C3_W4_Lab_1_FashionMNIST_CAM.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of C3_W4_Lab_1_FashionMNIST-CAM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xerocopy/Advanced-Computer-Vision-with-TensorFlow/blob/main/C3_W4_Lab_1_FashionMNIST_CAM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhWiLK0IEsqX"
      },
      "source": [
        "# Ungraded Lab: Class Activation Maps with Fashion MNIST\n",
        "\n",
        "In this lab, you will see how to implement a simple class activation map (CAM) of a model trained on the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). This will show what parts of the image the model was paying attention to when deciding the class of the image. Let's begin!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOAlJAfRIY50"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSyMHuCVys-O"
      },
      "source": [
        "import keras\n",
        "from keras.datasets import fashion_mnist\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import Sequential,Model\n",
        "from keras.layers import Dense, Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
        "import scipy as sp"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMIJYr6FIbZt"
      },
      "source": [
        "## Download and Prepare the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01974419yy5W"
      },
      "source": [
        "# load the Fashion MNIST dataset\n",
        "(X_train,Y_train),(X_test,Y_test)  = fashion_mnist.load_data()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfVB6x6Oy1yF"
      },
      "source": [
        "# Put an additional axis for the channels of the image.\n",
        "# Fashion MNIST is grayscale so we place 1 at the end. Other datasets\n",
        "# will need 3 if it's in RGB.\n",
        "X_train = X_train.reshape(60000,28,28,1)\n",
        "X_test = X_test.reshape(10000,28,28,1)\n",
        "\n",
        "# Normalize the pixel values from 0 to 1\n",
        "X_train = X_train/255\n",
        "X_test  = X_test/255\n",
        "\n",
        "# Cast to float\n",
        "X_train = X_train.astype('float')\n",
        "X_test  = X_test.astype('float')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDx99oRCzfTr"
      },
      "source": [
        "def show_img(img):\n",
        "    '''utility function for reshaping and displaying an image'''\n",
        "\n",
        "    # convert to float array if img is not yet preprocessed\n",
        "    img  = np.array(img,dtype='float')\n",
        "\n",
        "    # remove channel dimension\n",
        "    img = img.reshape((28,28))\n",
        "\n",
        "    # display image\n",
        "    plt.imshow(img)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAL6lD1PLlj2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "ef5ce298-5e90-451d-c0d4-905c6699249d"
      },
      "source": [
        "# test the function for the first train image. you can vary the index of X_train\n",
        "# below to see other images\n",
        "\n",
        "show_img(X_train[1])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "1xPggGPyOhm5"
      },
      "source": [
        "## Build the Classifier\n",
        "\n",
        "Let's quickly recap how we can build a simple classifier with this dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ds9n85HmJySy"
      },
      "source": [
        "### Define the Model\n",
        "\n",
        "You can build the classifier with the model below. The image will go through 4 convolutions followed by pooling layers. The final Dense layer will output the probabilities for each class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyCoMd93zpc_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03c103f6-bb41-44bc-f346-b94b1819db12"
      },
      "source": [
        "# use the Sequential API\n",
        "model = Sequential()\n",
        "\n",
        "# notice the padding parameter to recover the lost border pixels when doing the convolution\n",
        "model.add(Conv2D(16,input_shape=(28,28,1),kernel_size=(3,3),activation='relu',padding='same'))\n",
        "# pooling layer with a stride of 2 will reduce the image dimensions by half\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "# pass through more convolutions with increasing filters\n",
        "model.add(Conv2D(32,kernel_size=(3,3),activation='relu',padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(64,kernel_size=(3,3),activation='relu',padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(128,kernel_size=(3,3),activation='relu',padding='same'))\n",
        "\n",
        "# use global average pooling to take into account lesser intensity pixels\n",
        "model.add(GlobalAveragePooling2D())\n",
        "\n",
        "# output class probabilities\n",
