CNN_Fashion_MNIST_Example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN Fashion MNIST Example.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/markwest1972/CNN-Example-Google-Colaboratory/blob/master/CNN_Fashion_MNIST_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjmUDGHLWpbz",
        "colab_type": "text"
      },
      "source": [
        "## What is this?\n",
        "\n",
        "This Jupyter Notebook contains Python code for building a Convolutional Neural Network that gives 90% accuracy on classification of the Fashion MNIST Dataset. More information is given on [this blogpost](https://www.bouvet.no/bouvet-deler/understanding-convolutional-neural-networks-part-2).\n",
        "\n",
        "This code is supplied without license, warranty or support. Feel free to do with it what you will."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3D86_YqBol8e",
        "colab_type": "text"
      },
      "source": [
        "## Built for Google Collaboratory ##\n",
        "\n",
        "Train your network more quickly in Google Collaboratory. From the **Runtime** menu select **Change Runtime Type** and choose \"GPU\"!\n",
        "\n",
        "Don't forget to select **Runtime** -> **Restart runtime** to put your changes into effect!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSRFTqbOYAUY",
        "colab_type": "text"
      },
      "source": [
        "## Setting up\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HNLKc5fX9Nw",
        "colab_type": "code",
        "outputId": "b4fd2185-7a9d-4437-c57d-11288a96f11d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "# All the imports!\n",
        "import tensorflow as tf # tested with 1.14.0\n",
        "import numpy as np # tested with 1.16.4\n",
        "import matplotlib.pyplot as plt #tested with 3.0.3\n",
        "from sklearn.metrics import classification_report # tested with 0.21.2l\n",
        "\n",
        "# Supress deprecation warnings\n",
        "import logging\n",
        "logging.getLogger('tensorflow').disabled = True\n",
        "\n",
        "# Fetch \"Fashion MNIST\" data\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "# A good rule of thumb is to normalise input values - i.e. transform them to a\n",
        "# scale of 0 to 1. Each element in this dataset is a pixel value of 0 to 255, so\n",
        "# we'll normalise / rescale these values.\n",
        "x_train = x_train / 255.0\n",
        "x_test = x_test / 255.0\n",
        "\n",
        "# Map for human readable class names\n",
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcMYWwb9YjMe",
        "colab_type": "text"
      },
      "source": [
        "## Get data shapes\n",
        "\n",
        "We are expecting 60000 training images and 10000 test images, where each image is comprised of 28x28 greyscale pixel values. \n",
        "\n",
        "In addition we have the classes to which each image belongs. These are held in seperate files.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svbfCKBOYp-b",
        "colab_type": "code",
        "outputId": "ef5f26e7-9df8-488b-ef3d-a406bbdf47e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        }
      },
      "source": [
        "print(\"Shape of Training Image Data: \" + str(x_train.shape))\n",
        "print(\"Shape of Training Class Data: \" + str(y_train.shape))\n",
        "print(\"Shape of Test Image Data: \" + str(x_test.shape))\n",
        "print(\"Shape of Test Class Data: \" + str(y_test.shape))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Training Image Data: (60000, 28, 28)\n",
            "Shape of Training Class Data: (60000,)\n",
            "Shape of Test Image Data: (10000, 28, 28)\n",
            "Shape of Test Class Data: (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnPYVK-1Y6Pk",
        "colab_type": "text"
      },
      "source": [
        "## Visualise first 25 images from training data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ym0c8NpnY50k",
        "colab_type": "code",
        "outputId": "0050a6f1-849e-4de1-8ddd-343f4d06ab2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        }
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[y_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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YPXv2u46jLfniF78YlVVXV7vstddec9kNN9zgspkzZ7qsd+/eLlOvQWlpqcvU65xa9K/E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qIi6fZqzKrmHGo71chGnc/q8ZWVlSUdX0PgEysAAAAAyIiFFQAAAABkxMIKAAAAADJiYQUAAAAA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\nAACAjLiwAgAAAICMuLACAAAAgIz+H+MNqul7qrx0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x1080 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iontg782ZeH5",
        "colab_type": "text"
      },
      "source": [
        "## Visualise image with pixel values\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9KfNXIuZp0Y",
