TCN_cifar10.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TCN_cifar10.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMD39VflcLprlyfDbS1VhVQ",
      "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/github/ashishpatel26/tcn-keras-Examples/blob/master/TCN_cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__9fLZpy1sOW",
        "colab_type": "text"
      },
      "source": [
        "### Load the Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDRv43Dq029J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "f212a464-42ca-49a3-f165-a48b7e0026e6"
      },
      "source": [
        "!pip install keras-tcn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras-tcn\n",
            "  Downloading https://files.pythonhosted.org/packages/a8/5b/31eed031c196dc192eddf346f053ec6a97aefa4b931164fd8665c92a9d7d/keras_tcn-3.1.1-py3-none-any.whl\n",
            "Collecting keras==2.3.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.6/dist-packages (from keras-tcn) (1.18.5)\n",
            "Collecting keras-applications>=1.0.6\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 8.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (3.13)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.15.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.1.2)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.4.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (2.10.0)\n",
            "Installing collected packages: keras-applications, keras, keras-tcn\n",
            "  Found existing installation: Keras 2.4.3\n",
            "    Uninstalling Keras-2.4.3:\n",
            "      Successfully uninstalled Keras-2.4.3\n",
            "Successfully installed keras-2.3.1 keras-applications-1.0.8 keras-tcn-3.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04sFou_J09w4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from keras.datasets import cifar10\n",
        "from keras.utils import to_categorical\n",
        "from tcn import compiled_tcn"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWb70L8e1y-K",
        "colab_type": "text"
      },
      "source": [
        "### Read the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vmySAHB1FEm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def data_generator():\n",
        "    # input image dimensions\n",
        "    img_rows, img_cols = 32, 32\n",
        "    (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "    print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n",
        "    x_train = x_train.reshape(-1, img_rows * img_cols, 3)\n",
        "    x_test = x_test.reshape(-1, img_rows * img_cols, 3)\n",
        "\n",
        "    num_classes = 10\n",
        "    y_train = to_categorical(y_train, num_classes)\n",
        "    y_test = to_categorical(y_test, num_classes)\n",
        "    print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n",
        "    y_train = np.expand_dims(y_train, axis=2)\n",
        "    y_test = np.expand_dims(y_test, axis=2)\n",
        "\n",
        "    x_train = x_train.astype('float32')\n",
        "    x_test = x_test.astype('float32')\n",
        "    x_train /= 255\n",
        "    x_test /= 255\n",
        "    print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n",
        "\n",
        "    return (x_train, y_train), (x_test, y_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BEkunVb12e5",
        "colab_type": "text"
      },
      "source": [
        "### Load the data and Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uL0m8zQw1Psm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a0c50e68-4667-430b-9877-c7ec5bf4e7e7"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = data_generator()\n",
        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n",
            "(50000, 1024, 3) (50000, 10) (10000, 1024, 3) (10000, 10)\n",
            "(50000, 1024, 3) (50000, 10, 1) (10000, 1024, 3) (10000, 10, 1)\n",
            "(50000, 1024, 3) (50000, 10, 1) (10000, 1024, 3) (10000, 10, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnPV4-fw15oX",
        "colab_type": "text"
      },
      "source": [
        "### Design the TCN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMVPitCS1Wg3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d551ddf1-ebe0-4b0d-a40f-2bc339d113db"
      },
      "source": [
        "model = compiled_tcn(return_sequences=False,\n",
        "                         num_feat=3,\n",
        "                         num_classes=10,\n",
        "                         nb_filters=20,\n",
        "                         kernel_size=6,\n",
        "                         dilations=[2 ** i for i in range(9)],\n",
        "                         nb_stacks=1,\n",
        "                         max_len=x_train[0:1].shape[1],\n",
        "                         use_skip_connections=True)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x.shape= (None, 20)\n",
            "model.x = (None, 1024, 3)\n",
            "model.y = (None, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTFmxvWn1qww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "f4ae4848-2c7b-474f-e37c-880ad6a3fffc"
      },
      "source": [
        "print(f'x_train.shape = {x_train.shape}')\n",
        "print(f'y_train.shape = {y_train.shape}')\n",
        "print(f'x_test.shape = {x_test.shape}')\n",
        "print(f'y_test.shape = {y_test.shape}')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train.shape = (50000, 1024, 3)\n",
            "y_train.shape = (50000, 10, 1)\n",
            "x_test.shape = (10000, 1024, 3)\n",
