feature_map_solution.ipynb

{
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3 (ipykernel)",
            "language": "python"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4,
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Visual Demonstration of CNNs"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Import necessary libraries\n",
                "import numpy as np\n",
                "import random\n",
                "import pandas as pd\n",
                "import matplotlib.pyplot as plt\n",
                "%matplotlib inline\n",
                "\n",
                "import tensorflow as tf\n",
                "from tensorflow.keras.models import Sequential, Model\n",
                "\n",
                "from matplotlib import cm\n",
                "import helper\n",
                "from helper import cnn_model, get_cifar10, plot_featuremaps"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "{0: 'airplane',\n 1: 'automobile',\n 2: 'bird',\n 3: 'cat',\n 4: 'deer',\n 5: 'dog',\n 6: 'frog',\n 7: 'horse',\n 8: 'ship',\n 9: 'truck'}"
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# As we are using a pre-trained model, \n",
                "# we will only use 1000 images from the 'unseen' test data \n",
                "# The get_cifar10() function will load 1000 cifar10 images\n",
                "(x_test, y_test) = get_cifar10()\n",
                "\n",
                "# We also provide a handy dictionary to map response values to image labels\n",
                "cifar10dict = helper.cifar10dict\n",
                "cifar10dict"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "\u003cFigure size 1200x1200 with 5 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Let's look at some sample images with their labels\n",
                "# Run the helper code below to plot the image and its label\n",
                "num_images = 5\n",
                "fig, ax = plt.subplots(1,num_images,figsize=(12,12))\n",
                "for i in range(num_images):\n",
                "    image_index = random.randint(0,1000)\n",
                "    img = (x_test[image_index] + 0.5)\n",
                "    ax[i].imshow(img)\n",
                "    label = cifar10dict[np.argmax(y_test[image_index])]\n",
                "    ax[i].set_title(f'Actual: {label}')\n",
                "    ax[i].axis('off')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_4 (Conv2D)            (None, 32, 32, 32)        896       \n_________________________________________________________________\nactivation_6 (Activation)    (None, 32, 32, 32)        0         \n_________________________________________________________________\nconv2d_5 (Conv2D)            (None, 30, 30, 32)        9248      \n_________________________________________________________________\nactivation_7 (Activation)    (None, 30, 30, 32)        0         \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 15, 15, 32)        0         \n_________________________________________________________________\nconv2d_6 (Conv2D)            (None, 15, 15, 64)        18496     \n_________________________________________________________________\nactivation_8 (Activation)    (None, 15, 15, 64)        0         \n_________________________________________________________________\nconv2d_7 (Conv2D)            (None, 13, 13, 64)        36928     \n_________________________________________________________________\nactivation_9 (Activation)    (None, 13, 13, 64)        