Artificial Neural Networks with Keras.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGq5Q_FjW-K3"
      },
      "source": [
        "# Artificial Neural Networks with Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWvi1y40W-K7"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/AbdelMahm/INPT-2020/blob/master/Notebooks_DL/ANN_with_keras.ipynb\"><img src=\"https://colab.research.google.com/img/colab_favicon_256px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBClx10RW-K8"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Firstly, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "PnmL_TRbW-K9"
      },
      "outputs": [],
      "source": [
        "# TensorFlow ≥2.0 is required\n",
        "import tensorflow as tf\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(42)\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# Ignore useless warnings (see SciPy issue #5998)\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0NMmlH8W-K9"
      },
      "source": [
        "# Building an Image Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFcr38YkW-K-"
      },
      "source": [
        "First let's import TensorFlow and Keras."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "id": "l94Xa7m1W-K-"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "pT4xP5LMW-K_",
        "outputId": "e20de564-7213-4749-9384-69bd47e20ca7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2.8.0'"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tf.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xPp5La8W-K_"
      },
      "source": [
        "Let's start by loading the fashion MNIST dataset. Keras has a number of functions to load popular datasets in `keras.datasets`. The dataset is already split for you between a training set and a test set, but it can be useful to split the training set further to have a validation set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "DDM4jWA6W-K_",
        "outputId": "8e2d4248-a180-4b42-b609-7729e80554c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 1us/step\n",
            "40960/29515 [=========================================] - 0s 1us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 3s 0us/step\n",
            "26435584/26421880 [==============================] - 4s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0s/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 1s 0us/step\n",
            "4431872/4422102 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdpijPJFW-LA"
      },
      "source": [
        "The training set contains 60,000 grayscale images, each 28x28 pixels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5XkunuWW-LA",
        "outputId": "94c1fd56-b5ba-438f-c3d7-814b354010e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_full.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOdtPpS0W-LA"
      },
      "source": [
        "Each pixel intensity is represented as a byte (0 to 255):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fcIdTmCW-LA",
        "outputId": "96e74ada-535c-4105-a297-b25559a1a505"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dtype('uint8')"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_full.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-HPTgpeW-LA"
      },
      "source": [
        "Let's split the full training set into a validation set and a (smaller) training set. We also scale the pixel intensities down to the 0-1 range and convert them to floats, by dividing by 255."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "id": "drQqMQSzW-LB"
      },
      "outputs": [],
      "source": [
        "X_valid, X_train = X_train_full[:5000] / 255., X_train_full[5000:] / 255.\n",
        "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
        "X_test = X_test / 255."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKH9RPUgW-LB"
      },
      "source": [
        "You can plot an image using Matplotlib's `imshow()` function, with a `'binary'`\n",
        " color map:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "a7WJWFAvW-LB",
        "outputId": "0ae9936e-4a93-40a9-d8cb-4d83fcfbd9d5"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(X_train[7], cmap=\"binary\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FifaACwsW-LB"
      },
      "source": [
        "The labels are the class IDs (represented as uint8), from 0 to 9:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMXvZ0z_W-LC",
        "outputId": "168539bc-c375-4c5a-a6bb-f599792ba4b1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([4, 0, 7, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Q1y6puFW-LC"
      },
      "source": [
        "Here are the corresponding class names:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "id": "jqt92NRMW-LC"
      },
      "outputs": [],
      "source": [
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
        "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrQfX9BuW-LC"
      },
      "source": [
        "So the first image in the training set is a coat:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "E9-L_QupW-LC",
        "outputId": "d62e98a4-3d7b-4cc2-bfc1-5c34f51c4c16"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Coat'"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class_names[y_train[0]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImMzOFPYW-LC"
