Copy_of_CMPE188_M5_AssignmentP2v0.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of CMPE188_M5_AssignmentP2v0.ipynb",
      "provenance": [],
      "toc_visible": true,
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Initialization"
      ],
      "metadata": {
        "id": "8-eGYd7_klwp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing required libraries\n",
        "import keras\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from sklearn.metrics import classification_report\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "L0DbJi8K5v7R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a training set"
      ],
      "metadata": {
        "id": "HVK5ew1mksLd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.array([[0,0],[0,1],[1,0],[1,1]], \"float32\")\n",
        "y_train = np.array([[0],[1],[1],[0]], \"float32\")"
      ],
      "metadata": {
        "id": "ARllJny6jIaj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JI-qsEcYjoqx",
        "outputId": "01a646a7-586e-4309-ef6c-0f13ebaca44e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0.],\n",
              "       [0., 1.],\n",
              "       [1., 0.],\n",
              "       [1., 1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRtzciUIjrV-",
        "outputId": "8f1c2eb2-1f7c-4606-bf55-ac3ea0292480"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a simple neural network (same as our prediction example that we built from scratch -- here we use Python functions)"
      ],
      "metadata": {
        "id": "ST001vtwkvVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(3, input_dim = 2, activation = 'tanh'))\n",
        "model.add(Dense(1, activation = 'tanh'))"
      ],
      "metadata": {
        "id": "kCloy0YPiBOh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'mean_squared_error') "
      ],
      "metadata": {
        "id": "d6G9Og_Cib1f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the neural network"
      ],
      "metadata": {
        "id": "deCp_PGCk-ZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train, epochs = 300)"
      ],
      "metadata": {
        "id": "30lW_qzEi23c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "262d32a6-02c4-4058-bc1a-401bc51135eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2128\n",
            "Epoch 2/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2125\n",
            "Epoch 3/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2123\n",
            "Epoch 4/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2120\n",
            "Epoch 5/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2117\n",
            "Epoch 6/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2115\n",
            "Epoch 7/300\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.2112\n",
            "Epoch 8/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.2109\n",
            "Epoch 9/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.2106\n",
            "Epoch 10/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.2103\n",
            "Epoch 11/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2101\n",
            "Epoch 12/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2098\n",
            "Epoch 13/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2095\n",
            "Epoch 14/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2092\n",
            "Epoch 15/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2089\n",
            "Epoch 16/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2086\n",
            "Epoch 17/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2083\n",
            "Epoch 18/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2080\n",
            "Epoch 19/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2078\n",
            "Epoch 20/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2075\n",
            "Epoch 21/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2072\n",
            "Epoch 22/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2069\n",
            "Epoch 23/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2066\n",
            "Epoch 24/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2063\n",
            "Epoch 25/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2060\n",
            "Epoch 26/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2057\n",
            "Epoch 27/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2054\n",
            "Epoch 28/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2051\n",
            "Epoch 29/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2048\n",
            "Epoch 30/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2045\n",
            "Epoch 31/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2042\n",
            "Epoch 32/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2039\n",
            "Epoch 33/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2036\n",
            "Epoch 34/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2032\n",
            "Epoch 35/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.2029\n",
            "Epoch 36/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2026\n",
            "Epoch 37/300\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.2023\n",
            "Epoch 38/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2020\n",
            "Epoch 39/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2017\n",
            "Epoch 40/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2014\n",
            "Epoch 41/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2011\n",
            "Epoch 42/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2007\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.2004\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1998\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1995\n",
            "Epoch 47/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1992\n",
            "Epoch 48/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1988\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1985\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1982\n",
            "Epoch 51/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1979\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1975\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1972\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1969\n",
            "Epoch 55/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1966\n",
            "Epoch 56/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1962\n",
            "Epoch 57/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1959\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1949\n",
            "Epoch 61/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.1946\n",
            "Epoch 62/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1943\n",
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            "1/1 [==============================] - 0s 7ms/step - loss: 0.1939\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1936\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1933\n",
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            "Epoch 67/300\n",
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            "Epoch 68/300\n",
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            "Epoch 69/300\n",
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            "Epoch 70/300\n",
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            "Epoch 73/300\n",
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            "Epoch 74/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1902\n",
            "Epoch 75/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1899\n",
            "Epoch 76/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1895\n",
            "Epoch 77/300\n",
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            "Epoch 78/300\n",
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            "Epoch 79/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1885\n",
            "Epoch 80/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1882\n",
            "Epoch 81/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1878\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1871\n",
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            "Epoch 88/300\n",
