Bangla.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ruhullahil/gcolab/blob/master/Bangla.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dg_9FP6LwBkz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import backend as K"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jsKY9VBNKQM",
        "colab_type": "code",
        "outputId": "780d0d35-04a6-4aa0-ef61-9246ba71808a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhPVg_W7wYB4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from pathlib import Path\n",
        "import os\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm_notebook \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from keras.utils import np_utils\n",
        "\n",
        "# Disable Warnings\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
        "\n",
        "# Importing sequential model\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "# Importing convolutional layers\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "\n",
        "from tensorflow.python.keras.layers import Input, Dense, Activation, Flatten, Dropout\n",
        "\n",
        "# Importing MNIST dataset\n",
        "from tensorflow.keras.datasets import mnist\n",
        "\n",
        "# Importing optimizers\n",
        "from tensorflow.keras.optimizers import SGD, RMSprop, Adam\n",
        "\n",
        "# One Hot Encoding (OHE)\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Importing numpy and matplotlib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6uyA1xSz2i9",
        "colab_type": "code",
        "outputId": "44c720a3-c94a-4dcb-b9d3-ad443943fda7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "!unzip '/content/drive/My Drive/Colab Notebooks/Datasets/BanglaLekha-Isolated.zip '"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unzip:  cannot find or open /content/drive/My Drive/Colab Notebooks/Datasets/BanglaLekha-Isolated.zip , /content/drive/My Drive/Colab Notebooks/Datasets/BanglaLekha-Isolated.zip .zip or /content/drive/My Drive/Colab Notebooks/Datasets/BanglaLekha-Isolated.zip .ZIP.\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
        "id": "oTaxooLtwchU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "fileDirectories = [filedir for filedir in Path('./BanglaLekha-Isolated/Images/').glob('**/*.png')]\n",
        "\n",
        "#fileDirectories = fileDirectories(log_dir=\"./BanglaLekha-Isolated\".glob('**/*.png'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "f58a0392-6633-4f76-90a8-d345c36ea24a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(fileDirectories))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "166105\n"
          ],
          "name": "stdout"
        }
      ]
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        "def getCharName(dirAddress):\n",
        "    fileName = str(dirAddress).split('\\\\')[-1]\n",
        "    #print(fileName)\n",
        "    fileName = fileName.split('.')[0]\n",
        "    charName = fileName.split('_')[-1]\n",
        "    return int(charName)\n",
        "    \n",
        "#print(fileDirectories[0], \"=\", getCharName(fileDirectories[0]))"
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      "outputs": []
    },
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        "# Image dimention: (total_image, width, height)\n",
        "def plotFigs(images, rows=1, columns=1, title=None):\n",
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        "    if title:\n",
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        "    \n",
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        "        fig.add_subplot(rows, columns, i+1)\n",
        "        plt.imshow(img, cmap='gray')\n",
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        "    plt.show()"
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        "\n",
        "half = len(fileDirectories)//4"
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        "imgs = np.array(imgs)\n",
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      "execution_count": 0,
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zLpg0aVI9fdRl5qYu0p9nzpxpLxJI5DRlefJuVh0o53vCudDBTdi1axfr6urs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\n4gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhrZgmP_ws0y",
        "colab_type": "code",
        "outputId": "b6fe05c1-5853-456c-8e42-8ed64a8171bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(imgs.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(41526, 80, 80)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_Clm2Nxw1W1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define ConvNet\n",
        "class LeNet:\n",
        "    @staticmethod\n",
        "    def build(input_shape, classes):\n",
        "        model = Sequential()\n",
        "        \n",
        "        # Conv -> ReLU -> Pooling\n",
        "        model.add(Conv2D(32, kernel_size=5, padding='same', input_shape=input_shape))\n",
        "        model.add(Activation('relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.2))\n",
        "        \n",
        "        # Conv -> ReLU -> Pooling\n",
        "        model.add(Conv2D(16, kernel_size=5, input_shape='same'))\n",
        "        model.add(Activation('relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.2))\n",
        "        \n",
        "        model.add(Conv2D(16, kernel_size=5, input_shape='same'))\n",
        "        model.add(Activation('relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "        model.add(Dropout(0.2))\n",
        "                          \n",
        "        # Flattern -> ReLU\n",
        "        model.add(Flatten())\n",
        "        model.add(Activation('relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(0.2))\n",
        "        \n",
        "        model.add(Dense(40))\n",
        "        model.add(Activation('relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(0.2))\n",
