tvganesh/deconstructig-convolutional-neural-networks.ipynb Secret

## deconstructig-convolutional-neural-networks.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deconstructig Convolutional Neural Networks",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM+zS4YKgQuxch+5mQXRGAP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/tvganesh/de869e7d1259a4a102c26811f07019df/deconstructig-convolutional-neural-networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nC0b_9W4UVUU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6ffc6c85-27d9-4974-94b8-e92cddf8dcde"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D, Input\n",
        "from keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils import np_utils\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGkvVD-xUXjh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist=tf.keras.datasets.mnist\n",
        "# Set training and test data and labels\n",
        "(training_images,training_labels),(test_images,test_labels)=mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mli1pNqBm8mz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Normalize training data\n",
        "X =np.array(training_images).reshape(training_images.shape[0],28,28,1) \n",
        "# Normalize the images by dividing by 255.0\n",
        "X = X/255.0\n",
        "X.shape\n",
        "# Use one hot encoding for the labels\n",
        "Y = np_utils.to_categorical(training_labels, 10)\n",
        "Y.shape\n",
        "# Split training data into training and validation data in the ratio of 80:20\n",
        "X_train, X_validation, y_train, y_validation = train_test_split(X,Y,test_size=0.20, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2g3GnkeoIhA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "57ce6597-352b-4eaf-ada9-5df5b7ad7269"
      },
      "source": [
        "# Normalize test data\n",
        "X_test =np.array(test_images).reshape(test_images.shape[0],28,28,1) \n",
        "X_test=X_test/255.0\n",
        "#Use OHE for the test labels\n",
        "Y_test = np_utils.to_categorical(test_labels, 10)\n",
        "X_test.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SLKNkwla5bW",
        "colab_type": "text"
      },
      "source": [
        "# Display data\n",
        "Display the training data and the corresponding labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z1BonqlogKN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "outputId": "16eea826-e8d0-47c0-bd37-32e818ab7a8a"
      },
      "source": [
        "print(training_labels[0:10])\n",
        "f, axes = plt.subplots(1, 10, sharey=True,figsize=(10,10))\n",
        "for i,ax in enumerate(axes.flat):\n",
        "    ax.axis('off')\n",
        "    ax.imshow(X[i,:,:,0],cmap=\"gray\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5 0 4 1 9 2 1 3 1 4]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQ6gmGNfbGXq",
        "colab_type": "text"
      },
      "source": [
        "# Create a Convolutional Neural Network\n",
        "The CNN consists of 3 layers\n",
        "- Conv2D of size 28 x 28 with 24 filters\n",
        "- Perform Max pooling\n",
        "- Conv2D of size 14 x 14 with 48 filters\n",
        "- Perform max pooling\n",
        "- Conv2d of size 7 x 7 with 64 filters\n",
        "- Flatten\n",
        "- Use Dense layer with 128 units\n",
        "- Perform 25% dropout\n",
        "- Perform categorical cross entropy with softmax activation function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1y3YNLKTkrxQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes=10\n",
        "inputs = Input(shape=(28,28,1))\n",
        "x = Conv2D(24,kernel_size=(3,3),padding='same',activation=\"relu\")(inputs)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "x = Conv2D(48, (3, 3), padding='same',activation='relu')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "x = Conv2D(64, (3, 3), padding='same',activation='relu')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dropout(0.25)(x)\n",
        "output = Dense(num_classes,activation=\"softmax\")(x)\n",
        "\n",
        "model = Model(inputs,output)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', \n",
        "          optimizer='adam', \n",
        "          metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6rze_rwchWe",
        "colab_type": "text"
      },
      "source": [
        "# Summary of CNN\n",
        "Display the summary of CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSepNGECoqza",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "3f3c11c0-e8a3-4c53-811c-f806b103b363"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 24)        240       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 24)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 14, 14, 48)        10416     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 48)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 7, 7, 64)          27712     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               73856     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 113,514\n",
            "Trainable params: 113,514\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ia3RMY7XdlGR",
        "colab_type": "text"
      },
      "source": [