      },
      "source": [
        "The validation set contains 5,000 images, and the test set contains 10,000 images:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gys3ODEKW-LC",
        "outputId": "ec45a02d-7293-491f-e39d-f19b2c6fb1a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5000, 28, 28)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_valid.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7NYAWu6W-LE",
        "outputId": "024c846c-acc8-406f-a7bc-4c113abad6da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3BgFBNNW-LE"
      },
      "source": [
        "Let's take a look at a sample of the images in the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "FHfrelaYW-LE",
        "outputId": "7e3e4148-dca5-42e7-edbe-012be1818914"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x345.6 with 40 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_rows = 4\n",
        "n_cols = 10\n",
        "plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
        "for row in range(n_rows):\n",
        "    for col in range(n_cols):\n",
        "        index = n_cols * row + col\n",
        "        plt.subplot(n_rows, n_cols, index + 1)\n",
        "        plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\")\n",
        "        plt.axis('off')\n",
        "        plt.title(class_names[y_train[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8GdpjQwW-LE"
      },
      "source": [
        "## Construct a Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "collapsed": true,
        "id": "4moz7eyzW-LE"
      },
      "outputs": [],
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "collapsed": true,
        "id": "qW6QS-ggW-LE"
      },
      "outputs": [],
      "source": [
        "keras.backend.clear_session()\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cc3Gz-htW-LE"
      },
      "source": [
        "### or using the folowing method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "collapsed": true,
        "id": "MC_TlWEcW-LE"
      },
      "outputs": [],
      "source": [
        "model = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "    keras.layers.Dense(300, activation=\"relu\"),\n",
        "    keras.layers.Dense(100, activation=\"relu\"),\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bebyx1tiW-LF",
        "outputId": "656843e2-50d1-48c1-9835-5ee13787b842"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<keras.layers.core.flatten.Flatten at 0x29c1a5163e0>,\n",
              " <keras.layers.core.dense.Dense at 0x29c1a516cb0>,\n",
              " <keras.layers.core.dense.Dense at 0x29c7ff7c700>,\n",
              " <keras.layers.core.dense.Dense at 0x29c7ff7e770>]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mEXtQ26W-LF",
        "outputId": "2d210e32-fb24-4dcc-ddc3-c89921155673"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 300)               235500    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 100)               30100     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "id": "-KR3pwsVW-LF"
      },
      "outputs": [],
      "source": [
        "#keras.utils.plot_model(model, \"my_mnist_model.png\", show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "cfjAQ9hmW-LF",
        "outputId": "eb6f0212-ee84-42fe-ca8d-3944bc930d28"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'dense'"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hidden1 = model.layers[1]\n",
        "hidden1.name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRrfiiVCW-LF",
        "outputId": "3eaab14d-2d1d-4b2b-c966-9387197a04b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.get_layer(hidden1.name) is hidden1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "collapsed": true,
        "id": "Lt6FfP1vW-LF"
      },
      "outputs": [],
      "source": [
        "weights, biases = hidden1.get_weights()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tX_2NmNPW-LF",
        "outputId": "73159de1-bfb9-44db-f553-4df579e6d805"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-0.0112348 , -0.03483202, -0.03595975, ..., -0.00840872,\n",
              "         0.02643824,  0.06995866],\n",
              "       [-0.0622174 ,  0.05319916,  0.03744468, ...,  0.04900258,\n",
              "         0.00427967,  0.02021929],\n",
              "       [-0.01587974, -0.02756941, -0.00281294, ...,  0.04982504,\n",
              "         0.01766291, -0.00719861],\n",
              "       ...,\n",
              "       [ 0.00972795, -0.06858423,  0.07177235, ...,  0.02531698,\n",
              "        -0.04104866, -0.03784868],\n",
              "       [-0.02973263, -0.04966753, -0.02394531, ...,  0.02491154,\n",
              "         0.03247409,  0.0016934 ],\n",
              "       [-0.04853391,  0.0364758 , -0.00827939, ..., -0.06740534,\n",
              "         0.02054524, -0.0220806 ]], dtype=float32)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6YXgYz97W-LF",
        "outputId": "81b9bf5a-fd18-452c-b40a-a761096dd026"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(784, 300)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-muG25p5W-LG",
        "outputId": "bfdeab25-7fca-4f2d-8743-be147da626c5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "biases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdoW7n3YW-LG",
