C3_W4_Lab_1_FashionMNIST_CAM.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/souvikg544/AdvancedComputerVision/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": "code",
      "source": [
        "# Install this package to use Colab's GPU for training\n",
        "!apt install --allow-change-held-packages libcudnn8=8.4.1.50-1+cuda11.6"
      ],
      "metadata": {
        "id": "iXnj2Mbh0DFa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83561a83-d298-46d9-cbe0-9f5afc1aeecd"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following packages will be REMOVED:\n",
            "  libcudnn8-dev\n",
            "The following held packages will be changed:\n",
            "  libcudnn8\n",
            "The following packages will be upgraded:\n",
            "  libcudnn8\n",
            "1 upgraded, 0 newly installed, 1 to remove and 30 not upgraded.\n",
            "Need to get 420 MB of archives.\n",
            "After this operation, 3,369 MB disk space will be freed.\n",
            "Get:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  libcudnn8 8.4.1.50-1+cuda11.6 [420 MB]\n",
            "Fetched 420 MB in 7s (61.4 MB/s)\n",
            "(Reading database ... 155685 files and directories currently installed.)\n",
            "Removing libcudnn8-dev (8.0.5.39-1+cuda11.1) ...\n",
            "(Reading database ... 155663 files and directories currently installed.)\n",
            "Preparing to unpack .../libcudnn8_8.4.1.50-1+cuda11.6_amd64.deb ...\n",
            "Unpacking libcudnn8 (8.4.1.50-1+cuda11.6) over (8.0.5.39-1+cuda11.1) ...\n",
            "Setting up libcudnn8 (8.4.1.50-1+cuda11.6) ...\n"
          ]
        }
      ]
    },
    {
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMIJYr6FIbZt"
      },
      "source": [
        "## Download and Prepare the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01974419yy5W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0941c1aa-4f2c-4282-9d61-e4401c8adef0"
      },
      "source": [
        "# load the Fashion MNIST dataset\n",
        "(X_train,Y_train),(X_test,Y_test)  = fashion_mnist.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 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",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 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",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "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": 4,
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAL6lD1PLlj2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a5281c07-5e5e-4745-b27b-0b09f58f3c1f"
      },
      "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[60])"
      ],
      "execution_count": 7,