        "        \n",
        "        # SoftMax\n",
        "        model.add(Dense(classes))\n",
        "        model.add(Activation('softmax'))\n",
        "        \n",
        "        model.summary()\n",
        "        return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZAJ3Tuow3mF",
        "colab_type": "code",
        "outputId": "3f7d689a-74f8-4424-a72d-dd01ea8bcd35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "#model = LeNet.build()\n",
        "X_train, X_test, y_train, y_test = train_test_split(imgs, imgClass, test_size=0.33, random_state=42)\n",
        "\n",
        "print(len(np.unique(imgClass)), len(np.unique(y_test)), len(np.unique(y_train)))\n",
        "\n",
        "print(y_train.shape)\n",
        "\n",
        "#y_train = np.reshape(y_train, (len(y_train), -1))\n",
        "#y_test = np.reshape(y_test, (len(y_test), -1))\n",
        "\n",
        "print(y_train.shape, y_test.shape)\n",
        "print(y_train[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "84 84 84\n",
            "(27822,)\n",
            "(27822,) (13704,)\n",
            "67\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEogDCK7w5Rn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Constants & Hyper-Parameters\n",
        "NB_EPOCH = 40\n",
        "BATCH_SIZE = 64\n",
        "VERBOSE = 1\n",
        "OPTIMIZER = Adam()\n",
        "VALIDATION_SPLIT = 0.2\n",
        "IMG_ROWS, IMG_COLS = X_train.shape[1:]\n",
        "NB_CLASSES = len(np.unique(imgClass))+1\n",
        "INPUT_SHAPE = (1, IMG_ROWS, IMG_COLS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nI3d46aw68Z",
        "colab_type": "code",
        "outputId": "4e81ee50-21be-4f0a-e23c-a09ed7b3f64d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "\n",
        "# Setting Image ordering to theano : (channels, rows, columns)\n",
        "# Updated keras uses this command\n",
        "K.set_image_data_format('channels_first')\n",
        "#K.set_image_dim_ordering('th')\n",
        "\n",
        "# Normalize\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255\n",
        "X_test /= 255\n",
        "\n",
        "print(X_train.shape, 'train samples previous')\n",
        "# we need a 60K x [1 x 28 x 28] shape as input to the CONVNET\n",
        "X_train = X_train[:, np.newaxis, :, :]\n",
        "X_test = X_test[:, np.newaxis, :, :]\n",
        "print(X_train.shape, 'train samples')\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "y_train = np_utils.to_categorical(y_train, NB_CLASSES)\n",
        "y_test = np_utils.to_categorical(y_test, NB_CLASSES)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(27822, 80, 80) train samples previous\n",
            "(27822, 1, 80, 80) train samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1QVekQSw8dJ",
        "colab_type": "code",
        "outputId": "c8e266d0-2e40-441c-f2f1-6bc49c542072",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = LeNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=OPTIMIZER, \n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history = model.fit(X_train, y_train, \n",
        "                    batch_size=BATCH_SIZE, epochs=NB_EPOCH,\n",
        "                    verbose=1, validation_split=VALIDATION_SPLIT,\n",
        "                   use_multiprocessing=True)\n",
        "\n",
        "score = model.evaluate(X_test, y_test, verbose=VERBOSE)\n",
        "\n",
        "print(\"Test score:\", score[0], '\\nTest accuracy:', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 80, 80)        832       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 32, 80, 80)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 32, 80, 80)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 32, 40, 40)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32, 40, 40)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 36, 36)        12816     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 16, 36, 36)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 16, 36, 36)        144       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 18, 18)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 18, 18)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 16, 14, 14)        6416      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 16, 14, 14)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 16, 14, 14)        56        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 16, 7, 7)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 16, 7, 7)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 784)               3136      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 40)                31400     \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 40)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 40)                160       \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 40)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 85)                3485      \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 85)                0         \n",
            "=================================================================\n",
            "Total params: 58,765\n",
            "Trainable params: 56,857\n",
            "Non-trainable params: 1,908\n",
            "_________________________________________________________________\n",
            "Train on 22257 samples, validate on 5565 samples\n",
            "Epoch 1/40\n",
            "22257/22257 [==============================] - 18s 787us/sample - loss: 3.7652 - accuracy: 0.1447 - val_loss: 4.3957 - val_accuracy: 0.0473\n",
            "Epoch 2/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 2.4574 - accuracy: 0.3899 - val_loss: 1.8750 - val_accuracy: 0.5360\n",
            "Epoch 3/40\n",
            "22257/22257 [==============================] - 10s 440us/sample - loss: 1.7954 - accuracy: 0.5332 - val_loss: 1.3017 - val_accuracy: 0.6717\n",
