Custom__modek_cifar10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Custom _modek_cifar10.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/Sanyam8055/100-Days-of-ML/blob/master/Custom__modek_cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WvjoFjhTV3bQ",
        "colab": {}
      },
      "source": [
        "from keras import backend as K\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "% matplotlib inline\n",
        "np.random.seed(2017) \n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers.convolutional import Convolution2D, MaxPooling2D, DepthwiseConv2D,SeparableConv2D\n",
        "from keras.layers import Activation, Flatten, Dense, Dropout\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.utils import np_utils"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gXV4AJYVVvri",
        "colab": {}
      },
      "source": [
        "from keras.datasets import cifar10\n",
        "(train_features, train_labels), (test_features, test_labels) = cifar10.load_data()\n",
        "num_train, img_channels, img_rows, img_cols =  train_features.shape\n",
        "num_test, _, _, _ =  test_features.shape\n",
        "num_classes = len(np.unique(train_labels))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "12c4fccf-ae37-4208-eab1-19ba85cf6a60",
        "id": "_jc4BSCgVvaI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        }
      },
      "source": [
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "fig = plt.figure(figsize=(8,3))\n",
        "for i in range(num_classes):\n",
        "    ax = fig.add_subplot(2, 5, 1 + i, xticks=[], yticks=[])\n",
        "    idx = np.where(train_labels[:]==i)[0]\n",
        "    features_idx = train_features[idx,::]\n",
        "    img_num = np.random.randint(features_idx.shape[0])\n",
        "    im = features_idx[img_num]\n",
        "    ax.set_title(class_names[i])\n",
        "    plt.imshow(im)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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cZKY8Osokh8W2I9MUClxWq/K2FWXJTQjhY9skz6AXZk6qujDZ58DBQ67ObW6MhTlOKEMd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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "p_snlhL6VvRa",
        "colab": {}
      },
      "source": [
        "def plot_model_history(model_history):\n",
        "    fig, axs = plt.subplots(1,2,figsize=(15,5))\n",
        "    # summarize history for accuracy\n",
        "    axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])\n",
        "    axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])\n",
        "    axs[0].set_title('Model Accuracy')\n",
        "    axs[0].set_ylabel('Accuracy')\n",
        "    axs[0].set_xlabel('Epoch')\n",
        "    axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)\n",
        "    axs[0].legend(['train', 'val'], loc='best')\n",
        "    # summarize history for loss\n",
        "    axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])\n",
        "    axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])\n",
        "    axs[1].set_title('Model Loss')\n",
        "    axs[1].set_ylabel('Loss')\n",
        "    axs[1].set_xlabel('Epoch')\n",
        "    axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)\n",
        "    axs[1].legend(['train', 'val'], loc='best')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7O5IB2V2VvIY",
        "colab": {}
      },
      "source": [
        "def accuracy(test_x, test_y, model):\n",
        "    result = model.predict(test_x)\n",
        "    predicted_class = np.argmax(result, axis=1)\n",
        "    true_class = np.argmax(test_y, axis=1)\n",
        "    num_correct = np.sum(predicted_class == true_class) \n",
        "    accuracy = float(num_correct)/result.shape[0]\n",
        "    return (accuracy * 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xk0-jludVu15",
        "colab": {}
      },
      "source": [
        "train_features = train_features.astype('float32')/255\n",
        "test_features = test_features.astype('float32')/255\n",
        "# convert class labels to binary class labels\n",
        "train_labels = np_utils.to_categorical(train_labels, num_classes)\n",
        "test_labels = np_utils.to_categorical(test_labels, num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kz3POzNgLr4V",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_PWF9CqLRow",
        "colab_type": "code",
        "outputId": "4aeca7da-b431-40bf-dd9c-66b9d33ff6cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Define the model\n",
        "model = Sequential()\n",
        "model.add(SeparableConv2D(48, 3, 3, input_shape=(32, 32, 3)))#32\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('elu'))\n",
        "model.add(SeparableConv2D(48, 3, 3))#30\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('elu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))#15\n",
        "model.add(Dropout(0.25))\n",