            "y_test.shape = (10000, 10, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcYz3Ymj1_u4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "1ef9fb8a-adab-4a6d-8cfd-bcd78ddcc40e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         [(None, 1024, 3)]         0         \n",
            "_________________________________________________________________\n",
            "tcn (TCN)                    (None, 20)                41600     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                210       \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 41,810\n",
            "Trainable params: 41,810\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKE0L5YU3bx1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = y_train.squeeze().argmax(axis=1)\n",
        "y_test = y_test.squeeze().argmax(axis=1)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4GwAi1B2Cc4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "1f521a90-9ef0-4fa2-adad-070ea7fcf5be"
      },
      "source": [
        "history = model.fit(x_train, y_train, epochs=10,validation_data=(x_test, y_test), workers=4, use_multiprocessing=True)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 36s 23ms/step - loss: 1.2417 - accuracy: 0.5578 - val_loss: 1.2150 - val_accuracy: 0.5705\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 36s 23ms/step - loss: 1.2315 - accuracy: 0.5613 - val_loss: 1.1823 - val_accuracy: 0.5748\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 36s 23ms/step - loss: 1.2149 - accuracy: 0.5678 - val_loss: 1.1692 - val_accuracy: 0.5806\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 36s 23ms/step - loss: 1.2134 - accuracy: 0.5640 - val_loss: 1.1709 - val_accuracy: 0.5806\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 36s 23ms/step - loss: 1.2040 - accuracy: 0.5703 - val_loss: 1.1602 - val_accuracy: 0.5885\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 35s 23ms/step - loss: 1.1979 - accuracy: 0.5723 - val_loss: 1.1538 - val_accuracy: 0.5899\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 35s 22ms/step - loss: 1.1951 - accuracy: 0.5720 - val_loss: 1.1529 - val_accuracy: 0.5843\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 35s 22ms/step - loss: 1.1880 - accuracy: 0.5781 - val_loss: 1.1299 - val_accuracy: 0.5998\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 35s 22ms/step - loss: 1.1851 - accuracy: 0.5773 - val_loss: 1.1431 - val_accuracy: 0.5907\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 35s 22ms/step - loss: 1.1760 - accuracy: 0.5800 - val_loss: 1.1287 - val_accuracy: 0.5938\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsZkgUgI2IwA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a65732d2-546b-4690-a712-15f5b528c431"
      },
      "source": [
        "result = model.evaluate(x_test, y_test)\n",
        "print(f\"Accuracy : {result[1] * 100:.2f} %\")\n",
        "print(f\"Loss : {result[0] * 100:.2f} %\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 2s 7ms/step - loss: 1.1287 - accuracy: 0.5938\n",
            "Accuracy : 59.38 %\n",
            "Loss : 112.87 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BiLSTwT5s_0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "3b415b12-c2c2-482c-cc0d-03954de5ce32"
      },
      "source": [
        "from tensorflow import keras\n",
        "keras.utils.plot_model(model, show_shapes=True)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjmCS9CI70hI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "2a30f3dd-1f0f-416f-c91a-9b14941af3ad"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "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": "g06RkTFZ71Zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import seaborn as sns"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxsgXBti8G4B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "4b75fd1c-f161-4ba5-b8f8-c1183011fb1c"
      },
      "source": [
        "# predict probabilities for test set\n",
        "yhat_probs = np.argmax(model.predict(x_test, verbose=0), axis=1)\n",
        " \n",
        "# kappa\n",
        "kappa = cohen_kappa_score(y_test, yhat_probs)\n",
        "print('Cohens kappa: %f' % kappa)\n",
        "# ROC AUC\n",
        "# auc = roc_auc_score(y_test, yhat_probs, multi_class = 'ovo')\n",
        "# print('ROC AUC: %f' % auc)\n",
        "\n",
        "print(classification_report(y_test, yhat_probs))\n",
        "# confusion matrix\n",
        "matrix = confusion_matrix(y_test, yhat_probs)\n",
        "plt.figure(figsize = (15,7) )\n",
        "sns.heatmap(matrix, annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cohens kappa: 0.548667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      0.68      0.65      1000\n",
            "           1       0.77      0.65      0.70      1000\n",
            "           2       0.50      0.49      0.49      1000\n",
            "           3       0.37      0.36      0.37      1000\n",
            "           4       0.57      0.42      0.49      1000\n",
            "           5       0.48      0.47      0.48      1000\n",
            "           6       0.59      0.72      0.65      1000\n",
            "           7       0.67      0.63      0.65      1000\n",
            "           8       0.73      0.80      0.76      1000\n",
            "           9       0.63      0.71      0.67      1000\n",
            "\n",
            "    accuracy                           0.59     10000\n",
            "   macro avg       0.59      0.59      0.59     10000\n",
            "weighted avg       0.59      0.59      0.59     10000\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}