      "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": "dfc073ca-0e8d-4779-89ae-02eac64b0df5"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 7, 7, 64)          18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 3, 3, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 128)              0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense (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": "d6b1b7e1-a41a-467f-c9ac-9a64d57c0839"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1688/1688 [==============================] - 12s 5ms/step - loss: 0.6182 - accuracy: 0.7709 - val_loss: 0.4196 - val_accuracy: 0.8508\n",
            "Epoch 2/5\n",
            "1688/1688 [==============================] - 10s 6ms/step - loss: 0.3767 - accuracy: 0.8625 - val_loss: 0.3347 - val_accuracy: 0.8768\n",
            "Epoch 3/5\n",
            "1688/1688 [==============================] - 8s 5ms/step - loss: 0.3178 - accuracy: 0.8842 - val_loss: 0.3185 - val_accuracy: 0.8830\n",
            "Epoch 4/5\n",
            "1688/1688 [==============================] - 7s 4ms/step - loss: 0.2818 - accuracy: 0.8961 - val_loss: 0.2792 - val_accuracy: 0.8972\n",
            "Epoch 5/5\n",
            "1688/1688 [==============================] - 8s 5ms/step - loss: 0.2554 - accuracy: 0.9058 - val_loss: 0.2551 - val_accuracy: 0.9062\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efc105d68d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "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": "d3fb1696-fd55-44f9-fced-5256746283fe"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv2d_3\n",
            "global_average_pooling2d\n",
            "dense\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": "a556477a-ebba-46fd-c6ad-4aa7ce55b48b"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_input (InputLayer)   [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 28, 28, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 7, 7, 64)          18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 3, 3, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 128)              0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense (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": "96e54fc9-cb12-4a77-dac8-a811e6024915"
      },
      "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": 12,
      "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": "d3472f7f-423f-4321-a77a-16efb38a95bd"
      },
      "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": 16,
      "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": "d1a5bfa8-1a22-4592-eb5f-c9eeb7085f8e"
      },
      "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": 14,
      "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": "52a7e170-1977-479a-bc9b-5853f1e723a5"
      },