        "model.add(SeparableConv2D(96, 3, 3, border_mode='same'))#15\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('elu'))\n",
        "model.add(SeparableConv2D(96, 3, 3))#13\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('elu'))   \n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))#6\n",
        "model.add(Dropout(0.4))\n",
        "model.add(SeparableConv2D(192, 3, 3,border_mode='same'))#4\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(SeparableConv2D(192, 3, 3))#2\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(SeparableConv2D(num_classes,kernel_size=(2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Activation('softmax'))\n",
        "# Compile the model\n",
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Update your `SeparableConv2D` call to the Keras 2 API: `SeparableConv2D(48, (3, 3), input_shape=(32, 32, 3...)`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `SeparableConv2D` call to the Keras 2 API: `SeparableConv2D(48, (3, 3))`\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `SeparableConv2D` call to the Keras 2 API: `SeparableConv2D(96, (3, 3), padding=\"same\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: UserWarning: Update your `SeparableConv2D` call to the Keras 2 API: `SeparableConv2D(96, (3, 3))`\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: UserWarning: Update your `SeparableConv2D` call to the Keras 2 API: `SeparableConv2D(192, (3, 3), padding=\"same\")`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: UserWarning: Update your `SeparableConv2D` call to the Keras 2 API: `SeparableConv2D(192, (3, 3))`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_40\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "separable_conv2d_236 (Separa (None, 30, 30, 48)        219       \n",
            "_________________________________________________________________\n",
            "batch_normalization_37 (Batc (None, 30, 30, 48)        192       \n",
            "_________________________________________________________________\n",
            "activation_253 (Activation)  (None, 30, 30, 48)        0         \n",
            "_________________________________________________________________\n",
            "separable_conv2d_237 (Separa (None, 28, 28, 48)        2784      \n",
            "_________________________________________________________________\n",
            "batch_normalization_38 (Batc (None, 28, 28, 48)        192       \n",
            "_________________________________________________________________\n",
            "activation_254 (Activation)  (None, 28, 28, 48)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_113 (MaxPoolin (None, 14, 14, 48)        0         \n",
            "_________________________________________________________________\n",
            "dropout_119 (Dropout)        (None, 14, 14, 48)        0         \n",
            "_________________________________________________________________\n",
            "separable_conv2d_238 (Separa (None, 14, 14, 96)        5136      \n",
            "_________________________________________________________________\n",
            "batch_normalization_39 (Batc (None, 14, 14, 96)        384       \n",
            "_________________________________________________________________\n",
            "activation_255 (Activation)  (None, 14, 14, 96)        0         \n",
            "_________________________________________________________________\n",
            "separable_conv2d_239 (Separa (None, 12, 12, 96)        10176     \n",
            "_________________________________________________________________\n",
            "batch_normalization_40 (Batc (None, 12, 12, 96)        384       \n",
            "_________________________________________________________________\n",
            "activation_256 (Activation)  (None, 12, 12, 96)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_114 (MaxPoolin (None, 6, 6, 96)          0         \n",
            "_________________________________________________________________\n",
            "dropout_120 (Dropout)        (None, 6, 6, 96)          0         \n",
            "_________________________________________________________________\n",
            "separable_conv2d_240 (Separa (None, 6, 6, 192)         19488     \n",
            "_________________________________________________________________\n",
            "batch_normalization_41 (Batc (None, 6, 6, 192)         768       \n",
            "_________________________________________________________________\n",
            "activation_257 (Activation)  (None, 6, 6, 192)         0         \n",
            "_________________________________________________________________\n",
            "separable_conv2d_241 (Separa (None, 4, 4, 192)         38784     \n",
            "_________________________________________________________________\n",