        "outputId": "636a5f8a-c934-4ddc-c2c0-ecf1e924d70a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(300,)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "biases.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCmaOTDmW-LG"
      },
      "source": [
        "### Compile the Model\n",
        "Here you can chose the loss function, the optimizer, the performance metric, etc. check Keras documentation for further details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "collapsed": true,
        "id": "Me6AGlCBW-LG"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDPmL9STW-LG"
      },
      "source": [
        "This is equivalent to:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xghgPtszW-LG"
      },
      "source": [
        "```python\n",
        "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.SGD(),\n",
        "              metrics=[keras.metrics.sparse_categorical_accuracy])\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x39dB8kcW-LG"
      },
      "source": [
        "### Train the model\n",
        "Here you can chose how many epochs you need and the batch size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAJHAdKzW-LG",
        "outputId": "186d36cb-a4af-4798-8375-4717b3075efb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.7194 - accuracy: 0.7640 - val_loss: 0.5021 - val_accuracy: 0.8388\n",
            "Epoch 2/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.4894 - accuracy: 0.8293 - val_loss: 0.4358 - val_accuracy: 0.8550\n",
            "Epoch 3/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4436 - accuracy: 0.8445 - val_loss: 0.4084 - val_accuracy: 0.8644\n",
            "Epoch 4/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.4157 - accuracy: 0.8531 - val_loss: 0.3955 - val_accuracy: 0.8644\n",
            "Epoch 5/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3955 - accuracy: 0.8615 - val_loss: 0.3922 - val_accuracy: 0.8658\n",
            "Epoch 6/30\n",
            "1719/1719 [==============================] - 6s 3ms/step - loss: 0.3790 - accuracy: 0.8664 - val_loss: 0.3812 - val_accuracy: 0.8690\n",
            "Epoch 7/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.3653 - accuracy: 0.8719 - val_loss: 0.3619 - val_accuracy: 0.8754\n",
            "Epoch 8/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3532 - accuracy: 0.8753 - val_loss: 0.3615 - val_accuracy: 0.8730\n",
            "Epoch 9/30\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3428 - accuracy: 0.8783 - val_loss: 0.3472 - val_accuracy: 0.8790\n",
            "Epoch 10/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3329 - accuracy: 0.8814 - val_loss: 0.3483 - val_accuracy: 0.8760\n",
            "Epoch 11/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3246 - accuracy: 0.8850 - val_loss: 0.3371 - val_accuracy: 0.8814\n",
            "Epoch 12/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3169 - accuracy: 0.8858 - val_loss: 0.3555 - val_accuracy: 0.8726\n",
            "Epoch 13/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3095 - accuracy: 0.8891 - val_loss: 0.3478 - val_accuracy: 0.8746\n",
            "Epoch 14/30\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3023 - accuracy: 0.8907 - val_loss: 0.3285 - val_accuracy: 0.8814\n",
            "Epoch 15/30\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2977 - accuracy: 0.8917 - val_loss: 0.3245 - val_accuracy: 0.8824\n",
            "Epoch 16/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2903 - accuracy: 0.8967 - val_loss: 0.3191 - val_accuracy: 0.8898\n",
            "Epoch 17/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2840 - accuracy: 0.8974 - val_loss: 0.3174 - val_accuracy: 0.8864\n",
            "Epoch 18/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2798 - accuracy: 0.8989 - val_loss: 0.3058 - val_accuracy: 0.8930\n",
            "Epoch 19/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2745 - accuracy: 0.9021 - val_loss: 0.3158 - val_accuracy: 0.8862\n",
            "Epoch 20/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2692 - accuracy: 0.9036 - val_loss: 0.3068 - val_accuracy: 0.8848\n",
            "Epoch 21/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2646 - accuracy: 0.9036 - val_loss: 0.2998 - val_accuracy: 0.8944\n",
            "Epoch 22/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2594 - accuracy: 0.9064 - val_loss: 0.3139 - val_accuracy: 0.8850\n",
            "Epoch 23/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2543 - accuracy: 0.9082 - val_loss: 0.3136 - val_accuracy: 0.8860\n",
            "Epoch 24/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2500 - accuracy: 0.9101 - val_loss: 0.3136 - val_accuracy: 0.8888\n",
            "Epoch 25/30\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2458 - accuracy: 0.9106 - val_loss: 0.3052 - val_accuracy: 0.8908\n",
            "Epoch 26/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2416 - accuracy: 0.9125 - val_loss: 0.3043 - val_accuracy: 0.8914\n",
            "Epoch 27/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2375 - accuracy: 0.9143 - val_loss: 0.3036 - val_accuracy: 0.8898\n",
            "Epoch 28/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2335 - accuracy: 0.9156 - val_loss: 0.3103 - val_accuracy: 0.8882\n",