        "#  Perform Gradient descent and validate with the validation data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC1rNM8hk-PV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "06167714-72f3-47e0-8b87-fb32f1d7c9ea"
      },
      "source": [
        "epochs = 20\n",
        "batch_size=256\n",
        "history = model.fit(X_train,y_train,\n",
        "         epochs=epochs,\n",
        "         batch_size=batch_size,\n",
        "         validation_data=(X_validation,y_validation))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/20\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.4735 - accuracy: 0.8550 - val_loss: 0.0995 - val_accuracy: 0.9702\n",
            "Epoch 2/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.1067 - accuracy: 0.9679 - val_loss: 0.0631 - val_accuracy: 0.9805\n",
            "Epoch 3/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0741 - accuracy: 0.9772 - val_loss: 0.0510 - val_accuracy: 0.9856\n",
            "Epoch 4/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0566 - accuracy: 0.9826 - val_loss: 0.0484 - val_accuracy: 0.9853\n",
            "Epoch 5/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0473 - accuracy: 0.9849 - val_loss: 0.0382 - val_accuracy: 0.9883\n",
            "Epoch 6/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0394 - accuracy: 0.9873 - val_loss: 0.0403 - val_accuracy: 0.9886\n",
            "Epoch 7/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0330 - accuracy: 0.9897 - val_loss: 0.0374 - val_accuracy: 0.9893\n",
            "Epoch 8/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0291 - accuracy: 0.9906 - val_loss: 0.0379 - val_accuracy: 0.9884\n",
            "Epoch 9/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0268 - accuracy: 0.9911 - val_loss: 0.0371 - val_accuracy: 0.9903\n",
            "Epoch 10/20\n",
            "48000/48000 [==============================] - 2s 37us/step - loss: 0.0219 - accuracy: 0.9934 - val_loss: 0.0331 - val_accuracy: 0.9910\n",
            "Epoch 11/20\n",
            "48000/48000 [==============================] - 2s 37us/step - loss: 0.0209 - accuracy: 0.9935 - val_loss: 0.0357 - val_accuracy: 0.9898\n",
            "Epoch 12/20\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0192 - accuracy: 0.9936 - val_loss: 0.0335 - val_accuracy: 0.9912\n",
            "Epoch 13/20\n",
            "48000/48000 [==============================] - 2s 37us/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0397 - val_accuracy: 0.9902\n",
            "Epoch 14/20\n",
            "48000/48000 [==============================] - 2s 37us/step - loss: 0.0159 - accuracy: 0.9950 - val_loss: 0.0389 - val_accuracy: 0.9895\n",
            "Epoch 15/20\n",
            "48000/48000 [==============================] - 2s 37us/step - loss: 0.0145 - accuracy: 0.9952 - val_loss: 0.0330 - val_accuracy: 0.9908\n",
            "Epoch 16/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0130 - accuracy: 0.9959 - val_loss: 0.0313 - val_accuracy: 0.9920\n",
            "Epoch 17/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0339 - val_accuracy: 0.9917\n",
            "Epoch 18/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0109 - accuracy: 0.9963 - val_loss: 0.0347 - val_accuracy: 0.9920\n",
            "Epoch 19/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0110 - accuracy: 0.9964 - val_loss: 0.0396 - val_accuracy: 0.9904\n",
            "Epoch 20/20\n",
            "48000/48000 [==============================] - 2s 36us/step - loss: 0.0096 - accuracy: 0.9972 - val_loss: 0.0415 - val_accuracy: 0.9903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRdxdohDdVKf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "244f0acf-85c5-4f30-b06c-99bf75453239"
      },
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc      = history.history[     'accuracy' ]\n",
        "val_acc  = history.history[ 'val_accuracy' ]\n",
        "loss     = history.history[    'loss' ]\n",
        "val_loss = history.history['val_loss' ]\n",
        "\n",
        "epochs   = range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot  ( epochs,     acc,label=\"training accuracy\" )\n",
        "plt.plot  ( epochs, val_acc, label='validation acuracy' )\n",
        "plt.title ('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot  ( epochs,     loss , label=\"training loss\")\n",
        "plt.plot  ( epochs, val_loss,label=\"validation loss\" )\n",
        "plt.title ('Training and validation loss'   )\n",
        "plt.legend()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f755453eda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XM1IqfoJsirK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "outputId": "fc6e5561-76bb-40be-d17c-10a108c503a4"
      },
      "source": [
        "f, axes = plt.subplots(1, 10, sharey=True,figsize=(10,10))\n",
        "for i,ax in enumerate(axes.flat):\n",
        "    ax.axis('off')\n",
        "    ax.imshow(X_test[i,:,:,0],cmap=\"gray\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMmPWCyVryG3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "f8031874-be2d-459b-c500-f577b34d7d27"
      },
      "source": [
        "l=[]\n",
        "for i in range(10):\n",
        "  x=X_test[i].reshape(1,28,28,1)\n",
        "  y=model.predict(x)\n",
        "  m = np.argmax(y, axis=1)\n",
        "  print(m)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7]\n",