        "colab_type": "code",
        "outputId": "d05c719e-6a29-40e6-a979-97e1698e8fa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        }
      },
      "source": [
        "# Lets view the first image and classname in the dataset \n",
        "# Tip: Change \"index\" value to view different images.\n",
        "index = 0\n",
        "plt.figure(figsize=(20,16))\n",
        "plt.imshow(x_train[index], cmap=plt.cm.binary)\n",
        "plt.xlabel(class_names[y_train[index]])\n",
        "plt.colorbar()\n",
        "#plt.grid(True)\n",
        "#plt.rc('grid', linestyle=\"-\", color='fuchsia')\n",
        "\n",
        "ax = plt.gca()\n",
        "ax.set_xticks(np.arange(-.5, 28, 1))\n",
        "ax.set_yticks(np.arange(-.5, 28, 1))\n",
        "ax.set_xticklabels(np.arange(0, 29, 1))\n",
        "ax.set_yticklabels(np.arange(0, 29, 1))\n",
        "ax.xaxis.tick_top()\n",
        "\n",
        "# Adds Pixel Values on top of image\n",
        "#for i in range(28):\n",
        "#    for j in range(28):\n",
        "#        text = ax.text(j, i, round(x_train[index][i, j], 2),\n",
        "#                       ha=\"center\", va=\"center\", color=\"fuchsia\")\n",
        "        \n",
        "        \n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hAAAAAEZEIQAAAABGxB4BAAAArBR7BExnRQAAAACMiEIAAAAAjIjWAAAAAFaK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\ne1uSbUly+umnD/eVBgAAwNKrKq0BM8x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            "text/plain": [
              "<Figure size 1440x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PrT53xeSbDHe",
        "colab_type": "text"
      },
      "source": [
        "## Create and build Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_RzUtykbIbr",
        "colab_type": "code",
        "outputId": "b0d32a54-d943-43b1-b4c5-3716bf047615",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "# We begin by defining the a empty stack. We'll use this for building our \n",
        "# network, later by layer.\n",
        "model = tf.keras.models.Sequential()\n",
        "\n",
        "# We start with a convolutional layer this will extract features from \n",
        "# the input images by sliding a convolution filter over the input image, \n",
        "# resulting in a feature map.\n",
        "model.add(\n",
        "    tf.keras.layers.Conv2D(\n",
        "        filters=32, # How many filters we will learn \n",
        "        kernel_size=(3, 3), # Size of feature map that will slide over image\n",
        "        strides=(1, 1), # How the feature map \"steps\" across the image\n",
        "        padding='valid', # We are not using padding\n",
        "        activation='relu', # Rectified Linear Unit Activation Function\n",
        "        input_shape=(28, 28, 1) # The expected input shape for this layer\n",
        "    )\n",
        ") \n",
        "\n",
        "# The next layer we will add is a Maxpooling layer. This will reduce the \n",
        "# dimensionality of each feature, which reduces the number of parameters that \n",
        "# the model needs to learn, which shortens training time.\n",
        "model.add(\n",
        "    tf.keras.layers.MaxPooling2D(\n",
        "        pool_size=(2, 2), # Size feature will be mapped to\n",
        "        strides=(2, 2) # How the pool \"steps\" across the feature\n",
        "    )\n",
        ")\n",
        "          \n",
        "# We'll now add a dropout layer. This fights overfitting and forces the model to \n",
        "# learn multiple representations of the same data by randomly disabling neurons \n",
        "# in the learning phase.\n",
        "model.add(\n",
        "    tf.keras.layers.Dropout(\n",
        "        rate=0.25 # Randomly disable 25% of neurons\n",
        "    )\n",
        ")\n",
        "\n",
        "# Output from previous layer is a 3D tensor. This must be flattened to a 1D \n",
        "# vector before beiung fed to the Dense Layers.\n",
        "model.add(\n",
        "    tf.keras.layers.Flatten()\n",
        ")\n",
        "\n",
        "# A dense (interconnected) layer is added for mapping the derived features \n",
        "# to the required class.\n",
        "model.add(\n",
        "    tf.keras.layers.Dense(\n",
        "        units=128, # Output shape\n",
        "        activation='relu' # Rectified Linear Unit Activation Function\n",
        "    )\n",
        ")\n",
        "\n",
        "# Final layer with 10 outputs and a softmax activation. Softmax activation \n",
        "# enables me to calculate the output based on the probabilities. \n",
        "# Each class is assigned a probability and the class with the maximum \n",
        "# probability is the model’s output for the input.\n",
        "model.add(\n",
        "    tf.keras.layers.Dense(\n",