        "model.add(Dense(10,activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_4 (Conv2D)           (None, 28, 28, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 14, 14, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 14, 14, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 7, 7, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 7, 7, 64)          18496     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 3, 3, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " global_average_pooling2d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling2D)                                        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 98,442\n",
            "Trainable params: 98,442\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fxsip1WuOk0Q"
      },
      "source": [
        "### Train the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8LswgblLvc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bb570f5-c17d-4e6c-95b1-6ea1086cb69f"
      },
      "source": [
        "# configure the training\n",
        "model.compile(loss='sparse_categorical_crossentropy',metrics=['accuracy'],optimizer='adam')\n",
        "\n",
        "# train the model. just run a few epochs for this test run. you can adjust later.\n",
        "model.fit(X_train,Y_train,batch_size=32, epochs=5, validation_split=0.1, shuffle=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1688/1688 [==============================] - 64s 38ms/step - loss: 0.6312 - accuracy: 0.7669 - val_loss: 0.4920 - val_accuracy: 0.8207\n",
            "Epoch 2/5\n",
            "1688/1688 [==============================] - 55s 33ms/step - loss: 0.3805 - accuracy: 0.8599 - val_loss: 0.3407 - val_accuracy: 0.8750\n",
            "Epoch 3/5\n",
            "1688/1688 [==============================] - 54s 32ms/step - loss: 0.3171 - accuracy: 0.8851 - val_loss: 0.3121 - val_accuracy: 0.8875\n",
            "Epoch 4/5\n",
            "1688/1688 [==============================] - 54s 32ms/step - loss: 0.2829 - accuracy: 0.8961 - val_loss: 0.2832 - val_accuracy: 0.8942\n",
            "Epoch 5/5\n",
            "1688/1688 [==============================] - 54s 32ms/step - loss: 0.2558 - accuracy: 0.9072 - val_loss: 0.2653 - val_accuracy: 0.9020\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5970bddb50>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eriUpnzxQkus"
      },
      "source": [
        "## Generate the Class Activation Map\n",
        "\n",
        "To generate the class activation map, we want to get the features detected in the last convolution layer and see which ones are most active when generating the output probabilities. In our model above, we are interested in the layers shown below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UblFRpsK0Gd7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b82ee0f-6bc8-4be4-8f6a-ab3ac2b44e4b"
      },
      "source": [
        "# final convolution layer\n",
        "print(model.layers[-3].name)\n",
        "\n",
        "# global average pooling layer\n",
        "print(model.layers[-2].name)\n",
        "\n",
        "# output of the classifier\n",
        "print(model.layers[-1].name)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv2d_7\n",
            "global_average_pooling2d_1\n",
            "dense_1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXAYTZZVrZFB"
      },
      "source": [
        "You can now create your CAM model as shown below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSc42Fd4rZFB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b22c69f2-f20a-456d-84cc-b2614141fea9"
      },
      "source": [
        "# same as previous model but with an additional output\n",
        "cam_model  = Model(inputs=model.input,outputs=(model.layers[-3].output,model.layers[-1].output))\n",
        "cam_model.summary()\n",
        "\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_4_input (InputLayer)  [(None, 28, 28, 1)]      0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 28, 28, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 14, 14, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 14, 14, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 7, 7, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 7, 7, 64)          18496     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 3, 3, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " global_average_pooling2d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling2D)                                        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 98,442\n",
            "Trainable params: 98,442\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvWbe6sLrZFB"
      },
      "source": [
        "Use the CAM model to predict on the test set, so that it generates the features and the predicted probability for each class (`results`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8625UmNrxOU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dac2872c-3990-4e0b-f5fc-18fae92c67ab"
      },
      "source": [
        "# get the features and results of the test images using the newly created model\n",
        "features,results = cam_model.predict(X_test)\n",
