CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import layers, models\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and preprocess the MNIST dataset\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data to fit the model\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize pixel values to be between 0 and 1\n",
    "train_images, test_images = train_images / 255.0, test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert labels to one-hot encoding\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the CNN model\n",
    "model = models.Sequential()\n",
    "\n",
    "# First convolutional layer\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Second convolutional layer\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# Third convolutional layer\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "\n",
    "# Flatten the results to feed into a dense layer\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "# Fully connected (Dense) layers\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(10, activation='softmax'))  # 10 output classes for the digits 0-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.8681 - loss: 0.4292 - val_accuracy: 0.9823 - val_loss: 0.0517\n",
      "Epoch 2/5\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 10ms/step - accuracy: 0.9842 - loss: 0.0526 - val_accuracy: 0.9888 - val_loss: 0.0354\n",
      "Epoch 3/5\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.9892 - loss: 0.0340 - val_accuracy: 0.9893 - val_loss: 0.0361\n",
      "Epoch 4/5\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 9ms/step - accuracy: 0.9913 - loss: 0.0274 - val_accuracy: 0.9899 - val_loss: 0.0309\n",
      "Epoch 5/5\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.9933 - loss: 0.0222 - val_accuracy: 0.9916 - val_loss: 0.0258\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x37aeb53a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(train_images, train_labels, epochs=5, batch_size=64, shuffle=True, validation_data=(test_images, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9894 - loss: 0.0307\n",
      "Test accuracy: 0.9916\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print(f\"Test accuracy: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m576\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m650\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">279,968</span> (1.07 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m279,968\u001b[0m (1.07 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">93,322</span> (364.54 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m93,322\u001b[0m (364.54 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">186,646</span> (729.09 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m186,646\u001b[0m (729.09 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a new model that will output the feature maps at each convolutional and pooling layer\n",
    "\n",
    "# First we grah the output of the first 6 layers because that is all we care to visualize \n",
    "layer_outputs = [layer.output for layer in model.layers[:6]]  # Get the outputs of the first 6 layers\n",
    "\n",
    "layer_input = [layer.input for layer in model.layers[:1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "activation_model = models.Model(layer_input, outputs=layer_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose an image to visualize (from test set)\n",
    "test_image = test_images[11].reshape(1, 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "[4.1754009e-07 2.9680311e-13 5.5729554e-15 2.1163035e-16 8.5600228e-13\n",
      " 1.2138612e-09 9.9999952e-01 9.1246751e-16 3.3748770e-09 7.1747005e-16]\n",
      "0.9999995\n",
      "(array([6]),)\n"
     ]
    }
   ],
   "source": [
    "# Lets see what the model we trained actually outputs for that image\n",
    "temp = model.predict(test_image)\n",
    "\n",
    "# Get the softmax output \n",
    "temp2=temp[0]\n",
    "print(temp2) # softmax output \n",
    "\n",
    "# Get the highest value which is actually going to be our prediction \n",
    "print(max(temp2))\n",
    "\n",
    "# The index represents the number 0 - 9 \n",
    "print(np.where(temp2==(max(temp2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n"
     ]
    }
   ],
   "source": [
    "# Get the activations for this test image\n",
    "activations = activation_model.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing layer: Conv2D 1 with 32 filters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing layer: MaxPooling 1 with 32 filters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing layer: Conv2D 2 with 64 filters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing layer: MaxPooling 2 with 64 filters\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing layer: Conv2D 3 with 64 filters\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the feature maps for each layer\n",
    "layer_names = ['Conv2D 1', 'MaxPooling 1', 'Conv2D 2', 'MaxPooling 2', 'Conv2D 3']\n",
    "\n",
    "# Plot the activations\n",
    "for i, activation in enumerate(activations[:5]):  # Skip fully connected layers\n",
    "    num_filters = activation.shape[-1]\n",
    "    size = activation.shape[1]\n",
    "\n",
    "    print(f\"Visualizing layer: {layer_names[i]} with {num_filters} filters\")\n",
    "    \n",
    "    # Displaying the activations\n",
    "    fig, ax = plt.subplots(1, num_filters, figsize=(20, 5))\n",
    "    for j in range(num_filters):\n",
    "        if size == 1:\n",
    "            ax[j].imshow(activation[0, :, :, j], cmap='gray')\n",
    "        else:\n",
    "            ax[j].imshow(activation[0, :, :, j], cmap='viridis')\n",
    "        ax[j].axis('off')\n",
    "\n",
    "    plt.show()"
   ]
  }
 ],
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