0         \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 6, 6, 64)          0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 2304)              0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 512)               1180160   \n_________________________________________________________________\nactivation_10 (Activation)   (None, 512)               0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 10)                5130      \n_________________________________________________________________\nactivation_11 (Activation)   (None, 10)                0         \n=================================================================\nTotal params: 1,250,858\nTrainable params: 1,250,858\nNon-trainable params: 0\n_________________________________________________________________\n"
                }
            ],
            "source": [
                "# For this exercise we use a pre-trained network by calling\n",
                "# the cnn_model() function\n",
                "model = cnn_model()\n",
                "model.summary()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\r 1/32 [..............................] - ETA: 4s - loss: 0.8871 - accuracy: 0.7500\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\r 6/32 [====\u003e.........................] - ETA: 0s - loss: 0.7284 - accuracy: 0.7344\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\r12/32 [==========\u003e...................] - ETA: 0s - loss: 0.7221 - accuracy: 0.7500\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\r20/32 [=================\u003e............] - ETA: 0s - loss: 0.7307 - accuracy: 0.7516\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\r28/32 [=========================\u003e....] - ETA: 0s - loss: 0.6901 - accuracy: 0.7667\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\u0008\r32/32 [==============================] - 0s 8ms/step - loss: 0.6972 - accuracy: 0.7660\nThe test set accuracy for the pre-trained model is 76.60 %\n"
                }
            ],
            "source": [
                "# Evaluate the pretrained model on the test set\n",
                "model_score = model.evaluate(x_test,y_test)\n",
                "print(f'The test set accuracy for the pre-trained model is {100*model_score[1]:.2f} %')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "\u003cFigure size 1200x1200 with 5 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Visualizing the predictions on 5 randomly selected images\n",
                "num_images = 5\n",
                "fig, ax = plt.subplots(1,num_images,figsize=(12,12))\n",
                "for i in range(num_images):\n",
                "    image_index = random.randint(0,1000)\n",
                "    prediction= cifar10dict[int(np.squeeze(np.argmax(model.predict(x_test[image_index:image_index+1]),axis=1),axis=0))]\n",
                "    img = (x_test[image_index] + 0.5)\n",
                "    ax[i].imshow(img)\n",
                "    ax[i].set_title(f'Predicted: {prediction} \\n Actual: {cifar10dict[np.argmax(y_test[image_index:image_index+1])]}')\n",
                "    ax[i].axis('off')"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Visualize kernels corresponding to the filters for the 1st layer\n",
                ""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [],
            "source": [
                "# The 'weights' variable is of the form \n",
                "# [height, width, channel, number of filters]\n",
                "\n",