            "[2]\n",
            "[1]\n",
            "[0]\n",
            "[4]\n",
            "[1]\n",
            "[4]\n",
            "[9]\n",
            "[5]\n",
            "[9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eoSXHOE3hkru",
        "colab_type": "text"
      },
      "source": [
        "# Generate the filter activations at the intermediate CNN layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hpaRMTD5ZWK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "59bca632-bec2-469f-e639-2ce55e707200"
      },
      "source": [
        "\n",
        "img = test_images[51].reshape(1,28,28,1)\n",
        "fig = plt.figure(figsize=(5,5))\n",
        "print(img.shape)\n",
        "plt.imshow(img[0,:,:,0],cmap=\"gray\")\n",
        "plt.axis('off')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmBTIGkQhtLf",
        "colab_type": "text"
      },
      "source": [
        "# Display the activations at the intermediate layers\n",
        "This displays the intermediate activations as the image passes through the filters and generates these feature maps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YToGnlOE4ttA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "276dd23f-b385-4dae-e65b-ce929e543357"
      },
      "source": [
        "layer_names = ['conv2d_4', 'conv2d_5', 'conv2d_6']\n",
        "\n",
        "layer_outputs = [layer.output for layer in model.layers if 'conv2d' in layer.name]\n",
        "activation_model = Model(inputs=model.input,outputs=layer_outputs)\n",
        "intermediate_activations = activation_model.predict(img)\n",
        "images_per_row = 8\n",
        "max_images = 8\n",
        "\n",
        "for layer_name, layer_activation in zip(layer_names, intermediate_activations):\n",
        "    print(layer_name,layer_activation.shape)\n",
        "    n_features = layer_activation.shape[-1]\n",
        "    print(\"features=\",n_features)\n",
        "    n_features = min(n_features, max_images)\n",
        "    print(n_features)\n",
        "\n",
        "    size = layer_activation.shape[1]\n",
        "    print(\"size=\",size)\n",
        "    n_cols = n_features // images_per_row\n",
        "    display_grid = np.zeros((size * n_cols, images_per_row * size))\n",
        "\n",
        "\n",
        "    for col in range(n_cols):\n",
        "      for row in range(images_per_row):\n",
        "          channel_image = layer_activation[0,:, :, col * images_per_row + row]\n",
        "\n",
        "          channel_image -= channel_image.mean()\n",
        "          channel_image /= channel_image.std()\n",
        "          channel_image *= 64\n",
        "          channel_image += 128\n",
        "          channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n",
        "          display_grid[col * size : (col + 1) * size,\n",
        "                         row * size : (row + 1) * size] = channel_image\n",
        "    scale = 2. / size\n",
        "    plt.figure(figsize=(scale * display_grid.shape[1],\n",
        "                        scale * display_grid.shape[0]))\n",
        "    plt.axis('off')\n",
        "    plt.title(layer_name)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(display_grid, aspect='auto', cmap='viridis')\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conv2d_4 (1, 28, 28, 24)\n",
            "features= 24\n",
            "8\n",
            "size= 28\n",
            "n_cols= 1\n",
            "grid= (28, 224)\n",
            "conv2d_5 (1, 14, 14, 48)\n",
            "features= 48\n",
            "8\n",
            "size= 14\n",
            "n_cols= 1\n",
            "grid= (14, 112)\n",
            "conv2d_6 (1, 7, 7, 64)\n",
            "features= 64\n",
            "8\n",
            "size= 7\n",
            "n_cols= 1\n",
            "grid= (7, 56)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gmp02AKr7kRu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# To fix the ImportError: cannot import name 'imresize' in the next cell. Run this cell. Then comment and restart and run all\n",
        "#!pip install scipy==1.1.0\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSaf2b95YmfN",
        "colab_type": "text"
      },
      "source": [
        "## Visualize the pattern that the filters respond to maximally\n",
        "- Choose a loss function that maximizes the value of the CNN filter in a given layer\n",
        "- Start from a blank input image.\n",
        "- Do gradient ascent in input space. Modify input values so that the filter activates more\n",
        "- Repeat this in a loop."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESmfCjEre-fs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "781764d2-9070-4bc6-f139-4ce7f3d6ec27"
      },
      "source": [
        "from vis.visualization import visualize_activation, get_num_filters\n",
        "from vis.utils import utils\n",
        "from vis.input_modifiers import Jitter\n",
        "\n",
        "max_filters = 24\n",
        "selected_indices = []\n",
        "vis_images = [[], [], [], [], []]\n",
        "i = 0\n",
        "selected_filters = [[0, 3, 11, 15, 16, 17, 18, 22], \n",
        "    [8, 21, 23, 25, 31, 32, 35, 41], \n",
        "    [2, 7, 11, 14, 19, 26, 35, 48]]\n",
        "\n",
        "# Set the layers\n",
