Cifar10_Keras_example.ipynb

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  "metadata": {
    "colab": {
      "name": "PlayGround",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uDJZYTqdZW5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "47c7bb6a-f0a9-4288-d71d-6a4ccf0dc148"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.layers import Conv2D, BatchNormalization, Dropout, Dense, GlobalAveragePooling2D\n",
        "from keras.layers import Activation, Reshape, Permute, multiply, MaxPooling2D, AveragePooling2D\n",
        "import albumentations as albu\n",
        "from albumentations import (HorizontalFlip, ShiftScaleRotate, GridDistortion)\n",
        "import keras.backend as K\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQ_-SYQvyMj9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9670326a-c556-43ad-a1e4-5320adef02df"
      },
      "source": [
        "#Load cifar10 data\n",
        "(train_x, train_y), (test_x, test_y) = keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 12s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idUJGGJmp9-d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_y = keras.utils.to_categorical(train_y, 10)\n",
        "test_y = keras.utils.to_categorical(test_y, 10)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(train_x, train_y, test_size = 0.25, random_state = 2019)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnkQuwrMyckz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Swish activation function : https://arxiv.org/abs/1710.05941\n",
        "def _swish(x):\n",
        "  return K.sigmoid(x)*x\n",
        "\n",
        "#helper function for convolution -> batch_normalization-> activation\n",
        "def _conv_bn_act(filters = 32, kernel_size = (3,3), strides = 1, activation = \"relu\"):\n",
        "\n",
        "  def f(input_x):\n",
        "\n",
        "    x = Conv2D(filters = filters, kernel_size = kernel_size, strides = (strides, strides), kernel_initializer=\"he_normal\")(input_x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(activation)(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "  return f\n",
        "\n",
        "#helper function for fully-connected -> batch_normalization-> activation\n",
        "def _fc_bn_act(units, activation = \"relu\"):\n",
        "\n",
        "  def f(input_x):\n",
        "\n",
        "    x = Dense(units = units, kernel_initializer=\"he_normal\")(input_x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(activation)(x)\n",
        "    \n",
        "    return x\n",
        "  return f\n",
        "\n",
        "#Squeeze and excitation block in keras\n",
        "def SEBlock(se_ratio = 16, activation = \"relu\", data_format = 'channels_last', ki = \"he_normal\"):\n",
        "    '''\n",
        "    se_ratio : ratio for reduce the filter number of first Dense layer(fc layer) in block\n",
        "    activation : activation function that of first dense layer\n",
        "    data_format : channel axis is at the first of dimension or the last\n",
        "    ki : kernel initializer\n",
        "    '''\n",
        "\n",
        "    def f(input_x):\n",
        "\n",
        "        channel_axis = -1 if data_format == 'channels_last' else 1\n",
        "        input_channels = input_x._keras_shape[channel_axis]\n",
        "\n",
        "        reduced_channels = input_channels // se_ratio\n",
        "\n",
        "        #Squeeze operation\n",
        "        x = GlobalAveragePooling2D()(input_x)\n",
        "        x = Reshape(1,1,input_channels)(x) if data_format == 'channels_first' else x\n",
        "        x = Dense(reduced_channels, kernel_initializer= ki)(x)\n",
        "        x = Activation(activation)(x)\n",
        "        #Excitation operation\n",
        "        x = Dense(input_channels, kernel_initializer=ki, activation='sigmoid')(x)\n",
        "        x = Permute(dims=(3,1,2))(x) if data_format == 'channels_first' else x\n",
        "        x = multiply([input_x, x])\n",
        "\n",
        "        return x\n",
        "\n",
        "    return f\n",
        "\n",
        "\n",
        "def CNN(input_shape = (32,32,3), output_units = 10, activation = _swish, SE_flag = False, data_format = 'channels_last'):\n",
        "\n",
        "  input_layer = keras.layers.Input(shape = input_shape)\n",
        "\n",
        "  x = _conv_bn_act(filters=32, activation = activation)(input_layer)\n",
        "  x = SEBlock(se_ratio=1, activation = activation, data_format=data_format)(x) if SE_flag == True else x\n",