                "# Use .get_weights() with the appropriate layer number \n",
                "# to get the weights and bias of the first layer i.e. layer number 0\n",
                "weights, bias= model.layers[0].get_weights()\n",
                "\n",
                "assert weights.shape == (3,3,3,32), \"Computed weights are incorrect\""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [],
            "source": [
                ""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Number of filters: 32\nNumber of channels 3\nFilter height 3\nFilter width 3\n"
                }
            ],
            "source": [
                "# How many filters are in the first convolution layer?\n",
                "n_filters = weights.shape[-1]\n",
                "print(f'Number of filters: {n_filters}')\n",
                "\n",
                "# Print the filter size\n",
                "filter_channel = weights.shape[2]\n",
                "filter_height = weights.shape[0]\n",
                "filter_width = weights.shape[1]\n",
                "print(f'Number of channels {filter_channel}')\n",
                "print(f'Filter height {filter_height}')\n",
                "print(f'Filter width {filter_width}')\n",
                ""
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### ⏸ Based on the dimensions of the input image given to the defined model, how many kernels constitute the first filter?\n",
                "\n",
                "\n",
                "#### A. $3$\n",
                "#### B. $32$\n",
                "#### C. $1$\n",
                "#### D. $24$"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [],
            "source": [
                "### edTest(test_chow1) ###\n",
                "# Submit an answer choice as a string below (eg. if you choose option C, put 'C')\n",
                "answer1 = 'A'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [],
            "source": [
                "# The 'weights' variable (defined above) is of the form \n",
                "# [height, width, channel, number of filters]\n",
                "# From this select all three channels, the entire length and width\n",
                "# and the first filter\n",
                "kernels_filter1 = weights[:,:,:,0]\n",
                "\n",
                "# Test case to check if you have indexed correctly\n",
                "assert kernels_filter1.shape == (3,3,3)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "\u003cFigure size 1200x400 with 3 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Use the helper code below to plot each kernel of the choosen filter \n",
                "fig, axes = plt.subplots(1,3,figsize = (12,4))\n",
                "colors = ['Red','Green','Blue']\n",
                "for num, i in enumerate(axes):\n",
                "  i.imshow(kernels_filter1[num],cmap='gray')\n",
                "  i.set_title(f'Kernel for {colors[num]} channel')\n",
                "  "
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Visualizing one *filter* for the first convolutional layer\n",
                "\n",
                "Each of the above *kernels* stacked together forms a filter, which interacts with the input."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Text(0.5, 1.0, '1st Filter of convolution')"
                    },
                    "execution_count": 37,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "\u003cFigure size 400x400 with 1 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# For the same filter above, we perform normalization because the current\n",