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            "Epoch 89/300\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.1850\n",
            "Epoch 90/300\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1836\n",
            "Epoch 94/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1833\n",
            "Epoch 95/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1829\n",
            "Epoch 96/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1826\n",
            "Epoch 97/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1822\n",
            "Epoch 98/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1819\n",
            "Epoch 99/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.1815\n",
            "Epoch 100/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1812\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1808\n",
            "Epoch 102/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1805\n",
            "Epoch 103/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1801\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 0.1798\n",
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            "Epoch 106/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1790\n",
            "Epoch 107/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1787\n",
            "Epoch 108/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1783\n",
            "Epoch 109/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1780\n",
            "Epoch 110/300\n",
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            "Epoch 111/300\n",
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            "Epoch 112/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1769\n",
            "Epoch 113/300\n",
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            "Epoch 114/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.1762\n",
            "Epoch 115/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1758\n",
            "Epoch 116/300\n",
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            "Epoch 117/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1751\n",
            "Epoch 118/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1747\n",
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            "1/1 [==============================] - 0s 7ms/step - loss: 0.1740\n",
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            "Epoch 122/300\n",
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            "Epoch 128/300\n",
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            "Epoch 137/300\n",
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            "Epoch 156/300\n",
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            "Epoch 164/300\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 0.1571\n",
            "Epoch 167/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1567\n",
            "Epoch 168/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1563\n",
            "Epoch 169/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1559\n",
            "Epoch 170/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1556\n",
            "Epoch 171/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1552\n",
            "Epoch 172/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1548\n",
            "Epoch 173/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1544\n",
            "Epoch 174/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1541\n",
            "Epoch 175/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1537\n",
            "Epoch 176/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1533\n",
            "Epoch 177/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.1529\n",
            "Epoch 178/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1525\n",
            "Epoch 179/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1522\n",
            "Epoch 180/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1518\n",
            "Epoch 181/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1514\n",
            "Epoch 182/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1510\n",
            "Epoch 183/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1507\n",
            "Epoch 184/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1503\n",
            "Epoch 185/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1499\n",
            "Epoch 186/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1495\n",
            "Epoch 187/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1491\n",
            "Epoch 188/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1488\n",
            "Epoch 189/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1484\n",
            "Epoch 190/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1480\n",
            "Epoch 191/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1476\n",
            "Epoch 192/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1472\n",
            "Epoch 193/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1469\n",
            "Epoch 194/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1465\n",
            "Epoch 195/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1461\n",
            "Epoch 196/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1457\n",
            "Epoch 197/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1453\n",
            "Epoch 198/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1450\n",
            "Epoch 199/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1446\n",
            "Epoch 200/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1442\n",
            "Epoch 201/300\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.1438\n",
            "Epoch 202/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1434\n",
            "Epoch 203/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1430\n",
            "Epoch 204/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1427\n",
            "Epoch 205/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.1423\n",
            "Epoch 206/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.1419\n",
            "Epoch 207/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1415\n",
            "Epoch 208/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1411\n",
            "Epoch 209/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1408\n",
            "Epoch 210/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1404\n",
            "Epoch 211/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1400\n",
            "Epoch 212/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1396\n",
            "Epoch 213/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1392\n",
            "Epoch 214/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1388\n",
            "Epoch 215/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.1385\n",
            "Epoch 216/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1381\n",
            "Epoch 217/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1377\n",
            "Epoch 218/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1373\n",
            "Epoch 219/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1369\n",
            "Epoch 220/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1365\n",
            "Epoch 221/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1362\n",
            "Epoch 222/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1358\n",
            "Epoch 223/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1354\n",
            "Epoch 224/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1350\n",
            "Epoch 225/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.1346\n",
            "Epoch 226/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1342\n",
            "Epoch 227/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1339\n",
            "Epoch 228/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1335\n",
            "Epoch 229/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1331\n",
            "Epoch 230/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1327\n",
            "Epoch 231/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1323\n",
            "Epoch 232/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1319\n",
            "Epoch 233/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1316\n",