        "layer_name = ['conv2d_4', 'conv2d_5', 'conv2d_6']\n",
        "# Set the layer indices\n",
        "layer_idx = [1,3,5]\n",
        "for layer_name,layer_idx in zip(layer_name,layer_idx):\n",
        "\n",
        "\n",
        "    # Visualize all filters in this layer.\n",
        "    if selected_filters:\n",
        "        filters = selected_filters[i]\n",
        "    else:\n",
        "        # Randomly select filters\n",
        "        filters = sorted(np.random.permutation(get_num_filters(model.layers[layer_idx]))[:max_filters])\n",
        "    selected_indices.append(filters)\n",
        "\n",
        "    # Generate input image for each filter.\n",
        "    # Loop through the selected filters in each layer and generate the activation of these filters\n",
        "    for idx in filters:\n",
        "        img = visualize_activation(model, layer_idx, filter_indices=idx, tv_weight=0., \n",
        "                                   input_modifiers=[Jitter(0.05)], max_iter=300) \n",
        "        vis_images[i].append(img)\n",
        "\n",
        "    # Generate stitched image palette with 4 cols so we get 2 rows.\n",
        "    stitched = utils.stitch_images(vis_images[i], cols=4)    \n",
        "    plt.figure(figsize=(20, 30))\n",
        "    plt.title(layer_name)\n",
        "    plt.axis('off')\n",
        "    stitched = stitched.reshape(1,61,127,1)\n",
        "    plt.imshow(stitched[0,:,:,0])\n",
        "    plt.show()\n",
        "    i += 1"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drNqJEwbSrat",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1c2ec899-88f1-4518-d256-636c497dcba5"
      },
      "source": [
        "from vis.utils import utils\n",
        "new_vis_images = [[], [], [], [], []]\n",
        "i = 0\n",
        "layer_name = ['conv2d_4', 'conv2d_5', 'conv2d_6']\n",
        "layer_idx = [1,3,5]\n",
        "for layer_name,layer_idx in zip(layer_name,layer_idx):\n",
        "   \n",
        "    # Generate input image for each filter.\n",
        "    for j, idx in enumerate(selected_indices[i]):\n",
        "        img = visualize_activation(model, layer_idx, filter_indices=idx, \n",
        "                                   seed_input=vis_images[i][j], input_modifiers=[Jitter(0.05)], max_iter=300) \n",
        "        #img = utils.draw_text(img, 'Filter {}'.format(idx))  \n",
        "        new_vis_images[i].append(img)\n",
        "\n",
        "    stitched = utils.stitch_images(new_vis_images[i], cols=4)   \n",
        "    plt.figure(figsize=(20, 30))\n",
        "    plt.title(layer_name)\n",
        "    plt.axis('off')\n",
        "    print(stitched.shape)\n",
        "    stitched = stitched.reshape(1,61,127,1)\n",
        "    plt.imshow(stitched[0,:,:,0])\n",
        "    plt.show()\n",
        "    i += 1"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(61, 127, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(61, 127, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(61, 127, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0guQbgsGP3uz",
        "colab_type": "text"
      },
      "source": [
        "## Visualizing Class Outputs\n",
        "Here the CNN will generate the  image that maximally represents the category. Each of the output represents one of the digits as can be seen below\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb_CTYi-warp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "f9a69e28-7689-4f57-f280-cb534f2b7cae"
      },
      "source": [
        "from vis.utils import utils\n",
        "from keras import activations\n",
        "codes = '''\n",
        "zero 0\n",
        "one 1\n",
        "two 2\n",
        "three 3\n",
        "four 4\n",
        "five 5\n",
        "six 6\n",
        "seven 7\n",
        "eight 8\n",
        "nine 9\n",
        "'''\n",
        "layer_idx=10\n",
        "initial = []\n",
        "images = []\n",
        "tuples = []\n",
        "# Swap softmax with linear for better visualization\n",
        "model.layers[layer_idx].activation = activations.linear\n",
        "model = utils.apply_modifications(model)\n",
        "for line in codes.split('\\n'):\n",
        "    if not line:\n",
        "        continue\n",
        "    name, idx = line.rsplit(' ', 1)\n",
        "    idx = int(idx)\n",
        "    img = visualize_activation(model, layer_idx, filter_indices=idx, \n",
        "                               tv_weight=0., max_iter=300, input_modifiers=[Jitter(0.05)])\n",
        "\n",
        "    initial.append(img)\n",
        "    tuples.append((name, idx))\n",
        "\n",
        "i = 0\n",
        "for name, idx in tuples:\n",
        "    img = visualize_activation(model, layer_idx, filter_indices=idx,\n",
        "                               seed_input = initial[i], max_iter=300, input_modifiers=[Jitter(0.05)])\n",
        "    #img = utils.draw_text(img, name) # Unable to display text on gray scale image\n",
        "    i += 1\n",
        "    images.append(img)\n",
        "\n",
        "stitched = utils.stitch_images(images, cols=4)\n",
        "plt.figure(figsize=(20, 20))\n",
        "plt.axis('off')\n",
        "stitched = stitched.reshape(1,94,127,1)\n",
        "plt.imshow(stitched[0,:,:,0])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}