                "# values are between -1 and 1 and the imshow function would truncate all values \n",
                "# less than 0 making the visual difficult to infer from.\n",
                "kernels_filter1 = (kernels_filter1 - kernels_filter1.min(axis=(0,1)))/(kernels_filter1.max(axis=(0,1)) - kernels_filter1.min(axis=(0,1)))\n",
                "\n",
                "#Plotting the filter\n",
                "fig, ax = plt.subplots(1,1,figsize = (4,4))\n",
                "ax.imshow(kernels_filter1)\n",
                "ax.set_title(f'1st Filter of convolution')"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Visualizing all the filters (32) for the first convolutional layer"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Text(0.5, 0.53, 'All 32 filters for 1st convolution layer')"
                    },
                    "execution_count": 38,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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Eg0F94xvfaPZv3/zmNxUMBpudr6vKzc3V/Pnz9fLLL6tv374d+hi/+tWvtHbtWknShRdeqCFDhnTmEj3z5ptv6rnnnpMkzZs3T4cddlir5zvxxBM1depUSfv/utmeuXPnxu6F9EX//ve/tW/fPknSyJEj2/04X3zFos60YMECNTU1SZJ++9vfqnv37i3Oc+6558b+urt27Vq98sorbX68I488UnPmzEnIWuPRVW5faf8TQG/fvl2S9POf/7zd50IoKSlp9r779p40aZLKyspaXOaQQw7RAw88IGn/c0rMnz+/3fU8/PDDrd4j6tvf/nbsHhTRe6C5RZ+j6dNPP9ULL7zQ6sd+6qmnYs/pEz1/Iq9LV/f000/HXpVq9uzZOuGEE1qcp3///rr99tslSXV1dVq4cGGbHy87O1sLFy5s9V4jV111Vez01j5/nWHAgAHtPll03759de2110qSnn322RbPb5Ofn6/x48dLkioqKlRXV9fiY9TX18dekv68887Tf/3XfzX79+g9aIqLi/Wb3/ymzSc2/9nPfqZDDz1UTU1N7T4Je/fu3TVv3ryDei4iAPAamyUAPNHY2Bj7we38889v8ctk9+7ddd5550mSHnvsMTU2NtpeYqveeOMNvfHGG3r99de1ZMkSXX/99crKytK1116rWbNmxX6RjEdVVZWuv/56SVLv3r1jD6FJBosXL5a0f0Ppiw8d+KLoL9/btm1TKBRq9TxZWVktNtbcevbsGfvF5tFHH1VDQ0NHln3Q/vrXv0ra/wpPp5xySpvn+8EPftDiMq0ZP358m6+kZFNXuX2l/Q/DkPa3FX01KlPu2/ryyy9v83ynn3567CEP7X1+jj32WB133HGt/lsgENCJJ54oSfrwww9b/Lv7+Bc9Jn5R9PRDDz20xUN1Ovu6+EF0/YFAoNWHk0R94xvfiG1gtXedv/rVr6p3796t/lt+fr6OOOIISa1//hIhHA5r06ZNeuutt/Tmm2/qzTffVG5ubrN/+6Low2fC4bCeeuqpFv/+5z//Wbt27ZLU+kNwosfqsWPHtvtKWRkZGbGH+bT2ym9RY8eOVX5+fpv/DgB+wGYJAE8sXbo09lfh6KvgfFH09O3bt3eZH+5LS0tVWlqq448/XmVlZSovL9eGDRvUu3dv3X333Tr//PPj2th56623dOGFF6qhoUHBYFB/+MMfVFRUlMBrYNerr74qaf9fdjMyMlp9BZTo2wUXXBC73I4dO1r9eEcccUS7P8gHg8HYX1iffPJJHX744frJT36i559/XtXV1Z14zdoWiUT03nvvSZJOPvnkds974oknxv6i/Oabb7Z5vrZ+EbetK9y+UdHnXhgyZEjsF0lT0ds6KysrtpHRlujn8L333lN9fX2r5znQcwxF72VTU1PT4t+CwaAuvvhiSft/of3ieXbs2KGXXnpJknTJJZcoLa35j26dfV38IHqdBwwY0OYmh9T8Nmnv6+tgPn+d5aOPPtL06dM1YMAAFRYW6rDDDlNpaamOPfZYHXvssc02BKObHm6nnnpq7PmyWrsXTfS0oqKiFhvX1dXVev/99yVJ999/f7vH6UAgEHtp+7aO01LXOWYBwMFgswSAJ6J33+3evXub9zhw/8W1vbv7eq2kpCT2RJ6VlZV66KGHjC63adMmlZWV6dNPP1V6eroef/zxdp/g0Y927tzZocu1djdyaf/DCQ5k3rx5Gjt2rKT9v4DcfvvtOv/889WzZ08NGzZMd9xxR0KfGPXTTz+N/f+BNr4yMzPVs2dPSfsf4tIWk+tti9e3b1T0F8Y+ffrEfdnobd2jR482H24QFX24guM4zT63bgfarIlucLS1kRp9aM2ePXta3CvgiSeeiF3uiw/BkTr/uvhB9DqbbCxHr3N7X18H+/k7WC+88IKOPvpozZs3Tx999NEBz79nz55WT4/eu2TZsmXNnkh1+/btevHFFyVJ3/ve91p00tnHaalrHbMAoKN4NRwA1oXD4dhdfj/77LPYc5O055lnnlFNTU2XvVtvWVmZcnJytGfPHj355JMHfFjAtm3bdPbZZ2vbtm0KBAJ6+OGHdeGFF1parT3RXy4GDhyoZ5991vhybb0qhslDUQoKCvTss89q7dq1+sMf/qBly5bpH//4hxobG/XKK6/olVde0e23365nnnmmzVeN6Cwmj9f/4vMPtKYrPAQnqivdvpLZbXwwlzX5/BysM844QyUlJQqFQnrsscc0ceLE2L9FH4Jz1FFHtfrcHFFd5brYlAzX+ZNPPtG3v/1t1dXVqVu3brrmmmt0zjnn6Mtf/rIKCwtjD3t76aWXdNZZZ0lq+zpNmDBB119/vSKRiB555JHY8xz97ne/ix2LW3sIjnsTaMaMGe0+nMutvVcG6krHLADoKDZLAFj3hz