      "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": 15,
      "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": "93ac318d-28ca-4446-e9d6-71947c5e3898"
      },
      "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": 17,
      "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": 18,
      "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": 19,
      "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": "19e54d8a-919c-4e5c-c17c-ba0b5d72c614"
      },
      "source": [
        "show_maps(desired_class=8, num_maps=20)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "please choose a class less than 10\n",
            "Predicted Class = 8, Probability = 0.9964031\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 = 8, Probability = 0.9999993\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 = 8, Probability = 0.99987483\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 = 8, Probability = 0.9998791\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 = 8, Probability = 0.97590977\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 = 8, Probability = 0.99991083\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 = 8, Probability = 0.99901533\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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m5w3P7CQPkPwFyZdIvkjyq8Ptu0k+SfKV4e9d0VxCiOmxkY/xPQBfN7MbAfw5gK+QvBHAfQCeMrODAJ4a/i2EmFFCsZvZaTN7bnj7AoCXAVwD4A4AR4d3Owrgzs1apBCiPh/qCzqS1wH4FIBfAdhnZqeHobcA7EuMOUxykeTi0vmlGksVQtRhw2InuRXATwF8zczOr42ZmSHx/ZqZHTGzBTNb2LF9R63FCiFGZ0NiJ9nEQOg/MrOfDTe/TXL/ML4fwJnNWaIQYhyE1hsHtWkfAvCymX1rTehxAIcAPDj8/Vg8V4FW4aS4BuabF+9X/kN55cWPu/Glq9wwLpfL6Vi1xx17adm3zi53V/143x/f6aetu15QxrofWGtWp180gNIx50rHlgOAVpAC2w5KUc85paa3BGWo20Ep6VYZxf3z6JnzaR3M/ybdHhwAWKbbPXvW20Z89k8D+BKAF0geG267HwOR/4TkPQBeB3DXBuYSQkyJUOxm9kukr534zHiXI4TYLHS5rBCZILELkQkSuxCZILELkQkSuxCZMPEU10ZQFtnD89lL+imqvRU/jXTn66N72SuBlz3f8/e96qSoDuL++K7ns5s/dz9Ica2LV0q6dDxhAGgEdaxbvg2PdiN9h3aQ0tz0ek3Db5MNAKWTjg0ABdPXVhROGvgApzy3SkkLISR2ITJBYhciEyR2ITJBYhciEyR2ITJBYhciEybsswMlA3PUwfPZPX8R8EsaAwCjftEOUQnscG2B31wW/jHrlWmfvV8FPnvQsjnMZw+g67P7j6sZPO5mkFPedK7piPLRm4XvdTeCtUfPGZ3zrHfMBmMdn90ZqzO7EJkgsQuRCRK7EJkgsQuRCRK7EJkgsQuRCRK7EJkwUZ8dJArXnxy9bnzUPDjyuiOM6X1XQd51Xa/aqwUO+J5vv/B99mqGffYi8KobQc54y6kr34p89MKfO85X958zL17ntap8diGExC5ELkjsQmSCxC5EJkjsQmSCxC5EJkjsQmTCRvqzHwDwQwD7MDDCj5jZd0g+AOBvAbwzvOv9ZvbEBuZLB4Occno+u+ODA0BUHb1AlJ+cnqG0wA8OPNuoL33kunY9b9X89/MqurZhE332Inhksdcd5cOnc9bLmnOH1whMyWf32MhFNT0AXzez50huA/AsySeHsW+b2T9uysqEEGNlI/3ZTwM4Pbx9geTLAK7Z7IUJIcbLh/qfneR1AD4F4FfDTfeSfJ7kwyR3JcYcJrlIcvHcuXdrLVYIMTobFjvJrQB+CuBrZnYewPcA3ADgJgzO/N9cb5yZHTGzBTNb2Llz3fcDIcQE2JDYSTYxEPqPzOxnAGBmb5tZ38wqAN8HcPPmLVMIUZdQ7Bx8nfoQgJfN7Ftrtu9fc7fPAzg+/uUJIcbFRr6N/zSALwF4geSx4bb7AdxN8iYM7LgTAL4cTUQEtkLgOLgJroFDFJZzDuKF874Y2