        "\n",
        "# shape of the features\n",
        "print(\"features shape: \", features.shape)\n",
        "print(\"results shape\", results.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features shape:  (10000, 3, 3, 128)\n",
            "results shape (10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfQMCOtmTaDX"
      },
      "source": [
        "You can generate the CAM by getting the dot product of the class activation features and the class activation weights.\n",
        "\n",
        "You will need the weights from the Global Average Pooling layer (GAP) to calculate the activations of each feature given a particular class.\n",
        "- Note that you'll get the weights from the dense layer that follows the global average pooling layer.\n",
        "  - The last conv2D layer has (h,w,depth) of (3 x 3 x 128), so there are 128 features.\n",
        "  - The global average pooling layer collapses the h,w,f (3 x 3 x 128) into a dense layer of 128 neurons (1 neuron per feature).\n",
        "  - The activations from the global average pooling layer get passed to the last dense layer.\n",
        "  - The last dense layer assigns weights to each of those 128 features (for each of the 10 classes),\n",
        "  - So the weights of the last dense layer (which immmediately follows the global average pooling layer) are referred to in this context as the \"weights of the global average pooling layer\".\n",
        "\n",
        "For each of the 10 classes, there are 128 features, so there are 128 feature weights, one weight per feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwfG1vyprZFC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89402d1d-e47f-483b-a5da-0d6660407cf6"
      },
      "source": [
        "# these are the weights going into the softmax layer\n",
        "last_dense_layer = model.layers[-1]\n",
        "\n",
        "# get the weights list.  index 0 contains the weights, index 1 contains the biases\n",
        "gap_weights_l = last_dense_layer.get_weights()\n",
        "\n",
        "print(\"gap_weights_l index 0 contains weights \", gap_weights_l[0].shape)\n",
        "print(\"gap_weights_l index 1 contains biases \", gap_weights_l[1].shape)\n",
        "\n",
        "# shows the number of features per class, and the total number of classes\n",
        "# Store the weights\n",
        "gap_weights = gap_weights_l[0]\n",
        "\n",
        "print(f\"There are {gap_weights.shape[0]} feature weights and {gap_weights.shape[1]} classes.\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gap_weights_l index 0 contains weights  (128, 10)\n",
            "gap_weights_l index 1 contains biases  (10,)\n",
            "There are 128 feature weights and 10 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7GtR-4NrZFC"
      },
      "source": [
        "Now, get the features for a specific image, indexed between 0 and 999."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2aA6_ZTrZFD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2a27628-c6a4-45d4-83ba-31e3f4cf4224"
      },
      "source": [
        "# Get the features for the image at index 0\n",
        "idx = 0\n",
        "features_for_img = features[idx,:,:,:]\n",
        "\n",
        "print(f\"The features for image index {idx} has shape (height, width, num of feature channels) : \", features_for_img.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The features for image index 0 has shape (height, width, num of feature channels) :  (3, 3, 128)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OSiDiTnrZFD"
      },
      "source": [
        "The features have height and width of 3 by 3.  Scale them up to the original image height and width, which is 28 by 28."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sNUNDLDrZFD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9594c922-514a-42f0-eddb-5431dbc40d4d"
      },
      "source": [
        "features_for_img_scaled = sp.ndimage.zoom(features_for_img, (28/3, 28/3,1), order=2)\n",
        "\n",
        "# Check the shape after scaling up to 28 by 28 (still 128 feature channels)\n",
        "print(\"features_for_img_scaled up to 28 by 28 height and width:\", features_for_img_scaled.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features_for_img_scaled up to 28 by 28 height and width: (28, 28, 128)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYj5w9NhrZFD"
      },
      "source": [
        "For a particular class (0...9), get the 128 weights.\n",
        "\n",
        "Take the dot product with the scaled features for this selected image with the weights.\n",
        "\n",
        "The shapes are:\n",
        "scaled features: (h,w,depth) of (28 x 28 x 128).\n",
        "weights for one class: 128\n",
        "\n",
        "The dot product produces the class activation map, with the shape equal to the height and width of the image: 28 x 28."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNj4D8FprZFD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9ec736b-9c89-48d6-ee1b-9812b333f38e"