        "  x = _conv_bn_act(filters=32, activation = activation)(x)\n",
        "  x = MaxPooling2D(pool_size=(2,2))(x)\n",
        "  x = _conv_bn_act(filters=64, activation = activation)(x)\n",
        "  x = SEBlock(se_ratio=1, activation = activation, data_format=data_format)(x) if SE_flag == True else x\n",
        "  x = _conv_bn_act(filters=64, activation = activation)(x)\n",
        "  x = Dropout(0.25)(x)\n",
        "\n",
        "  #Use GlobalAveragePooling2D to replace flatten\n",
        "  x = GlobalAveragePooling2D()(x)\n",
        "  x = Reshape(1,1,x._keras_shape[1])(x) if data_format == 'channels_first' else x\n",
        "    \n",
        "  x = _fc_bn_act(units=256, activation = activation)(x)\n",
        "  x = Dropout(0.25)(x)\n",
        "  x = _fc_bn_act(units=128, activation = activation)(x)\n",
        "  \n",
        "  output_layer = Dense(units = output_units, activation=\"softmax\", kernel_initializer=\"he_normal\")(x)\n",
        "\n",
        "  model = keras.models.Model(inputs = [input_layer], outputs = [output_layer])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kUBkrRZZtmzf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 128\n",
        "lr = 1e-3\n",
        "epochs = 20\n",
        "optimizer = keras.optimizers.Adam(lr=lr)\n",
        "loss = \"categorical_crossentropy\"\n",
        "metric = ['accuracy']\n",
        "\n",
        "steps_per_epoch = len(x_train) // batch_size\n",
        "validation_steps = len(x_test) // batch_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5Q_YFr7zSn9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def input_generator(x,y,aug,batch_size):\n",
        "\n",
        "  x_len = len(x)\n",
        "  batch_x, batch_y = [],[]\n",
        "  while True:\n",
        "\n",
        "    batch_indices = np.random.choice(x_len, size = batch_size)\n",
        "    \n",
        "    for idx in batch_indices:\n",
        "      batch_y.append(y[idx])\n",
        "      batch_x.append(aug(image = x[idx])['image']/255.0)\n",
        "\n",
        "    batch_x, batch_y = np.stack(batch_x), np.stack(batch_y)\n",
        "    yield batch_x, batch_y\n",
        "    batch_x, batch_y = [],[]\n",
        "  \n",
        "aug_for_train = albu.Compose([HorizontalFlip(p=0.5),\n",
        "                              ShiftScaleRotate(shift_limit=0.1,scale_limit=0.25,rotate_limit=20,p=0.5),\n",
        "                              GridDistortion(p=0.5)])\n",
        "aug_for_valid = albu.Compose([])\n",
        "\n",
        "train_gen = input_generator(x_train, y_train, aug_for_train, batch_size)\n",
        "valid_gen = input_generator(x_test, y_test, aug_for_valid, batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dI713vL-23Ep",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_training_result(history):\n",
        "\n",
        "  plt.figure(figsize=(16,12))\n",
        "\n",
        "  plt.subplot(2,1,1)\n",
        "  plt.plot(history.history['loss'], label = 'train_loss', color = 'g')\n",
        "  plt.plot(history.history['val_loss'], label = 'valid_loss', color = 'r')\n",
        "  plt.title('training/validation loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.ylabel('loss')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.subplot(2,1,2)\n",
        "  plt.plot(history.history['acc'], label = 'train_acc', color = 'g')\n",
        "  plt.plot(history.history['val_acc'], label = 'valid_acc', color = 'r')\n",
        "  plt.title('training/validation accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0vHZkz-4BNz",
        "colab_type": "code",
        "outputId": "615afa60-f2f0-4071-8866-3648acc916e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Build the regular Convolution neural network\n",
        "regular_cnn = CNN()\n",
        "regular_cnn.compile(loss = loss, metrics = metric, optimizer = optimizer)\n",
        "regular_cnn.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 30, 30, 32)        128       \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 28, 28, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 28, 28, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 12, 12, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 12, 12, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 10, 10, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 10, 10, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation_10 (Activation)   (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_2 ( (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 256)               16640     \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "activation_11 (Activation)   (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "activation_12 (Activation)   (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 118,698\n",