            "Epoch 29/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2296 - accuracy: 0.9171 - val_loss: 0.3013 - val_accuracy: 0.8920\n",
            "Epoch 30/30\n",
            "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2265 - accuracy: 0.9176 - val_loss: 0.2952 - val_accuracy: 0.8952\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(X_train, y_train, batch_size=32, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "ZhC7SREbW-LG"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'verbose': 1, 'epochs': 30, 'steps': 1719}"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "history.params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "38D_Es7MW-LG"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n"
          ]
        }
      ],
      "source": [
        "print(history.epoch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "YIEObQ5rW-LG"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "history.history.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHvLK7UCW-LG"
      },
      "source": [
        "### Plot learning Curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "1RJjIcNPW-LH"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "LkTSz-SrW-LH"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3265 - accuracy: 0.8828\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.326519250869751, 0.8827999830245972]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "BE1-c3cbW-LH"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.02, 0.  , 0.98],\n",
              "       [0.  , 0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],\n",
              "       [0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_new = X_test[:3]\n",
        "y_proba = model.predict(X_new)\n",
        "y_proba.round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "8TXRyxLBW-LH"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([9, 2, 1], dtype=int64)"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = model.predict(X_new)\n",
        "y_predx=np.argmax(y_pred,axis=1)\n",
        "y_predx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "T61B-sSAW-LH"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Ankle boot', 'Pullover', 'Trouser'], dtype='<U11')"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.array(class_names)[y_predx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "x3ulfGxIW-LH"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([9, 2, 1], dtype=uint8)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_new = y_test[:3]\n",
        "y_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "SyEnmI8yW-LH"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 518.4x172.8 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(7.2, 2.4))\n",
        "for index, image in enumerate(X_new):\n",
        "    plt.subplot(1, 3, index + 1)\n",
        "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\")\n",
        "    plt.axis('off')\n",
        "    plt.title(class_names[y_test[index]], fontsize=12)\n",
        "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "k_D3BJHPW-LH"
      },
      "source": [
        "## Exercise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cF7r17BNW-LH"
      },
      "source": [
        "#### 1) Plot Learning curves when using different optimizers\n",
        "#### 2) Plot Learning curves when using different batch size\n",
        "#### 3) Plot Learning curves when using different number of epochs\n",
        "#### 4) Add additional neurons and use Dropout Layer. Plot Learning curves.\n",
        "#### 5) Use Batch Normalisation\n",
        "#### 6) repeat with more layers\n",
        "#### 7) apply on different datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1) Plot Learning curves when using different optimizers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Learning curves using \"Adam\" optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "ZlpWS_nkW-LH"
      },
      "outputs": [],
      "source": [
        "model_1 = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "    keras.layers.Dense(300, activation=\"relu\"),\n",
        "    keras.layers.Dense(100, activation=\"relu\"),\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model_1.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"adam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history_1 = model_1.fit(X_train, y_train, batch_size=32, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1332x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.DataFrame(history.history)\n",
        "data_1= pd.DataFrame(history_1.history)\n",
        "\n",
        "f, (ax1, ax2) = plt.subplots(1, 2)\n",
        "f.set_size_inches(18.5, 3.5)\n",
        "f.suptitle('Learning curves using', fontsize=16)\n",
        "\n",
        "\n",
        "ax1.plot(data_1)\n",
        "ax1.set_title(' \"Adam\" optimizer', color='blue')\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.plot(data)\n",
        "ax2.set_title('\"SGD\" optimizer', color='blue')\n",
        "ax2.grid(True)\n",
        "ax2.set\n",
        "\n",
        "f.legend(data_1.columns, loc='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2) Plot Learning curves when using different batch size\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model_2 = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "    keras.layers.Dense(300, activation=\"relu\"),\n",