      },
      "source": [
        "# Select the weights that are used for a specific class (0...9)\n",
        "class_id = 0\n",
        "# take the dot product between the scaled image features and the weights for \n",
        "gap_weights_for_one_class = gap_weights[:,class_id]\n",
        "\n",
        "print(\"features_for_img_scaled has shape \", features_for_img_scaled.shape)\n",
        "print(\"gap_weights_for_one_class has shape \", gap_weights_for_one_class.shape)\n",
        "# take the dot product between the scaled features and the weights for one class\n",
        "cam = np.dot(features_for_img_scaled, gap_weights_for_one_class)\n",
        "\n",
        "print(\"class activation map shape \", cam.shape)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features_for_img_scaled has shape  (28, 28, 128)\n",
            "gap_weights_for_one_class has shape  (128,)\n",
            "class activation map shape  (28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vag5enPdrZFE"
      },
      "source": [
        "### Conceptual interpretation\n",
        "To think conceptually about what what you're doing and why:\n",
        "- In the 28 x 28 x 128 feature map, each of the 128 feature filters is tailored to look for a specific set of features (for example, a shoelace).  \n",
        "  - The actual features are learned, not selected by you directly.\n",
        "- Each of the 128 weights for a particular class decide how much weight to give to each of the 128 features, for that class.\n",
        "  - For instance, for the \"shoe\" class, it may have a higher weight for the feature filters that look for shoelaces.\n",
        "- At each of the 28 by 28 pixels, you can take the vector of 128 features and compare them with the vector of 128 weights.  \n",
        "  - You can do this comparison with a dot product.\n",
        "  - The dot product results in a scalar value at each pixel.\n",
        "  - Apply this dot product across all of the 28 x 28 pixels.\n",
        "  - The scalar result of the dot product will be larger when the image both has the particular feature (e.g. shoelace), and that feature is also weighted more heavily for the particular class (e.g shoe).\n",
        "  \n",
        "So you've created a matrix with the same number of pixels as the image, where the value at each pixel is higher when that pixel is relevant to the prediction of a particular class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-e9U5poVBis"
      },
      "source": [
        "Here is the function that implements the Class activation map calculations that you just saw."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YByJ8J1008Ms"
      },
      "source": [
        "def show_cam(image_index):\n",
        "  '''displays the class activation map of a particular image'''\n",
        "\n",
        "  # takes the features of the chosen image\n",
        "  features_for_img = features[image_index,:,:,:]\n",
        "\n",
        "  # get the class with the highest output probability\n",
        "  prediction = np.argmax(results[image_index])\n",
        "\n",
        "  # get the gap weights at the predicted class\n",
        "  class_activation_weights = gap_weights[:,prediction]\n",
        "\n",
        "  # upsample the features to the image's original size (28 x 28)\n",
        "  class_activation_features = sp.ndimage.zoom(features_for_img, (28/3, 28/3, 1), order=2)\n",
        "\n",
        "  # compute the intensity of each feature in the CAM\n",
        "  cam_output  = np.dot(class_activation_features,class_activation_weights)\n",
        "  \n",
        "  print('Predicted Class = ' +str(prediction)+ ', Probability = ' + str(results[image_index][prediction]))\n",
        "  \n",
        "  # show the upsampled image\n",
        "  plt.imshow(np.squeeze(X_test[image_index],-1), alpha=0.5)\n",
        "  \n",
        "  # strongly classified (95% probability) images will be in green, else red\n",
        "  if results[image_index][prediction]>0.95:\n",
        "    cmap_str = 'Greens'\n",
        "  else:\n",
        "    cmap_str = 'Reds'\n",
        "\n",
        "  # overlay the cam output\n",
        "  plt.imshow(cam_output, cmap=cmap_str, alpha=0.5)\n",
        "\n",
        "  # display the image\n",
        "  plt.show()"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fjIdEmlXbez"
      },
      "source": [
        "You can now test generating class activation maps. Let's use the utility function below. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iQjMfg9U03_"
      },
      "source": [
        "def show_maps(desired_class, num_maps):\n",
        "    '''\n",
        "    goes through the first 10,000 test images and generates CAMs \n",
        "    for the first `num_maps`(int) of the `desired_class`(int)\n",
        "    '''\n",
        "\n",
        "    counter = 0\n",
        "\n",