            "Trainable params: 117,546\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYVAfvFo4eDP",
        "colab_type": "code",
        "outputId": "80b1547b-5494-40a5-e07b-e14420e9fe29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        }
      },
      "source": [
        "regular_cnn_history = regular_cnn.fit_generator(generator=train_gen, steps_per_epoch=steps_per_epoch, epochs=epochs, \n",
        "                                                validation_data=valid_gen, validation_steps=validation_steps)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Epoch 1/20\n",
            "292/292 [==============================] - 27s 93ms/step - loss: 1.6107 - acc: 0.4077 - val_loss: 1.6095 - val_acc: 0.4409\n",
            "Epoch 2/20\n",
            "292/292 [==============================] - 21s 74ms/step - loss: 1.3361 - acc: 0.5208 - val_loss: 1.2428 - val_acc: 0.5457\n",
            "Epoch 3/20\n",
            "292/292 [==============================] - 22s 75ms/step - loss: 1.2060 - acc: 0.5709 - val_loss: 1.2593 - val_acc: 0.5641\n",
            "Epoch 4/20\n",
            "292/292 [==============================] - 21s 73ms/step - loss: 1.1291 - acc: 0.5968 - val_loss: 1.2508 - val_acc: 0.5697\n",
            "Epoch 5/20\n",
            "292/292 [==============================] - 21s 73ms/step - loss: 1.0819 - acc: 0.6157 - val_loss: 0.9878 - val_acc: 0.6450\n",
            "Epoch 6/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 1.0224 - acc: 0.6372 - val_loss: 0.9627 - val_acc: 0.6595\n",
            "Epoch 7/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 0.9848 - acc: 0.6503 - val_loss: 1.0145 - val_acc: 0.6467\n",
            "Epoch 8/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 0.9586 - acc: 0.6590 - val_loss: 0.9473 - val_acc: 0.6627\n",
            "Epoch 9/20\n",
            "292/292 [==============================] - 21s 71ms/step - loss: 0.9323 - acc: 0.6721 - val_loss: 0.8896 - val_acc: 0.6900\n",
            "Epoch 10/20\n",
            "292/292 [==============================] - 22s 75ms/step - loss: 0.9101 - acc: 0.6785 - val_loss: 0.8483 - val_acc: 0.6907\n",
            "Epoch 11/20\n",
            "292/292 [==============================] - 21s 73ms/step - loss: 0.8923 - acc: 0.6877 - val_loss: 0.9232 - val_acc: 0.6670\n",
            "Epoch 12/20\n",
            "292/292 [==============================] - 22s 74ms/step - loss: 0.8643 - acc: 0.6956 - val_loss: 0.8381 - val_acc: 0.7146\n",
            "Epoch 13/20\n",
            "292/292 [==============================] - 21s 73ms/step - loss: 0.8447 - acc: 0.7029 - val_loss: 0.8430 - val_acc: 0.7034\n",
            "Epoch 14/20\n",
            "292/292 [==============================] - 21s 73ms/step - loss: 0.8395 - acc: 0.7054 - val_loss: 0.7790 - val_acc: 0.7284\n",
            "Epoch 15/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 0.8212 - acc: 0.7110 - val_loss: 0.7297 - val_acc: 0.7386\n",
            "Epoch 16/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 0.8048 - acc: 0.7182 - val_loss: 0.8339 - val_acc: 0.7217\n",
            "Epoch 17/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 0.7949 - acc: 0.7202 - val_loss: 0.7779 - val_acc: 0.7255\n",
            "Epoch 18/20\n",
            "292/292 [==============================] - 21s 72ms/step - loss: 0.7877 - acc: 0.7231 - val_loss: 0.7192 - val_acc: 0.7459\n",
            "Epoch 19/20\n",
            "292/292 [==============================] - 22s 75ms/step - loss: 0.7724 - acc: 0.7290 - val_loss: 0.7311 - val_acc: 0.7475\n",
            "Epoch 20/20\n",
            "292/292 [==============================] - 21s 73ms/step - loss: 0.7657 - acc: 0.7333 - val_loss: 0.7142 - val_acc: 0.7525\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UXvFv7T822c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "36f6a1ed-110f-401f-cd5f-775b1fdadf05"
      },
      "source": [
        "# Build the convolution neural network with SE block in it\n",
        "SE_cnn = CNN(SE_flag = True)\n",
        "SE_cnn.compile(loss = loss, metrics = metric, optimizer = optimizer)\n",