        "        units=10, # Output shape\n",
        "        activation='softmax' # Softmax Activation Function\n",
        "    )\n",
        ")\n",
        "\n",
        "# Build the model\n",
        "model.compile(\n",
        "    loss=tf.keras.losses.sparse_categorical_crossentropy, # loss function\n",
        "    optimizer=tf.keras.optimizers.Adam(), # optimizer function\n",
        "    metrics=['accuracy'] # reporting metric\n",
        ")\n",
        "\n",
        "# Display a summary of the models structure\n",
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 5408)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               692352    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 693,962\n",
            "Trainable params: 693,962\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DIbCDVxXQ10C",
        "colab_type": "text"
      },
      "source": [
        "## Visualise the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIF9upctQyy3",
        "colab_type": "code",
        "outputId": "7c9a4a42-2c15-47b0-a76b-dc80f831cd12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        }
      },
      "source": [
        "tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=False)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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3bt3C52767Q8AIxZ985TeRUREVFRU3Lt3b9WqVbIPMgG6s2bNGqr49/Lly2VXFRUVRUZG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\nAuHuoDiFQCDcHRSnEAiEu/MfvoE36z2uM2EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZpd_vWLcfU9",
        "colab_type": "text"
      },
      "source": [
        "## Train the CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNWdTFc3cilQ",
        "colab_type": "code",
        "outputId": "15d8d2f3-5a30-4f42-f5b0-1d8f9eae6439",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        }
      },
      "source": [
        "# Add an empty color dimension as the Convolutional net is expecting this\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "\n",
        "# Train the CNN on the training data\n",
        "history = model.fit(\n",
        "    \n",
        "      # Training data : features (images) and classes.\n",
        "      x_train, y_train,\n",
        "                    \n",
        "      # number of samples to work through before updating the \n",
        "      # internal model parameters via back propagation.\n",
        "      batch_size=256, \n",
        "\n",
        "      # An epoch is an iteration over the entire training data.\n",
        "      epochs=10, \n",
        "\n",
        "      # The model will set apart his fraction of the training \n",
        "      # data, will not train on it, and will evaluate the loss\n",
        "      # and any model metrics on this data at the end of \n",
        "      # each epoch. \n",
        "      validation_split=0.2, \n",
        "\n",
        "      verbose=1) "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/10\n",
            "48000/48000 [==============================] - 5s 109us/sample - loss: 0.5233 - acc: 0.8179 - val_loss: 0.3647 - val_acc: 0.8733\n",
            "Epoch 2/10\n",
            "48000/48000 [==============================] - 2s 44us/sample - loss: 0.3364 - acc: 0.8819 - val_loss: 0.3481 - val_acc: 0.8754\n",
            "Epoch 3/10\n",
            "48000/48000 [==============================] - 2s 44us/sample - loss: 0.2975 - acc: 0.8956 - val_loss: 0.2884 - val_acc: 0.8995\n",
            "Epoch 4/10\n",
            "48000/48000 [==============================] - 2s 43us/sample - loss: 0.2731 - acc: 0.9026 - val_loss: 0.2804 - val_acc: 0.9012\n",
            "Epoch 5/10\n",
            "48000/48000 [==============================] - 2s 43us/sample - loss: 0.2536 - acc: 0.9095 - val_loss: 0.2620 - val_acc: 0.9062\n",
            "Epoch 6/10\n",
            "48000/48000 [==============================] - 2s 43us/sample - loss: 0.2375 - acc: 0.9151 - val_loss: 0.2683 - val_acc: 0.9044\n",
            "Epoch 7/10\n",
            "48000/48000 [==============================] - 2s 44us/sample - loss: 0.2256 - acc: 0.9183 - val_loss: 0.2558 - val_acc: 0.9080\n",
            "Epoch 8/10\n",
            "48000/48000 [==============================] - 2s 43us/sample - loss: 0.2126 - acc: 0.9224 - val_loss: 0.2511 - val_acc: 0.9117\n",
            "Epoch 9/10\n",
            "48000/48000 [==============================] - 2s 43us/sample - loss: 0.1988 - acc: 0.9274 - val_loss: 0.2430 - val_acc: 0.9142\n",
            "Epoch 10/10\n",
            "48000/48000 [==============================] - 2s 43us/sample - loss: 0.1889 - acc: 0.9313 - val_loss: 0.2371 - val_acc: 0.9147\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7FI8OgXe00J",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate model with test data and view results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYRQQIKhexMe",