            "batch_normalization_42 (Batc (None, 4, 4, 192)         768       \n",
            "_________________________________________________________________\n",
            "activation_258 (Activation)  (None, 4, 4, 192)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_115 (MaxPoolin (None, 2, 2, 192)         0         \n",
            "_________________________________________________________________\n",
            "dropout_121 (Dropout)        (None, 2, 2, 192)         0         \n",
            "_________________________________________________________________\n",
            "separable_conv2d_242 (Separa (None, 1, 1, 10)          2698      \n",
            "_________________________________________________________________\n",
            "flatten_34 (Flatten)         (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "activation_259 (Activation)  (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 81,973\n",
            "Trainable params: 80,629\n",
            "Non-trainable params: 1,344\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MyttUU2lN98",
        "colab_type": "code",
        "outputId": "b354af69-f03e-43d7-d080-47b9b11f1ccc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(zoom_range=0.0, \n",
        "                             horizontal_flip=False\n",
        "                            )\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "def scheduler(epoch, lr):\n",
        "  return round(0.003 * 1/(1 + 0.319 * epoch), 10)\n",
        "\n",
        "# train the model\n",
        "start = time.time()\n",
        "# Train the model\n",
        "model_info = model.fit_generator(datagen.flow(train_features, train_labels, batch_size = 128),\n",
        "                                 samples_per_epoch = train_features.shape[0], nb_epoch = 50, \n",
        "                                 validation_data = (test_features, test_labels), verbose=1, callbacks=[LearningRateScheduler(scheduler)])\n",
        "\n",
        "end = time.time()\n",
        "print (\"Model took %0.2f seconds to train\"%(end - start))\n",
        "# plot model history\n",
        "plot_model_history(model_info)\n",
        "# compute test accuracy\n",
        "print (\"Accuracy on test data is: %0.2f\"%accuracy(test_features, test_labels, model))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras_pre..., validation_data=(array([[[..., verbose=1, callbacks=[<keras.ca..., steps_per_epoch=390, epochs=50)`\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "390/390 [==============================] - 37s 95ms/step - loss: 1.3742 - acc: 0.5014 - val_loss: 1.3988 - val_acc: 0.5484\n",
            "Epoch 2/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 1.0189 - acc: 0.6352 - val_loss: 1.0049 - val_acc: 0.6564\n",
            "Epoch 3/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.8870 - acc: 0.6841 - val_loss: 0.9708 - val_acc: 0.6715\n",
            "Epoch 4/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.8030 - acc: 0.7172 - val_loss: 0.7382 - val_acc: 0.7411\n",
            "Epoch 5/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.7512 - acc: 0.7372 - val_loss: 0.7794 - val_acc: 0.7339\n",
            "Epoch 6/50\n",
            "390/390 [==============================] - 28s 72ms/step - loss: 0.7100 - acc: 0.7498 - val_loss: 0.6611 - val_acc: 0.7721\n",
            "Epoch 7/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.6811 - acc: 0.7613 - val_loss: 0.6688 - val_acc: 0.7681\n",
            "Epoch 8/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.6554 - acc: 0.7693 - val_loss: 0.6246 - val_acc: 0.7828\n",
            "Epoch 9/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.6346 - acc: 0.7767 - val_loss: 0.6536 - val_acc: 0.7733\n",
            "Epoch 10/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.6146 - acc: 0.7847 - val_loss: 0.6117 - val_acc: 0.7893\n",
            "Epoch 11/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5993 - acc: 0.7900 - val_loss: 0.6117 - val_acc: 0.7917\n",
            "Epoch 12/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5869 - acc: 0.7963 - val_loss: 0.6609 - val_acc: 0.7733\n",
            "Epoch 13/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5737 - acc: 0.7977 - val_loss: 0.6122 - val_acc: 0.7910\n",
            "Epoch 14/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5614 - acc: 0.8036 - val_loss: 0.5749 - val_acc: 0.8034\n",
            "Epoch 15/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5564 - acc: 0.8051 - val_loss: 0.5811 - val_acc: 0.8017\n",
            "Epoch 16/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5428 - acc: 0.8090 - val_loss: 0.6066 - val_acc: 0.7936\n",
            "Epoch 17/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5350 - acc: 0.8123 - val_loss: 0.5911 - val_acc: 0.8003\n",
            "Epoch 18/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5282 - acc: 0.8154 - val_loss: 0.5835 - val_acc: 0.8018\n",