        "SE_cnn.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 30, 30, 32)   896         input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 30, 30, 32)   128         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 30, 30, 32)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_3 (Glo (None, 32)           0           activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_7 (Dense)                 (None, 32)           1056        global_average_pooling2d_3[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 32)           0           dense_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_8 (Dense)                 (None, 32)           1056        activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "multiply_1 (Multiply)           (None, 30, 30, 32)   0           activation_13[0][0]              \n",
            "                                                                 dense_8[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 28, 28, 32)   9248        multiply_1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 28, 28, 32)   128         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 32)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 14, 14, 32)   0           activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 12, 12, 64)   18496       max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 12, 12, 64)   256         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 12, 12, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_4 (Glo (None, 64)           0           activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_9 (Dense)                 (None, 64)           4160        global_average_pooling2d_4[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 64)           0           dense_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_10 (Dense)                (None, 64)           4160        activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "multiply_2 (Multiply)           (None, 12, 12, 64)   0           activation_16[0][0]              \n",
            "                                                                 dense_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 10, 10, 64)   36928       multiply_2[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 10, 10, 64)   256         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 10, 10, 64)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 10, 10, 64)   0           activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_5 (Glo (None, 64)           0           dropout_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_11 (Dense)                (None, 256)          16640       global_average_pooling2d_5[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 256)          1024        dense_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 256)          0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_6 (Dropout)             (None, 256)          0           activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_12 (Dense)                (None, 128)          32896       dropout_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 128)          512         dense_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 128)          0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dense_13 (Dense)                (None, 10)           1290        activation_20[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 129,130\n",
            "Trainable params: 127,978\n",
            "Non-trainable params: 1,152\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMNkWZsD-DZK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "outputId": "d19a96a2-0891-48e8-9b09-1c8a0fc548d1"
      },
      "source": [
        "SE_cnn_history = SE_cnn.fit_generator(generator=train_gen, steps_per_epoch=steps_per_epoch, epochs=epochs, \n",
        "                                                validation_data=valid_gen, validation_steps=validation_steps)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "292/292 [==============================] - 26s 89ms/step - loss: 1.5830 - acc: 0.4190 - val_loss: 1.5278 - val_acc: 0.4465\n",
            "Epoch 2/20\n",
            "292/292 [==============================] - 23s 79ms/step - loss: 1.2923 - acc: 0.5316 - val_loss: 1.1565 - val_acc: 0.5824\n",
            "Epoch 3/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 1.1684 - acc: 0.5790 - val_loss: 1.0259 - val_acc: 0.6248\n",
            "Epoch 4/20\n",
            "292/292 [==============================] - 23s 78ms/step - loss: 1.0780 - acc: 0.6151 - val_loss: 0.9771 - val_acc: 0.6442\n",
            "Epoch 5/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 1.0339 - acc: 0.6294 - val_loss: 0.9465 - val_acc: 0.6664\n",
            "Epoch 6/20\n",
