Visualization_of_ConvNets.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iACL64AqP572"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/drive/1tCndf1ME08W7K5cs6iWAKkg35l5wYyKE?usp=sharing\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6gHiH-I7uFa"
      },
      "source": [
        "# Visualizing the effect of ConvNets:\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qnCNAG-VecJ9"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Load the Fashion MNIST dataset\n",
        "fmnist = tf.keras.datasets.fashion_mnist\n",
        "(training_images, training_labels), (test_images, test_labels) = fmnist.load_data()\n",
        "\n",
        "# Normalize the pixel values\n",
        "training_images = training_images / 255.0\n",
        "test_images = test_images / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xcsRtq9OLorS"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# Define the model\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "\n",
        "# Setup training parameters\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "print(f'\\nMODEL TRAINING:')\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "\n",
        "# Evaluate on the test set\n",
        "print(f'\\nMODEL EVALUATION:')\n",
        "test_loss = model.evaluate(test_images, test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C0tFgT1MMKi6"
      },
      "outputs": [],
      "source": [
        "# Define the model\n",
        "model = tf.keras.models.Sequential([\n",
        "                                                         \n",
        "  # Add convolutions and max pooling\n",
        "  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),\n",
        "  tf.keras.layers.MaxPooling2D(2, 2),\n",
        "  tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "  # Add the same layers as before\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "# Print the model summary\n",
        "model.summary()\n",
        "\n",
        "# Use same settings\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "print(f'\\nMODEL TRAINING:')\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "\n",
        "# Evaluate on the test set\n",
        "print(f'\\nMODEL EVALUATION:')\n",
        "test_loss = model.evaluate(test_images, test_labels)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXx_LX3SAlFs"
      },
      "source": [
        "# Visualizing the Convolutions and Pooling\n",
        "\n",
        "Let's explore how to show the convolutions graphically. The cell below prints the first 100 labels in the test set, and you can see that the ones at index `0`, index `23` and index `28` are all the same value (i.e. `9`). They're all shoes. Let's take a look at the result of running the convolution on each, and you'll begin to see common features between them emerge. Now, when the dense layer is training on that data, it's working with a lot less, and it's perhaps finding a commonality between shoes based on this convolution/pooling combination."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f-6nX4QsOku6"
      },
      "outputs": [],
      "source": [
        "print(test_labels[:100])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Raw Images from the DataSet "
      ],
      "metadata": {
        "id": "Mgsbzfq6Td9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.imshow(test_images[0])\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(test_images[23])\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(test_images[28])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "OJfQukKaTX6h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9FGsHhv6JvDx"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import models\n",
        "\n",
        "f, axarr = plt.subplots(3,4)\n",
        "\n",
        "FIRST_IMAGE=0\n",
        "SECOND_IMAGE=23\n",
        "THIRD_IMAGE=28\n",
        "CONVOLUTION_NUMBER = 1\n",
        "\n",
        "layer_outputs = [layer.output for layer in model.layers]\n",
        "activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs)\n",
        "\n",
        "for x in range(0,4):\n",
        "  f1 = activation_model.predict(test_images[FIRST_IMAGE].reshape(1, 28, 28, 1))[x]\n",
        "  axarr[0,x].imshow(f1[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
        "  axarr[0,x].grid(False)\n",
        "  \n",
        "  f2 = activation_model.predict(test_images[SECOND_IMAGE].reshape(1, 28, 28, 1))[x]\n",
        "  axarr[1,x].imshow(f2[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
        "  axarr[1,x].grid(False)\n",
        "  \n",
        "  f3 = activation_model.predict(test_images[THIRD_IMAGE].reshape(1, 28, 28, 1))[x]\n",
        "  axarr[2,x].imshow(f3[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
        "  axarr[2,x].grid(False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F_1N2nbCV6P1"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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