TBV0E66Cmwai+Z3UmzD4xJYaxaYllHZY+9JjVo2R/ZVVK7ZG+89n0Bc3jt63NHa65SS9kmP3ci38b9MzBB66kKI2UFX0AmRCRK7EJkgsQuRCRK7EJkgsQuRCRK7EJkw2VLS2Lz0vagMdbjX4A711h21bI52Hni6TmqwhdcXBAR+cTh8k8oiD+LxDMlIzee77uu4lpU+IjqzC5EJErsQmSCxC5EJErsQmSCxC5EJErsQmSCxC5EJrFsq+EPtjHwHwOtrNu0B8LuJLeDDMatrm9V1AVrbqIxzbdea2VXrBSYq9g/snFw0s4WpLcBhVtc2q+sCtLZRmdTa9DFeiEyQ2IXIhGmL/ciU9+8xq2ub1XUBWtuoTGRtU/2fXQgxOaZ9ZhdCTAiJXYhMmIrYSd5G8tckXyV53zTWkILkCZIvkDxGcnHKa3mY5BmSx9ds203ySZKvDH9PpadWYm0PkDw1PHbHSN4+pbUdIPkLki+RfJHkV4fbp3rsnHVN5LhN/H92kiWA/wHwVwBOAngGwN1m9tJEF5KA5AkAC2Y29QswSP4FgIsAfmhmfzLc9g8AzprZg8M3yl1m9nczsrYHAFycdhvvYbei/WvbjAO4E8DfYIrHzlnXXZjAcZvGmf1mAK+a2Wtm1gHwYwB3TGEdM4+ZPQ3g7Ps23wHg6PD2UQxeLBMnsbaZwMxOm9lzw9sXALzXZnyqx85Z10SYhtivAfDGmr9PYrb6vRuAn5N8luThaS9mHfaZ2enh7bcA7JvmYtYhbOM9Sd7XZnxmjt0o7c/roi/oPsgtZvanAD4H4CvDj6sziQ3+B5sl73RDbbwnxTptxn/PNI/dqO3P6zINsZ8CcGDN3x8bbpsJzOzU8PcZAI9i9lpRv/1eB93h7zNTXs/vmaU23uu1GccMHLtptj+fhtifAXCQ5PUkWwC+CODxKazjA5C8YvjFCUheAeCzmL1W1I8DODS8fQjAY1Ncyx8wK228U23GMeVjN/X252Y28R8At2PwjfxvAPz9NNaQWNcnAPzX8OfFaa8NwCMYfKzrYvDdxj0ArgTwFIBXAPwngN0ztLZ/BfACgOcxENb+Ka3tFgw+oj8P4Njw5/ZpHztnXRM5brpcVohM0Bd0QmSCxC5EJkjsQmSCxC5EJkjsQmSCxC5EJkjsQmTC/wEDWXC24gZS6AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 8, Probability = 0.9998235\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 = 8, Probability = 0.99999964\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 = 8, Probability = 0.9995908\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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+ruTLQc8FPju7XmQ7Hvns6dfEJQQ961ktfBef3cyuNbMfmdkTZva4mX1quP2ImT1gZk8Nfx+O9iWEmB57+RhfA/iMu98A4M8AfNLMbgDwWQAPuvt1AB4c/i2EmFFCsbv7aXf/2fD2eQBPArgGwK0A7h3e7V4AH96vJIUQ3bmsL+jM7G0A3gPgJwCOuvvpYegFAEcTY46b2ZqZra2f49dpCyH2jz2L3cwOAvgugE+7+7mdMd+uItn1mxp3P+Huq+6+urLMv4ATQuwfexK7mfWwLfRvuvv3hpvPmNmxYfwYgLP7k6IQYhyE1puZGYCvAXjS3b+4I3QfgDsA3D38/YM97As9VhoYlA0uk1bS65FdUfD3tWCVXFSk7LAK7KkFstQ0AGxU3P7aCJZF3qzT+++36bJgACDVsXvCiIUEAL0inft8GZW48tfDYsUf24Eqba9FY3tFEA+sOQuXfE4/9sXg9VI5m5f0fO/FZ38vgI8BeMzMHhlu+xy2Rf4dM/s4gGcB3LaHfQkhpkQodnf/MdJvFx8YbzpCiP1Cl8sKkQkSuxCZILELkQkSuxCZILELkQlTWLI57T9uNtzbPEeWXR7sfgHf6xws+dLEBbhvykpgyzbw8IMlmd35ksysjTUALJPS3znw9twWzFuEBSWyFbm+oQxONb3gDvNVECfjFyru8S8GNdG9wAuP2kGz8+xy4OEvP59+vZT9dF46swuRCRK7EJkgsQuRCRK7EJkgsQuRCRK7EJkgsQuRCRP12X2uRP3WdBPallvheOlCuj65Kbiv+VL7JhofgLdc7pOWzJsNX9Z4s+Ved7/p03jd5z78rxfSce8HraK3Aj84qPMvgnp2Fu8FLbQX2mA5aON+9CKJLwRjez1+zUcVSKcKzqNFm57YU33+WjR2XUYrn12I7JHYhcgEiV2ITJDYhcgEiV2ITJDYhcgEiV2ITJioz14MWiz+Kt37HQtBj/MeqSkfcEO45SXhKInvuR1Px4qgnr3q8/ig4ceuB9wLX3gx7eM3QS28B8tNR1jQcJ/1R68K7qNXBX95RjXlrGQ9aFmPYiuqV+d94S24hoD122fLLr92j8uP6MwuRDZI7EJkgsQuRCZI7EJkgsQuRCZI7EJkgsQuRCbsZX32awF8A8BRAA7ghLt/2czuAvDXAF4c3vVz7n4/3ZmDLwh+kXjwAArS4zz0i5171WXQu70l9exVy+vR26Be3dugbzypUQYAlOmnsXTu93owLxGRJ1ywvvGBFx357HE8vf8iOHaUW1zHz+eFzVt07UKw4yR7uaimBvAZd/+ZmR0C8FMze2AY+5K7//3omQkhJsVe1mc/DeD08PZ5M3sSwDX7nZgQYrxc1v/sZvY2AO8B8JPhpjvN7FEzu8fMdu03ZWbHzWzNzNZeXf91p2SFEKOzZ7Gb2UEA3wXwaXc/B+ArAN4B4EZsn/m/sNs4dz/h7qvuvnp45YoxpCyEGIU9id3MetgW+jfd/XsA4O5n3L3x7W94vgrgpv1LUwjRlVDstv3V4NcAPOnuX9yx/diOu30EwMnxpyeEGBd7+Tb+vQA+BuAxM3tkuO1zAG43sxuxbag9A+ATezlgXL63P0R2RpQXG9+lnTIAlEG8DWwgEEsyWpLZu9g82IP1Rh5bV3srmjcj57Lw+e5QZrqneKdpZ4PTsb18G//jxB64py6EmCl0BZ0QmSCxC5EJErsQmSCxC5EJErsQmSCxC5EJE20lHdOltI/7yUG4k+8aZR2VO0Z7iIabs9yiVtHR+30wr5385sCj7+hl8yN3vL6g4+Ui07jeRGd2ITJBYhciEyR2ITJBYhciEyR2ITJBYhciEyR2ITLBui7Ze1kHM3sRwLM7Nl0J4KWJJXB5zGpus5oXoNxGZZy5vdXd37xbYKJi/62Dm625++rUEiDMam6zmheg3EZlUrnpY7wQmSCxC5EJ0xb7iSkfnzGruc1qXoByG5WJ5DbV/9mFEJNj2md2IcSEkNiFyISpiN3Mbjaz/zazX5jZZ6eRQwoze8bMHjOzR8xsbcq53GNmZ83s5I5tR8zsATN7avh71zX2ppTbXWZ2ajh3j5jZLVPK7Voz+5GZPWFmj5vZp4bbpzp3JK+JzNvE/2c3sxLA/wD4SwDPA3gYwO3u/sREE0lgZs8AWHX3qV+AYWZ/DuACgG+4+7uH2/4OwCvufvfwjfKwu//NjOR2F4AL017Ge7ha0bGdy4wD+DCAv8IU547kdRsmMG/TOLPfBOAX7v60u/cBfBvArVPIY+Zx94cAvPKGzbcCuHd4+15sv1gmTiK3mcDdT7v7z4a3zwN4bZnxqc4dyWsiTEPs1wB4bsffz2O21nt3AD80s5+a2fFpJ7MLR9399PD2CwCOTjOZXQiX8Z4kb1hmfGbmbpTlz7uiL+h+m/e5+58C+BCATw4/rs4kvv0/2Cx5p3taxntS7LLM+OtMc+5GXf68K9MQ+ykA1+74+y3DbTOBu58a/j4L4PuYvaWoz7y2gu7w99kp5/M6s7SM927LjGMG5m6ay59PQ+wPA7jOzN5uZnMAPgrgvink8VuY2dLwixOY2RKAD2L2lqK+D8Adw9t3APjBFHP5DWZlGe/UMuOY8txNfflzd5/4D4BbsP2N/P8C+Ntp5JDI6w8A/Hz48/i0cwPwLWx/rBtg+7uNjwN4E4AHATwF4D8BHJmh3P4VwGMAHsW2sI5NKbf3Yfsj+qMAHhn+3DLtuSN5TWTedLmsEJmgL+iEyASJXYhMkNiFyASJXYhMkNiFyASJXYhMkNiFyIT/A6kUZX3E2+OzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 8, Probability = 0.9999397\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 = 8, Probability = 0.9795395\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 = 8, Probability = 0.9732521\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 = 8, Probability = 0.99661475\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 = 8, Probability = 0.9931676\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 = 8, Probability = 0.9999987\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 = 8, Probability = 0.9965305\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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PAG8/+lL6uJYbm3zfJMdfkDp6XdlFMqFgF8mEgl0kEwp2kUwo2EUyoWAXyYSCXSQT082zm6FbpnOEPOMLNKQ+ecuuoH3LguculyueZ98k+eQ+eP2xG79/AEEt/kaHHxmr0v37QTl6r+anQFTP3gTXi4bk0peDqdeXgpryqsNz2YukvdvhcyeUBd92hy0HDaCK1ikg50wR1MqzOelb1bOb2bVm9kMz+6WZPWlmnxw9frmZPWRmT4++Hou2JSKzs59f4wcAPu3uNwP4SwAfN7ObAXwGwMPufhOAh0f/F5E5FQa7u59x95+Nvl8H8BSAqwHcAeD+0dPuB/DBgxqkiLT3pj6gM7PrAbwLwI8BHHf3M6OmlwAcT/Q5aWZrZrZ24UJ6DjkROVj7DnYzOwzgOwA+5e4Xd7e5uyPx+Zq7n3L3VXdfXVlZaTVYERnfvoLdzCoMA/0b7v7d0cMvm9mJUfsJAGcPZogiMglh6s3MDMBXATzl7l/Y1fQAgLsB3Dv6+v1wWzBUUVqBuO7q15JtdZC3u1Qfpu0bNR9Xp0wvF71Q8dRaEaT9enQJXmAxSM0d7qZLbKPU21bN990EizaXQQVtaWTJ5iA91S35dMyLHf6eLZDUW1Wmp3IetvN9l0GJazco/S166VQvK2EdPoGk3kjbfvLs7wZwF4DHzezR0WOfxTDIv21mHwXwHIAP72NbIjIjYbC7+4+QXuH9fZMdjogcFN0uK5IJBbtIJhTsIplQsItkQsEukokpl7gW6JIcogdFrpea9B14R4t0HhwA+sHSwnUwZTLt7bw8doHkmgFgJchVLwdTUYMsyzxogqWs+ZbDElZWUgkABXntZRGU1wbtC6Tkedg/nSvvlHzbbMpzIL734eLgKG3vFOl7M4roGnxQJa4i8sdBwS6SCQW7SCYU7CKZULCLZELBLpIJBbtIJqa8ZHOUW+V59uu655NtwazE6DY8F355h9ekN6T/FSXvuzHgo/vTYBrrp3o8Z7veT//M5lsGPFiyOcLqpwGgJNeT2oNa+oafng14Lrzv6Xs6KtIGANvGt70+4Dn+Q+f52DuWPu7RMeWUZxfJnoJdJBMKdpFMKNhFMqFgF8mEgl0kEwp2kUxMNc8OWFyrSzzz8z1XmAIADI7wvOfWFTzjXFTpudcBYLtO1y83zvPsvQHfd/Mqz3UXp9Pz5QPAIqmnb5zXXXtQjx6JerPp0+M55/lx6QQ15VWRPi5VMQi2TZtRBktZF8FaAEbq/MM8OzksrKeu7CKZULCLZELBLpIJBbtIJhTsIplQsItkQsEukon9rM9+LYCvAziOYYbvlLt/yczuAfB3AF4ZPfWz7v4g3RZa1ur203nT8lU+b/xykMtugrnfyyadlx04n9e9Q/oCwCCota+DOcwLT//MjurVo7n6I9G88ez9LoNcdNQere/O2tvuO8yjt5hPP8Q2Tdr2c1PNAMCn3f1nZnYEwE/N7KFR2xfd/Z/2PUgRmZn9rM9+BsCZ0ffrZvYUgKsPemAiMllv6ncJM7sewLsA/Hj00CfM7DEzu8/MjiX6nDSzNTNbe+1CelopETlY+w52MzsM4DsAPuXuFwF8GcCNAG7B8Mr/+b36ufspd19199VjK5dNYMgiMo59BbuZVRgG+jfc/bsA4O4vu3vt7g2ArwC49eCGKSJthcFuw49TvwrgKXf/wq7HT+x62ocAPDH54YnIpOzn0/h3A7gLwONm9ujosc8CuNPMbsEwHfcsgI/tZ4dRSoJhSaI2ZYHAsPiWYamSIpgSuQhKNcsiGByvpqRHtAlKMVvOJM1WDwbAlx+O0ldlsMx21L8gE4xHqa8wpdgi5RhpEyPsbNjPp/E/SmyB5tRFZL7oDjqRTCjYRTKhYBfJhIJdJBMKdpFMKNhFMjHlqaQBnhXmSd8o88lEpZxRWtScPCHsG7S3zNmy/kbKX0dP4O2BNmNv87qG7YEW6ep2yybvJ1d+MLl0Rld2kUwo2EUyoWAXyYSCXSQTCnaRTCjYRTKhYBfJhEVTDU90Z2avAHhu10NXAvjt1Abw5szr2OZ1XIDGNq5Jju06d3/LXg1TDfY/2LnZmruvzmwAxLyObV7HBWhs45rW2PRrvEgmFOwimZh1sJ+a8f6ZeR3bvI4L0NjGNZWxzfRvdhGZnllf2UVkShTsIpmYSbCb2W1m9j9m9msz+8wsxpBiZs+a2eNm9qiZrc14LPeZ2Vkze2LXY5eb2UNm9vTo655r7M1obPeY2enRsXvUzG6f0diuNbMfmtkvzexJM/vk6PGZHjsyrqkct6n/zW5mJYBfAfgbAC8C+AmAO939l1MdSIKZPQtg1d1nfgOGmf0VgA0AX3f3Px899o8Azrn7vaMflMfc/e/nZGz3ANiY9TLeo9WKTuxeZhzABwH8LWZ47Mi4PowpHLdZXNlvBfBrd3/G3XcAfAvAHTMYx9xz90cAnHvDw3cAuH/0/f0YnixTlxjbXHD3M+7+s9H36wB+t8z4TI8dGddUzCLYrwbwwq7/v4j5Wu/dAfzAzH5qZidnPZg9HHf3M6PvXwJwfJaD2UO4jPc0vWGZ8bk5duMsf96WPqD7Q+9x978A8AEAHx/9ujqXfPg32DzlTve1jPe07LHM+OtmeezGXf68rVkE+2kA1+76/zWjx+aCu58efT0L4HuYv6WoX/7dCrqjr2dnPJ7XzdMy3nstM445OHazXP58FsH+EwA3mdkNZtYF8BEAD8xgHH/AzJZHH5zAzJYBvB/ztxT1AwDuHn1/N4Dvz3Asv2delvFOLTOOGR+7mS9/7u5T/wfgdgw/kf9fAP8wizEkxvU2AL8Y/Xty1mMD8E0Mf63rY/jZxkcBXAHgYQBPA/gvAJfP0dj+DcDjAB7DMLBOzGhs78HwV/THADw6+nf7rI8dGddUjptulxXJhD6gE8mEgl0kEwp2kUwo2EUyoWAXyYSCXSQTCnaRTPwfunRFDr94tcoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class = 8, Probability = 0.9947614\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 = 8, Probability = 0.9997372\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 = 8, Probability = 0.9887284\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"
          }
        }
      ]
    }
  ]
}