        "colab_type": "code",
        "outputId": "4e2cf74d-fcf2-47ef-f1f7-0142c93c85be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# Get Model Predictions for test data\n",
        "predicted_classes = model.predict_classes(x_test)\n",
        "print(classification_report(y_test, predicted_classes, target_names=class_names))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " T-shirt/top       0.86      0.88      0.87      1000\n",
            "     Trouser       0.99      0.98      0.98      1000\n",
            "    Pullover       0.82      0.89      0.85      1000\n",
            "       Dress       0.92      0.90      0.91      1000\n",
            "        Coat       0.86      0.82      0.84      1000\n",
            "      Sandal       0.98      0.98      0.98      1000\n",
            "       Shirt       0.76      0.73      0.74      1000\n",
            "     Sneaker       0.96      0.94      0.95      1000\n",
            "         Bag       0.97      0.98      0.98      1000\n",
            "  Ankle boot       0.95      0.97      0.96      1000\n",
            "\n",
            "    accuracy                           0.91     10000\n",
            "   macro avg       0.91      0.91      0.91     10000\n",
            "weighted avg       0.91      0.91      0.91     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5ISTseAhkm3",
        "colab_type": "text"
      },
      "source": [
        "## View examples of incorrectly classified test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFXEvBB9hvor",
        "colab_type": "code",
        "outputId": "f3f6ce4a-29f3-48f9-a025-18f8a1789f41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "incorrect = np.nonzero(predicted_classes!=y_test)[0]\n",
        "\n",
        "# Display the first 16 incorrectly classified images from the test data set\n",
        "plt.figure(figsize=(15, 8))\n",
        "for j, incorrect in enumerate(incorrect[0:8]):\n",
        "    plt.subplot(2, 4, j+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(x_test[incorrect].reshape(28, 28), cmap=\"Reds\")\n",
        "    plt.title(\"Predicted: {}\".format(class_names[predicted_classes[incorrect]]))\n",
        "    plt.xlabel(\"Actual: {}\".format(class_names[y_test[incorrect]]))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KxkQLAAAAAEpG9UYAAHZCSilsMzM/p31TvMA1K/345BlxTlTGvbc3zmlqccO1e2+Icxqb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\nWbrMbU4v+K+nJFXWvtNtry0MElMlqS84rmx+MSxJL2zwN3z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uSWb2dUnnp5T+t5k9a2Y/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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FNWsc2wp1-e",
        "colab_type": "text"
      },
      "source": [
        "## Print Feature Maps from the Convolutional Layer\n",
        "\n",
        "Inspired by this blogpost: https://towardsdatascience.com/visualizing-intermediate-activation-in-convolutional-neural-networks-with-keras-260b36d60d0.\n",
        "\n",
        "Basically what we do here is to copy a slice of our trained model - the Conv2D Layer. We then feed in a test image and visualise the output from the Conv2D - our feature maps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwyLfivClrRJ",
        "colab_type": "code",
        "outputId": "4d22cbb7-2c0f-4582-ccd4-a5b8b8011b3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "source": [
        "# The layer we want to copy from the trained CNN\n",
        "layer_name = 'conv2d' \n",
        "\n",
        "# Get the list of layers for the existing model\n",
        "layer_dict = {layer.name : layer for layer in model.layers}\n",
        "\n",
        "# Create a copy of our existing model containing just the Conv2D Layer\n",
        "modelslice = tf.keras.Model(inputs=model.inputs, outputs=layer_dict[layer_name].output)\n",
        "\n",
        "# Choose an image (0 to 59999) from the training set\n",
        "image = x_train[0] \n",
        "\n",
        "# Add the extra dimension expected by the slice\n",
        "image = np.expand_dims(image, axis=0)\n",
        "\n",
        "# Send the image through the model\n",
        "feature_maps = modelslice.predict(image)\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "    \n",
        "# We are assuming that we have 32 feature maps\n",
        "for i in range(32):\n",
        "    plt.subplot(4,8,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(feature_maps[0, :, :, i-1], cmap=plt.cm.binary)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ttijqvJQq01/fPIuh69UsPpb8PPT8on0+fzKPnGifc6a/ncq3WrlyZTQ9fvx4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\nlgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x576 with 32 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}