/8oc2/jLWlrq5OTz75pC677LIErergpKen65BDDtGePXsO+JfBXbt26atf/Wrs8e/33nuvvve979lYpnXRe018/PHHGjRo0AH/8t2Zhg0bpmHDhknaf/f55cuXa+HChXr66ae1c+dOXXTRRfrggw+Uk5PTqXPdf1Ft727q0v6XDHXfM8BPvLp9o3r16qWtW7dq27ZtcV82elt/8sknamhoaLfLjz/+WNL+X8wT9dfyQCCgSy65RL/85S/1t7/9TR9//LGKior0/vvvx568ta2HK9q4Lu6H/kSfuLg1tbW1xh/zYESv84G+vqTPr3NX/fr64x//qM8++0zS/ify/epXv9rq+UzuCdSzZ0+NGzdOFRUVWrRokX76058qEAho0aJFkvY/VKe1hxxFj9PS/u+1paWl8V8RAEhCPAwHgHXRh9T06dNHjz/++AHfoq980pUfilNfXx97WEC3bt3aPF91dbXOOeccvf3225L2v7JL9FVg/MT0r/nR5wuoq6vT//7v/yZySe3Kz8/X2LFj9dRTT+mqq66StP+u6atWrer0WcFgMPaEkH//+9/bPe/69etjTwrs519QTG/fznxljJNOOknS/ufFae/hAK2J3tb19fWx5z5pS/RVqo444oh2/5J+sKIPsWlsbFRFRYUk6fe//33s3y+55JJWL2fjurjv0dfeL+0bN25s9+N01uc/ep03b97c7kNI9u3bF7tNuurX11tvvSVp/2ZOWxsl0ufP/3Qg0YfibN68WcuXL9fq1av1zjvvSGr9XiWS9KUvfUmHHnqopP1PhNvV740DALawWQLAqk2bNsV+gbrooov0rW9964Bv0Vc/qaqq0pYtW7xcfpsWL14ce8LEY489ttXz1NXV6fzzz9drr70maf9LXl533XXW1tiZok+yGolE2j3f17/+9dj///KXv0zomkxF78outf5EiZ3h7LPPliS9/fbbWrNmTZvne/DBB1tcxu/au31NuzERfd6Uurq62MvimnLf1u09x9DLL78c29hM9OfnuOOOi/1CH90kiT4E5/TTT2/zoWk2rot7dnu/tLf1aj5R7idnPpgGout3HKfdlxp/8sknY0883FW/vqKvKBWJRNq8105dXZ3xHwvOOuus2Eu0L1y4MPbErnl5ebEnZ27N1772NUn7X/En+gSuAJDq2CwBYNWjjz4a+6tV9BUgDiR6Psdx9OijjyZsba3561//GnuVgLa8/fbbsb+mS/sfN/5F9fX1uvDCC2P3rvjRj36kW2+9tXMXa1H0STV37tzZ7itEDB06VGVlZZKk559/PvYY+rZs3rxZjz/+eIfX9eGHH6qqqqrd8yxdujT2/239AnqwrrzyythDF6644opWXylm6dKlsV9uhw0bpqFDhyZkLZ3pYG9f025MfPe73439NXz27Nntrmvr1q3N3nff3g8++KAqKytbXKa6ulo//OEPJe1/GMqVV155UOs1Eb13ydq1a/X444/r3XffbXZ6a2xcl+7du8de3WThwoWtPlnqihUr9Otf/7rdj+N+Mt4PPvggrjW4XXjhhSouLpYk3XbbbfrHP/7R4jyhUEjXXHONpP1P4NpVH8IZvRdabW1tq5sUjY2NmjRpkvHDzdwvp/ynP/0pdi+lb3zjG+0+59e1114be/6wyZMnH/CeLM8//7w2bNhgtCYA8CueswSAVdHNjt69e2vEiBFGlzn55JPVt29fbd26VY8++qhmz56dyCU2s2rVKp177rk666yzdM455+i4445Tz5491dDQoI8++khLly7Vo48+qr1790qSLrvssmZ/WY+65JJLYr9Ennnmmbr88svbfSnLrKwsHXnkkYm5Up3gtNNOk7T/+QsmT56s6dOnq2fPnrG72R9++OGx8y5cuFBDhgzR9u3bdcstt2jJkiX6/ve/r2OPPVbZ2dn65JNPtGHDBr344ot66aWXNG7cuDYfcnAgW7Zs0ejRo3X00Ufrwgsv1JAhQ2K/UIdCIVVUVOgPf/iDpP0PETrQS/t21LHHHqurr75at99+u9544w2ddNJJuu6663TiiSeqrq5Of/7zn/XrX/9ajY2NysrK0v3335+QdbQm+vwFUa+//nrs/1988cVmr6Jx+OGHa/jw4bH3D/b2jaebA8nOztajjz6qsrIy1dXV6ayzztKECRN04YUXqm/fvopEItq4caOef/55LV68uMU9GR544AGdfPLJqq+v1/nnn6/p06dr7Nix6tatm9avX6+5c+fGnlfommuusfIwju985zu64YYb5DiOpkyZImn/KyZF713XFhvXZcqUKZo8ebI+/vhjjRgxQjfddJO+8pWv6N///reee+45LViwQEOGDNHLL7/c5sc48cQTlZ2drb179+qmm25SRkaGBgwYENtYPPTQQ42e4yYzM1MPPPCAxo4dq5qaGg0fPlzXXnutzjrrLGVkZGj16tWaO3du7CE6d9xxh3r16hX3dbbhm9/8pm644QZFIhFdeumlev3113X22WeroKBAb731lu69916tW7dOp59+uvFDGS+77DLNmTOn2cPT2noITtTAgQP1m9/8Rpdddpn+/e9/6/TTT9eECRN0wQUXqF+/fmp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                        "text/plain": "\u003cFigure size 1400x1400 with 32 Axes\u003e"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Use the helper code below to visualize all filters for the first layer\n",
                "\n",
                "fig,ax=plt.subplots(4,8,figsize=(14,14))\n",
                "fig.subplots_adjust(bottom=0.2,top=0.5)\n",
                "for i in range(4):\n",
                "    for j in range(8):\n",
                "        filters= weights[:,:,:,(8*i)+j]\n",
                "        filters = (filters - filters.min(axis=(0,1)))/(filters.max(axis=(0,1)) - filters.min(axis=(0,1)))\n",
                "        ax[i,j].imshow(filters)\n",
                "        \n",
                "fig.suptitle('All 32 filters for 1st convolution layer',fontsize=20, y=0.53);     "
            ]
        }
    ]
}