 [34.93499143]\n",
      "17 [30.02047445]\n",
      "18 [31.3718837]\n",
      "19 [37.64392601]\n",
      "20 [19.40836251]\n",
      "21 [43.15836415]\n",
      "22 [32.11246922]\n",
      "23 [22.70968353]\n",
      "24 [30.66462645]\n",
      "25 [30.47610678]\n",
      "26 [29.70595449]\n",
      "27 [37.1078034]\n",
      "28 [30.35290775]\n",
      "29 [14.78405895]\n",
      "30 [28.5642269]\n",
      "31 [29.0871061]\n",
      "32 [24.14599314]\n",
      "33 [36.90508774]\n",
      "34 [25.56515701]\n",
      "35 [31.05446229]\n",
      "36 [12.10770972]\n",
      "37 [14.46123182]\n",
      "38 [18.30884727]\n",
      "39 [17.72615193]\n",
      "40 [25.65113815]\n",
      "41 [30.14862485]\n",
      "42 [14.65594263]\n",
      "43 [10.90982334]\n",
      "44 [16.1109213]\n",
      "45 [15.99907115]\n",
      "46 [17.45661565]\n",
      "47 [12.53292189]\n",
      "48 [26.74080877]\n",
      "49 [25.95294716]\n",
      "50 [11.89783619]\n",
      "51 [14.83810685]\n",
      "52 [17.95182576]\n",
      "53 [12.91463059]\n",
      "54 [23.54386187]\n",
      "55 [34.02472943]\n",
      "56 [13.01198369]\n",
      "57 [22.06107006]\n",
      "58 [11.21603771]\n",
      "59 [13.08367152]\n",
      "60 [17.91303023]\n",
      "61 [13.18922375]\n",
      "62 [6.5311096]\n",
      "63 [14.42095017]\n",
      "64 [11.26246387]\n",
      "65 [17.62659148]\n",
      "66 [9.02126443]\n",
      "67 [8.18903808]\n",
      "68 [19.45873374]\n",
      "69 [23.52440694]\n",
      "70 [16.13322014]\n",
      "71 [6.84669037]\n",
      "72 [12.48327502]\n",
      "73 [5.69905064]\n",
      "74 [13.67349183]\n",
      "75 [6.22339508]\n",
      "76 [13.0106188]\n",
      "77 [22.34614945]\n",
      "78 [9.77511101]\n",
      "79 [11.5554442]\n",
      "80 [6.03929862]\n",
      "81 [14.07433282]\n",
      "82 [5.39446969]\n",
      "83 [18.56274064]\n",
      "84 [9.4195962]\n",
      "85 [13.27526305]\n",
      "86 [5.63530872]\n",
      "87 [13.50722044]\n",
      "88 [5.49027096]\n",
      "89 [11.83817038]\n",
      "90 [14.75973483]\n",
      "91 [15.96313538]\n",
      "92 [17.45565916]\n",
      "93 [6.55666327]\n",
      "94 [4.3548878]\n",
      "95 [22.64586335]\n",
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    {
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      "2013 [6.70536201e-05]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2156 [0.26511057]\n",
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    {
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    {
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      "9999 [0.08327965]\n"
     ]
    }
   ],
   "source": [
    "w11 = 0.5\n",
    "w12 = 0.4\n",
    "w21 = 0.3\n",
    "w22 = 0.1\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "for epoch in range(100):\n",
    "    for i in range(len(X)):\n",
    "        x = X[i]\n",
    "        yy = y[i]\n",
    "        print(epoch*len(X)+i, error_nn(w11, w12, w21, w22, x, yy))\n",
    "        dE_dw11 = diffp(error_nn, 0, w11, w12, w21, w22, x, yy)\n",
    "        dE_dw12 = diffp(error_nn, 1, w11, w12, w21, w22, x, yy)\n",
    "        dE_dw21 = diffp(error_nn, 2, w11, w12, w21, w22, x, yy)\n",
    "        dE_dw22 = diffp(error_nn, 3, w11, w12, w21, w22, x, yy)\n",
    "        \n",
    "        w11 = w11 - learning_rate * dE_dw11\n",
    "        w12 = w12 - learning_rate * dE_dw12\n",
    "        \n",
    "        w21 = w21 - learning_rate * dE_dw21\n",