        "    keras.layers.Dense(100, activation=\"relu\"),\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model_2.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history_2 = model_2.fit(X_train, y_train, batch_size=15, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1332x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "data = pd.DataFrame(history.history)\n",
        "data_2= pd.DataFrame(history_2.history)\n",
        "\n",
        "f, (ax1, ax2) = plt.subplots(1, 2)\n",
        "f.set_size_inches(18.5, 3.5)\n",
        "f.suptitle('Learning curves using', fontsize=16)\n",
        "\n",
        "ax1.plot(data_2)\n",
        "ax1.set_title('batch_size = 32', color='blue')\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.plot(data)\n",
        "ax2.set_title('batch_size = 15', color='blue')\n",
        "ax2.grid(True)\n",
        "\n",
        "f.legend(data.columns, loc='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3) Plot Learning curves when using different number of epochs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model_3 = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "    keras.layers.Dense(300, activation=\"relu\"),\n",
        "    keras.layers.Dense(100, activation=\"relu\"),\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model_3.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history_3 = model_3.fit(X_train, y_train, batch_size=32, epochs=17,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1332x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = pd.DataFrame(history.history)\n",
        "data_3= pd.DataFrame(history_3.history)\n",
        "\n",
        "f, (ax1, ax2) = plt.subplots(1, 2)\n",
        "f.set_size_inches(18.5, 3.5)\n",
        "f.suptitle('Learning curves using', fontsize=16)\n",
        "\n",
        "\n",
        "ax1.plot(data_3)\n",
        "ax1.set_title('epochs = 32', color='blue')\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.plot(data)\n",
        "ax2.set_title('epochs = 17', color='blue')\n",
        "ax2.grid(True)\n",
        "\n",
        "f.legend(data.columns, loc='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4) Add additional neurons and use Dropout Layer. Plot Learning curves.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {},
      "outputs": [],
      "source": [
        "from keras.layers import Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model_4 = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "    keras.layers.Dense(300, activation=\"relu\"),\n",
        "    #Add droupout on hidden layers\n",
        "    keras.layers.Dropout(0.2),\n",
        "    keras.layers.Dense(100, activation=\"relu\"),\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "\n",
        "model_4.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history_4 = model_4.fit(X_train, y_train, batch_size=32, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1332x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = pd.DataFrame(history.history)\n",
        "data_4= pd.DataFrame(history_3.history)\n",
        "\n",
        "f, (ax1, ax2) = plt.subplots(1, 2)\n",
        "f.set_size_inches(18.5, 3.5)\n",
        "f.suptitle('Learning curves ', fontsize=16)\n",
        "\n",
        "\n",
        "ax1.plot(data_4)\n",
        "ax1.set_title('with Dropout layer', color='blue')\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.plot(data)\n",
        "ax2.set_title('without Dropout layer', color='blue')\n",
        "ax2.grid(True)\n",
        "\n",
        "f.legend(data.columns, loc='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5) Use Batch Normalisation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model_4 = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "    keras.layers.Dense(300, activation=\"relu\"),\n",
        "    #Add BatchNormalization layers\n",
        "    keras.layers.BatchNormalization(input_shape=(2,)),\n",
        "    keras.layers.Dense(100, activation=\"relu\"),\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "\n",
        "model_4.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history_4 = model_4.fit(X_train, y_train, batch_size=32, epochs=30,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1332x252 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = pd.DataFrame(history.history)\n",
        "data_4= pd.DataFrame(history_3.history)\n",
        "\n",
        "f, (ax1, ax2) = plt.subplots(1, 2)\n",
        "f.set_size_inches(18.5, 3.5)\n",
        "f.suptitle('Learning curves ', fontsize=16)\n",
        "\n",
        "\n",
        "ax1.plot(data_4)\n",
        "ax1.set_title('with BatchNormalization layer', color='blue')\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.plot(data)\n",
        "ax2.set_title('without BatchNormalization layer', color='blue')\n",
        "ax2.grid(True)\n",
        "\n",
        "f.legend(data.columns, loc='center')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ANN_with_keras.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.2"
    },
    "nav_menu": {
      "height": "264px",
      "width": "369px"
    },
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": false
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}