            "292/292 [==============================] - 23s 77ms/step - loss: 0.9914 - acc: 0.6480 - val_loss: 0.9410 - val_acc: 0.6617\n",
            "Epoch 7/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 0.9476 - acc: 0.6646 - val_loss: 0.8868 - val_acc: 0.6867\n",
            "Epoch 8/20\n",
            "292/292 [==============================] - 23s 77ms/step - loss: 0.9212 - acc: 0.6739 - val_loss: 0.9502 - val_acc: 0.6628\n",
            "Epoch 9/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 0.9010 - acc: 0.6817 - val_loss: 0.9048 - val_acc: 0.6772\n",
            "Epoch 10/20\n",
            "292/292 [==============================] - 23s 77ms/step - loss: 0.8720 - acc: 0.6927 - val_loss: 0.8767 - val_acc: 0.6845\n",
            "Epoch 11/20\n",
            "292/292 [==============================] - 22s 77ms/step - loss: 0.8526 - acc: 0.7007 - val_loss: 0.7953 - val_acc: 0.7237\n",
            "Epoch 12/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 0.8395 - acc: 0.7062 - val_loss: 0.7369 - val_acc: 0.7390\n",
            "Epoch 13/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 0.8175 - acc: 0.7126 - val_loss: 0.7572 - val_acc: 0.7305\n",
            "Epoch 14/20\n",
            "292/292 [==============================] - 22s 75ms/step - loss: 0.8037 - acc: 0.7140 - val_loss: 0.7343 - val_acc: 0.7385\n",
            "Epoch 15/20\n",
            "292/292 [==============================] - 23s 78ms/step - loss: 0.7970 - acc: 0.7230 - val_loss: 0.7752 - val_acc: 0.7263\n",
            "Epoch 16/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 0.7801 - acc: 0.7248 - val_loss: 0.7434 - val_acc: 0.7345\n",
            "Epoch 17/20\n",
            "292/292 [==============================] - 23s 78ms/step - loss: 0.7756 - acc: 0.7243 - val_loss: 0.7887 - val_acc: 0.7237\n",
            "Epoch 18/20\n",
            "292/292 [==============================] - 22s 75ms/step - loss: 0.7553 - acc: 0.7349 - val_loss: 0.6744 - val_acc: 0.7670\n",
            "Epoch 19/20\n",
            "292/292 [==============================] - 23s 78ms/step - loss: 0.7432 - acc: 0.7394 - val_loss: 0.7068 - val_acc: 0.7560\n",
            "Epoch 20/20\n",
            "292/292 [==============================] - 22s 76ms/step - loss: 0.7239 - acc: 0.7471 - val_loss: 0.6941 - val_acc: 0.7609\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMJ_ms-4-PLv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "a0bc3af5-21b6-4360-cfa8-4854aed03f80"
      },
      "source": [
        "display_training_result(regular_cnn_history)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rrVChAk3mraXVva0Ys3QMfRv2JSggyO1kIiIiIiJyrR5/HNauzdpjNmoEb7992c3Dhw+n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Mqw+v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\nwAcPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrw5G_bJ-T3S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "c77d7537-5f57-4a77-9df7-08c2cdb2a762"
      },
      "source": [
        "display_training_result(SE_cnn_history)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LvvzMM89Qo0YNHn74YQD++te/EhQUxKJFizh+/Djp6em88sor9OzZ86Lrdu3aRXR0NBs3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eorJ9Rff73TqOr88/9c0UeOOGdp81ZqN250tiUHBUHv3ie2ILdr53N/3iIiIuJ/dCZXRIrm55+d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\nDINcSZIkSdLEMMiVJEmSJE2M/wN0wZPBRpSdZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6qT7b2wAwg3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "22c78c6e-38bd-4965-a49b-c1003959b3d4"
      },
      "source": [
        "regular_cnn_scores = regular_cnn.evaluate(x=x_test/255.0, y=y_test)\n",
        "SE_cnn_scores = SE_cnn.evaluate(x=x_test/255.0, y=y_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12500/12500 [==============================] - 2s 172us/step\n",
            "12500/12500 [==============================] - 2s 181us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ta-N5BAZBF90",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5b9ad3db-2613-40d6-de84-d0c0cbc60faf"
      },
      "source": [
        "print(f' Regular CNN :  loss {regular_cnn_scores[0]}, accuracy {regular_cnn_scores[1]}')\n",
        "print(f' SE CNN : loss {SE_cnn_scores[0]}, accruacy {SE_cnn_scores[1]}')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Regular CNN :  loss 0.717884089794159, accuracy 0.7539200000190734\n",
            " SE CNN : loss 0.6834373204803467, accruacy 0.7627999999809265\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WF177SqgBIic",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}