    "        w22 = w22 - learning_rate * dE_dw22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "pursuant-hygiene",
   "metadata": {},
   "outputs": [],
   "source": [
    "ycap = forward_nn(w11, w12, w21, w22, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "latest-passport",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:6: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot()\n",
    "plt.plot(X, ycap, color='r')\n",
    "plt.subplot()\n",
    "plt.scatter(X, y, color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ranging-lemon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "9569 [0.10289805]\n",
      "9570 [0.08714297]\n",
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      "9572 [0.48867258]\n",
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      "9574 [0.11066104]\n",
      "9575 [0.02030079]\n",
      "9576 [0.12818552]\n",
      "9577 [0.12670834]\n",
      "9578 [0.05883531]\n",
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      "9583 [0.00221057]\n",
      "9584 [0.00058957]\n",
      "9585 [0.06827647]\n",
      "9586 [0.03960656]\n",
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      "9588 [0.0355202]\n",
      "9589 [0.43646211]\n",
      "9590 [0.00416278]\n",
      "9591 [0.10824083]\n",
      "9592 [0.38590122]\n",
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      "9594 [0.02227953]\n",
      "9595 [0.74829317]\n",
      "9596 [0.01074341]\n",
      "9597 [0.08338866]\n",
      "9598 [0.26901177]\n",
      "9599 [0.05115445]\n",
      "9600 [0.78711376]\n",
      "9601 [0.10484031]\n",
      "9602 [0.01602202]\n",
      "9603 [0.02477169]\n",
      "9604 [0.0300342]\n",
      "9605 [0.01491863]\n",
      "9606 [0.0084696]\n",
      "9607 [0.09172878]\n",
      "9608 [0.04552237]\n",
      "9609 [0.15875123]\n",
      "9610 [0.24986072]\n",
      "9611 [0.4302432]\n",
      "9612 [0.0410596]\n",
      "9613 [0.04697225]\n",
      "9614 [0.09442922]\n",
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      "9749 [0.04952847]\n",
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      "9967 [0.01375274]\n",
      "9968 [0.01634998]\n",
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      "9970 [0.0871402]\n",
      "9971 [0.02099385]\n",
      "9972 [0.48865054]\n",
      "9973 [0.08319815]\n",
      "9974 [0.11060277]\n",
      "9975 [0.02032096]\n",
      "9976 [0.12812663]\n",
      "9977 [0.12664374]\n",
      "9978 [0.05884115]\n",
      "9979 [0.21154664]\n",
      "9980 [0.00086605]\n",
      "9981 [0.04435766]\n",
      "9982 [0.0794409]\n",
      "9983 [0.00220354]\n",
      "9984 [0.00059162]\n",
      "9985 [0.06830808]\n",
      "9986 [0.0396302]\n",
      "9987 [0.04662712]\n",
      "9988 [0.03548898]\n",
      "9989 [0.43636172]\n",
      "9990 [0.00416819]\n",
      "9991 [0.10820186]\n",
      "9992 [0.38584073]\n",
      "9993 [0.01908204]\n",
      "9994 [0.02228122]\n",
      "9995 [0.74828695]\n",
      "9996 [0.01074241]\n",
      "9997 [0.08337947]\n",
      "9998 [0.2690169]\n",
      "9999 [0.05116079]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "w11 = 0.5\n",
    "w12 = 0.4\n",
    "w21 = 0.3\n",
    "w22 = 0.1\n",
    "\n",
    "_w11 = None\n",
    "_w12 = None\n",
    "_w21 = None\n",
    "_w22 = None\n",
    "\n",
    "_dE_dw11 = None\n",
    "_dE_dw12 = None\n",
    "_dE_dw21 = None\n",
    "_dE_dw22 = None\n",
    "\n",
    "learning_rate = 0.01\n",
    "\n",
    "def sn(g):\n",
    "    if g < 0:\n",
    "        return -1\n",
    "    return 1\n",