        "    if desired_class < 10:\n",
        "        print(\"please choose a class less than 10\")\n",
        "\n",
        "    # go through the first 10000 images\n",
        "    for i in range(0,10000):\n",
        "        # break if we already displayed the specified number of maps\n",
        "        if counter == num_maps:\n",
        "            break\n",
        "\n",
        "        # images that match the class will be shown\n",
        "        if np.argmax(results[i]) == desired_class:\n",
        "            counter += 1\n",
        "            show_cam(i)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctjDEfzrjXa5"
      },
      "source": [
        "For class 8 (handbag), you'll notice that most of the images have dark spots in the middle and right side. \n",
        "- This means that these areas were given less importance when categorizing the image. \n",
        "- The other parts such as the outline or handle contribute more when deciding if an image is a handbag or not. \n",
        "\n",
        "Observe the other classes and see if there are also other common areas that the model uses more in determining the class of the image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkPMvVurezkb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "79d6c24c-df3a-461c-ddcb-070ce53285e4"
      },
      "source": [
        "show_maps(desired_class=7, num_maps=20)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "please choose a class less than 10\n",
            "Predicted Class = 7, Probability = 0.99990344\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.99918383\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.99996495\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9994899\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.876428\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.99963677\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9468437\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.7940948\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9995158\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.99712\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9999509\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9995912\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.71715254\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9999293\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.98376024\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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b0N4N4quuE39F31O0FLTXNxBPgR0+eHNDnH57sKxxuO2yM7poWjKD0TE4V3kRVs2JvgA/GhukvUG/tLzHj75vr9lLUgfJ2T8J4AsAXiR5vLjtEfSL/EckHwTwOoD7BuhLRCoSFruZ/Rzl/5d8erTDEZFx0eWyIplQsYtkQsUukgkVu0gmVOwimZjsFFfEuazHy8rTc/boeCdnD3LyMEcPnpMwy3baw8w2+O8+mj4bLuHtd+9K3k7aydnrwTTSuN3P2aNpql57+DNz28vbdGYXyYSKXSQTKnaRTKjYRTKhYhfJhIpdJBMqdpFMTHbLZgTbCydsTRxl0dF89Sj+93L4lBwcACxaKjpod4+Nku6oOfF5SwraA1Ea7eXw0Vx4L6Pv9x0cH/XvjD567OFSdp3ZRbKhYhfJhIpdJBMqdpFMqNhFMqFiF8mEil0kExPO2Q2GhHXjE9YgD/tOyJNT56NHc+1TtmQOpcXwyc+7L1o/PVpX3pnbHRxbS8zhmTDXvpY0n72czuwimVCxi2RCxS6SCRW7SCZU7CKZULGLZELFLpKJQfZn3wfg+wB2ox+7HjSzb5N8FMDfAnijuOsjZvZ01J8XGcdzr52cPXXt9eCxveNTxl10EBw+viw72n89nrAeHD7GHD5Oo4ed+T1ATp702EF7wvUD3sgGuaimA+BrZvY8yUUAz5E8WrR9y8z+aYA+RKRig+zPfhbA2eLzFZKvANg77oGJyGi9r7/ZSd4M4BMAflHc9BDJF0geIrmz5JgDJJdJLq++vZo0WBEZ3sDFTnIBwI8BfNXM3gbwHQAfA7Af/TP/N7Y6zswOmtmSmS0tbFsYwZBFZBgDFTvJJvqF/gMz+wkAmNl5M+uaWQ/AdwHcPr5hikiqsNjZf1vycQCvmNk3N92+Z9PdPgfgxOiHJyKjMsi78Z8E8AUAL5I8Xtz2CID7Se5HPzg6BeBLUUcGP4oZ56rE8TLVwx+fmKwl8x4/2vY47DvhsadZ4tMyQA/DR3dJsZ1jkHfjf46txxZm6iIyPXQFnUgmVOwimVCxi2RCxS6SCRW7SCZU7CKZmPiWzeMy3iWNE03x0KbZsHnypg6ctvFk2YM89KD3GDWd2UUyoWIXyYSKXSQTKnaRTKjYRTKhYhfJhIpdJBMc63bA1z4Y+QaA1zfddD2ANyc2gPdnWsc2reMCNLZhjXJsN5nZDVs1TLTY3/Pg5LKZLVU2AMe0jm1axwVobMOa1Nj0a7xIJlTsIpmoutgPVvz4nmkd27SOC9DYhjWRsVX6N7uITE7VZ3YRmRAVu0gmKil2kneR/BXJV0k+XMUYypA8RfJFksdJLlc8lkMkL5A8sem2XSSPkjxZfNxyj72KxvYoyTPFc3ec5N0VjW0fyZ+RfJnkSyS/Utxe6XPnjGsiz9vE/2YnWQfwawB/BeA0gGcB3G9mL090ICVIngKwZGaVX4BB8i8BrAL4vpn9cXHbPwK4aGaPFf9R7jSzv5uSsT0KYLXqbbyL3Yr2bN5mHMC9AL6ICp87Z1z3YQLPWxVn9tsBvGpmr5nZOoAfAringnFMPTN7BsDFa26+B8Dh4vPD6L9YJq5kbFPBzM6a2fPF5ysA3t1mvNLnzhnXRFRR7HsB/GbT16cxXfu9G4CfknyO5IGqB7OF3WZ2tvj8HIDdVQ5mC+E23pN0zTbjU/PcDbP9eSq9Qfded5jZnwH4LIAvF7+uTiXr/w02TdnpQNt4T8oW24z/TpXP3bDbn6eqotjPANi36euPFLdNBTM7U3y8AOBJTN9W1Off3UG3+Hih4vH8zjRt473VNuOYgueuyu3Pqyj2ZwHcSvIWki0AnwdwpIJxvAfJ+eKNE5CcB/AZTN9W1EcAPFB8/gCApyocy++Zlm28y7YZR8XPXeXbn5vZxP8BuBv9d+T/F8DfVzGGknH9AYBfFv9eqnpsAJ5A/9e6DfTf23gQwHUAjgE4CeC/AeyaorH9G4AXAbyAfmHtqWhsd6D/K/oLAI4X/+6u+rlzxjWR502Xy4pkQm/QiWRCxS6SCRW7SCZU7CKZULGLZELFLpIJFbtIJv4f3th/o3VGdZ4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.99975425\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9993517\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9302486\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9998977\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 7, Probability = 0.9996092\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}