            "Epoch 234/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1312\n",
            "Epoch 235/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1308\n",
            "Epoch 236/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1304\n",
            "Epoch 237/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1300\n",
            "Epoch 238/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1296\n",
            "Epoch 239/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1293\n",
            "Epoch 240/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1289\n",
            "Epoch 241/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1285\n",
            "Epoch 242/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1281\n",
            "Epoch 243/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1277\n",
            "Epoch 244/300\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.1273\n",
            "Epoch 245/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1270\n",
            "Epoch 246/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1266\n",
            "Epoch 247/300\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.1262\n",
            "Epoch 248/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1258\n",
            "Epoch 249/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1254\n",
            "Epoch 250/300\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.1251\n",
            "Epoch 251/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1247\n",
            "Epoch 252/300\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.1243\n",
            "Epoch 253/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1239\n",
            "Epoch 254/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1235\n",
            "Epoch 255/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1232\n",
            "Epoch 256/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1228\n",
            "Epoch 257/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1224\n",
            "Epoch 258/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1220\n",
            "Epoch 259/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1216\n",
            "Epoch 260/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1213\n",
            "Epoch 261/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1209\n",
            "Epoch 262/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1205\n",
            "Epoch 263/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1201\n",
            "Epoch 264/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1197\n",
            "Epoch 265/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1194\n",
            "Epoch 266/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1190\n",
            "Epoch 267/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1186\n",
            "Epoch 268/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1182\n",
            "Epoch 269/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1179\n",
            "Epoch 270/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1175\n",
            "Epoch 271/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1171\n",
            "Epoch 272/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1167\n",
            "Epoch 273/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1163\n",
            "Epoch 274/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1160\n",
            "Epoch 275/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1156\n",
            "Epoch 276/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1152\n",
            "Epoch 277/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1148\n",
            "Epoch 278/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1145\n",
            "Epoch 279/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1141\n",
            "Epoch 280/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1137\n",
            "Epoch 281/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1134\n",
            "Epoch 282/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1130\n",
            "Epoch 283/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1126\n",
            "Epoch 284/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1122\n",
            "Epoch 285/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1119\n",
            "Epoch 286/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1115\n",
            "Epoch 287/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1111\n",
            "Epoch 288/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1107\n",
            "Epoch 289/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1104\n",
            "Epoch 290/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1100\n",
            "Epoch 291/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1096\n",
            "Epoch 292/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.1093\n",
            "Epoch 293/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.1089\n",
            "Epoch 294/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1085\n",
            "Epoch 295/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1082\n",
            "Epoch 296/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.1078\n",
            "Epoch 297/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1074\n",
            "Epoch 298/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.1071\n",
            "Epoch 299/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1067\n",
            "Epoch 300/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1063\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the neural network to predict the training data (we are training to measure just training accuracy at this point)"
      ],
      "metadata": {
        "id": "s6BXHVONlDm5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_train)\n",
        "y_pred_binary = (y_pred > 0.5)*1\n",
        "print('Predictions:\\n', y_pred_binary)\n",
        "print('Ground Truth\\n', y_train)\n",
        "print('Training Accuracy')"
      ],
      "metadata": {
        "id": "W2QXOG5qi9qI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f64b61b-9498-48ca-c83e-1239daa83575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions:\n",
            " [[0]\n",
            " [1]\n",
            " [1]\n",
            " [0]]\n",
            "Ground Truth\n",
            " [[0.]\n",
            " [1.]\n",
            " [1.]\n",
            " [0.]]\n",
            "Training Accuracy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercise 1: Modify the number of epochs that you use for training your model until you achieve good training set accuracy.  What number of epochs did you end up choosing?\n",
        "\n",
        "Note that this is not about finding an exact number but finding a number of epochs that enables you to predict the training set correctly (the classess predicted by y_pred_binary should be the same as those in x_train)"
      ],
      "metadata": {
        "id": "K6dKHP3OlZGh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of epochs I chose is: 300"
      ],
      "metadata": {
        "id": "4t6M8R8TmAPc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the evolution of the loss as the model continues to converge to the best solution (network weights that achieve minimum loss)"
      ],
      "metadata": {
        "id": "6Lqe6tuLlLBN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jBJZ1FmSU4vu",
        "outputId": "1788c3bc-b5e1-4c75-ce11-14db0b114979"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fee55da8490>]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# printing the trained weights\n",
        "keras_weights = model.get_weights()\n",
        "keras_weights"
      ],
      "metadata": {
        "id": "U3CN9mhBleNN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "646695c4-f8d2-46e9-e82c-4c5dce4c0420"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 0.8020827 ,  0.42220902, -0.9413251 ],\n",
              "        [-0.7295345 ,  0.1514431 ,  1.2453367 ]], dtype=float32),\n",
              " array([0.5619316 , 0.49155736, 0.55071145], dtype=float32),\n",
              " array([[-1.1906048],\n",
              "        [ 1.046126 ],\n",
              "        [-0.7475789]], dtype=float32),\n",
              " array([0.6924016], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}