    "\n",
    "def gd(w, _w, grad, _grad):\n",
    "    if _grad and sn(grad)*sn(_grad) < 0:\n",
    "        return (w + _w)/2\n",
    "    return w - learning_rate * grad\n",
    "\n",
    "for epoch in range(100):\n",
    "    for i in range(len(X)):\n",
    "        x = X[i]\n",
    "        yy = y[i]\n",
    "        print(epoch*len(X)+i, error_nn(w11, w12, w21, w22, x, yy))\n",
    "        dE_dw11 = diffp(error_nn, 0, w11, w12, w21, w22, x, yy)\n",
    "        dE_dw12 = diffp(error_nn, 1, w11, w12, w21, w22, x, yy)\n",
    "        dE_dw21 = diffp(error_nn, 2, w11, w12, w21, w22, x, yy)\n",
    "        dE_dw22 = diffp(error_nn, 3, w11, w12, w21, w22, x, yy)\n",
    "        \n",
    "        w11_ = gd(w11, _w11, dE_dw11, _dE_dw11)\n",
    "        _w11 = w11\n",
    "        w11 = w11_\n",
    "        w12_ = gd(w12, _w12, dE_dw12, _dE_dw12)\n",
    "        _w12 = w12\n",
    "        w12 = w12_\n",
    "        w21_ = gd(w21, _w21, dE_dw21, _dE_dw21)\n",
    "        _w21 = w21\n",
    "        w21 = w21_\n",
    "        w22_ = gd(w22, _w22, dE_dw22, _dE_dw22)\n",
    "        _w22 = w22\n",
    "        w22 = w22_\n",
    "        \n",
    "        _dE_dw11 = dE_dw11\n",
    "        _dE_dw12 = dE_dw12\n",
    "        _dE_dw21 = dE_dw21\n",
    "        _dE_dw22 = dE_dw22\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "integrated-integer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize\n",
    "w11 = 0.5\n",
    "w12 = 0.5\n",
    "w21 = 0.5\n",
    "w22 = 0.5\n",
    "\n",
    "def neuron(w11, w12, x):\n",
    "    #TODO: activation function\n",
    "    return w11*x + w12\n",
    "\n",
    "def sq_error(o1, yy):\n",
    "    e = o1 - yy\n",
    "    return e*e\n",
    "\n",
    "# Learning Rate\n",
    "eta = 0.01\n",
    "\n",
    "for epoch in range(50):\n",
    "    for i in range(len(X)):\n",
    "        x = X[i]\n",
    "        yy = y[i]\n",
    "        \n",
    "        #Forward Pass\n",
    "        h1 = neuron(w11, w12, x)\n",
    "        o1 = neuron(w21, w22, h1)\n",
    "\n",
    "        dE_do1 = diffp(sq_error, 0, o1, yy)\n",
    "        do1_dw21 = diffp(neuron, 0, w21, w22, h1)\n",
    "        dE_w21 = dE_do1 * do1_dw21\n",
    "        \n",
    "        do1_dw22 = diffp(neuron, 1, w21, w22, h1)\n",
    "        dE_w22 = dE_do1 * do1_dw22\n",
    "        \n",
    "        do1_dh = diffp(neuron, 2, w21, w22, h1)\n",
    "        dE_dh = dE_do1 * do1_dh\n",
    "        dh_dw11 = diffp(neuron, 0, w11, w12, x)\n",
    "        dE_dw11 = dE_dh * dh_dw11\n",
    "\n",
    "        dh_dw12 = diffp(neuron, 1, w11, w12, x)\n",
    "        dE_dw12 = dE_do1 * do1_dh * dh_dw12\n",
    "        \n",
    "        w11 = w11 - eta * dE_dw11\n",
    "        w12 = w12 - eta * dE_dw12\n",
    "\n",
    "        w21 = w21 - eta * dE_w21\n",
    "        w22 = w22 - eta * dE_w22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "assigned-array",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:11: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h1 = neuron(w11, w12, X)\n",
    "ycap1 = neuron(w21, w22, h1)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "# plt.subplot()\n",
    "# plt.scatter(X, ycap, color='r')\n",
    "plt.subplot()\n",
    "plt.plot(X, ycap1, color='g')\n",
    "plt.subplot()\n",
    "plt.scatter(X, y, color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "quiet-petersburg",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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