            "Epoch 19/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5220 - acc: 0.8183 - val_loss: 0.5507 - val_acc: 0.8134\n",
            "Epoch 20/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5188 - acc: 0.8180 - val_loss: 0.5389 - val_acc: 0.8157\n",
            "Epoch 21/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5082 - acc: 0.8220 - val_loss: 0.5393 - val_acc: 0.8175\n",
            "Epoch 22/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.5057 - acc: 0.8232 - val_loss: 0.5337 - val_acc: 0.8194\n",
            "Epoch 23/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4997 - acc: 0.8252 - val_loss: 0.5378 - val_acc: 0.8194\n",
            "Epoch 24/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4949 - acc: 0.8271 - val_loss: 0.5372 - val_acc: 0.8167\n",
            "Epoch 25/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4923 - acc: 0.8270 - val_loss: 0.5364 - val_acc: 0.8172\n",
            "Epoch 26/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4874 - acc: 0.8295 - val_loss: 0.5335 - val_acc: 0.8220\n",
            "Epoch 27/50\n",
            "390/390 [==============================] - 28s 72ms/step - loss: 0.4803 - acc: 0.8317 - val_loss: 0.5476 - val_acc: 0.8167\n",
            "Epoch 28/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4789 - acc: 0.8309 - val_loss: 0.5294 - val_acc: 0.8200\n",
            "Epoch 29/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4696 - acc: 0.8356 - val_loss: 0.5303 - val_acc: 0.8207\n",
            "Epoch 30/50\n",
            "390/390 [==============================] - 28s 72ms/step - loss: 0.4757 - acc: 0.8317 - val_loss: 0.5212 - val_acc: 0.8233\n",
            "Epoch 31/50\n",
            "390/390 [==============================] - 28s 72ms/step - loss: 0.4622 - acc: 0.8373 - val_loss: 0.5233 - val_acc: 0.8237\n",
            "Epoch 32/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4666 - acc: 0.8350 - val_loss: 0.5086 - val_acc: 0.8278\n",
            "Epoch 33/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4609 - acc: 0.8381 - val_loss: 0.5294 - val_acc: 0.8211\n",
            "Epoch 34/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4654 - acc: 0.8360 - val_loss: 0.5205 - val_acc: 0.8250\n",
            "Epoch 35/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4588 - acc: 0.8376 - val_loss: 0.5180 - val_acc: 0.8275\n",
            "Epoch 36/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4512 - acc: 0.8407 - val_loss: 0.5183 - val_acc: 0.8253\n",
            "Epoch 37/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4511 - acc: 0.8402 - val_loss: 0.5256 - val_acc: 0.8237\n",
            "Epoch 38/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4443 - acc: 0.8433 - val_loss: 0.5101 - val_acc: 0.8287\n",
            "Epoch 39/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4497 - acc: 0.8418 - val_loss: 0.5136 - val_acc: 0.8288\n",
            "Epoch 40/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4415 - acc: 0.8436 - val_loss: 0.5098 - val_acc: 0.8299\n",
            "Epoch 41/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4398 - acc: 0.8445 - val_loss: 0.5224 - val_acc: 0.8260\n",
            "Epoch 42/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4441 - acc: 0.8431 - val_loss: 0.5151 - val_acc: 0.8290\n",
            "Epoch 43/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4389 - acc: 0.8442 - val_loss: 0.5060 - val_acc: 0.8312\n",
            "Epoch 44/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4337 - acc: 0.8464 - val_loss: 0.5074 - val_acc: 0.8309\n",
            "Epoch 45/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4351 - acc: 0.8445 - val_loss: 0.5061 - val_acc: 0.8319\n",
            "Epoch 46/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4305 - acc: 0.8471 - val_loss: 0.5131 - val_acc: 0.8305\n",
            "Epoch 47/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4300 - acc: 0.8463 - val_loss: 0.5107 - val_acc: 0.8300\n",
            "Epoch 48/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4278 - acc: 0.8482 - val_loss: 0.5038 - val_acc: 0.8330\n",
            "Epoch 49/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4258 - acc: 0.8483 - val_loss: 0.5137 - val_acc: 0.8277\n",
            "Epoch 50/50\n",
            "390/390 [==============================] - 28s 71ms/step - loss: 0.4212 - acc: 0.8498 - val_loss: 0.5016 - val_acc: 0.8350\n",
            "Model took 1398.99 seconds to train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F1Y0UVTVSXNNE237nGDMkkbMnDmX6yBRmjEwlPyMRn2/vWK9AwLGlrI5VO6tZtaOaT3dU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\nEREREennlPiJiIiIiIj0c/8POeLKtTfZ+8gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy on test data is: 83.50\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}