            "Epoch 4/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 1.5093 - accuracy: 0.5937 - val_loss: 1.1563 - val_accuracy: 0.6985\n",
            "Epoch 5/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 1.3587 - accuracy: 0.6324 - val_loss: 1.0366 - val_accuracy: 0.7305\n",
            "Epoch 6/40\n",
            "22257/22257 [==============================] - 10s 446us/sample - loss: 1.2543 - accuracy: 0.6519 - val_loss: 0.9494 - val_accuracy: 0.7511\n",
            "Epoch 7/40\n",
            "22257/22257 [==============================] - 10s 445us/sample - loss: 1.1811 - accuracy: 0.6715 - val_loss: 0.9242 - val_accuracy: 0.7515\n",
            "Epoch 8/40\n",
            "22257/22257 [==============================] - 10s 440us/sample - loss: 1.1268 - accuracy: 0.6885 - val_loss: 0.8465 - val_accuracy: 0.7802\n",
            "Epoch 9/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 1.0974 - accuracy: 0.6954 - val_loss: 0.8462 - val_accuracy: 0.7756\n",
            "Epoch 10/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 1.0553 - accuracy: 0.7051 - val_loss: 0.7926 - val_accuracy: 0.7849\n",
            "Epoch 11/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 1.0285 - accuracy: 0.7098 - val_loss: 0.8224 - val_accuracy: 0.7817\n",
            "Epoch 12/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 1.0008 - accuracy: 0.7186 - val_loss: 0.7324 - val_accuracy: 0.8075\n",
            "Epoch 13/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.9735 - accuracy: 0.7246 - val_loss: 0.7618 - val_accuracy: 0.8000\n",
            "Epoch 14/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.9544 - accuracy: 0.7321 - val_loss: 0.7050 - val_accuracy: 0.8171\n",
            "Epoch 15/40\n",
            "22257/22257 [==============================] - 10s 440us/sample - loss: 0.9424 - accuracy: 0.7339 - val_loss: 0.7245 - val_accuracy: 0.8049\n",
            "Epoch 16/40\n",
            "22257/22257 [==============================] - 10s 440us/sample - loss: 0.9154 - accuracy: 0.7404 - val_loss: 0.7165 - val_accuracy: 0.8102\n",
            "Epoch 17/40\n",
            "22257/22257 [==============================] - 10s 444us/sample - loss: 0.9244 - accuracy: 0.7385 - val_loss: 0.7322 - val_accuracy: 0.8047\n",
            "Epoch 18/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.9035 - accuracy: 0.7449 - val_loss: 0.6955 - val_accuracy: 0.8138\n",
            "Epoch 19/40\n",
            "22257/22257 [==============================] - 10s 444us/sample - loss: 0.8887 - accuracy: 0.7476 - val_loss: 0.6856 - val_accuracy: 0.8156\n",
            "Epoch 20/40\n",
            "22257/22257 [==============================] - 10s 440us/sample - loss: 0.8827 - accuracy: 0.7513 - val_loss: 0.6660 - val_accuracy: 0.8255\n",
            "Epoch 21/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 0.8681 - accuracy: 0.7515 - val_loss: 0.6896 - val_accuracy: 0.8149\n",
            "Epoch 22/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 0.8613 - accuracy: 0.7542 - val_loss: 0.6821 - val_accuracy: 0.8174\n",
            "Epoch 23/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 0.8550 - accuracy: 0.7568 - val_loss: 0.6529 - val_accuracy: 0.8271\n",
            "Epoch 24/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.8516 - accuracy: 0.7545 - val_loss: 0.6686 - val_accuracy: 0.8225\n",
            "Epoch 25/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 0.8450 - accuracy: 0.7631 - val_loss: 0.6715 - val_accuracy: 0.8252\n",
            "Epoch 26/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 0.8318 - accuracy: 0.7619 - val_loss: 0.6623 - val_accuracy: 0.8253\n",
            "Epoch 27/40\n",
            "22257/22257 [==============================] - 10s 446us/sample - loss: 0.8255 - accuracy: 0.7629 - val_loss: 0.6285 - val_accuracy: 0.8336\n",
            "Epoch 28/40\n",
            "22257/22257 [==============================] - 10s 444us/sample - loss: 0.8256 - accuracy: 0.7666 - val_loss: 0.6369 - val_accuracy: 0.8347\n",
            "Epoch 29/40\n",
            "22257/22257 [==============================] - 10s 439us/sample - loss: 0.8132 - accuracy: 0.7674 - val_loss: 0.6454 - val_accuracy: 0.8282\n",
            "Epoch 30/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.8162 - accuracy: 0.7625 - val_loss: 0.6877 - val_accuracy: 0.8155\n",
            "Epoch 31/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 0.8036 - accuracy: 0.7664 - val_loss: 0.6430 - val_accuracy: 0.8282\n",
            "Epoch 32/40\n",
            "22257/22257 [==============================] - 10s 440us/sample - loss: 0.8065 - accuracy: 0.7651 - val_loss: 0.6329 - val_accuracy: 0.8316\n",
            "Epoch 33/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.7922 - accuracy: 0.7715 - val_loss: 0.7070 - val_accuracy: 0.8041\n",
            "Epoch 34/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 0.7904 - accuracy: 0.7730 - val_loss: 0.6970 - val_accuracy: 0.8165\n",
            "Epoch 35/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 0.7900 - accuracy: 0.7728 - val_loss: 0.6592 - val_accuracy: 0.8196\n",
            "Epoch 36/40\n",
            "22257/22257 [==============================] - 10s 443us/sample - loss: 0.7951 - accuracy: 0.7683 - val_loss: 0.6253 - val_accuracy: 0.8341\n",
            "Epoch 37/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 0.7792 - accuracy: 0.7733 - val_loss: 0.6301 - val_accuracy: 0.8340\n",
            "Epoch 38/40\n",
            "22257/22257 [==============================] - 10s 448us/sample - loss: 0.7786 - accuracy: 0.7714 - val_loss: 0.6197 - val_accuracy: 0.8345\n",
            "Epoch 39/40\n",
            "22257/22257 [==============================] - 10s 442us/sample - loss: 0.7696 - accuracy: 0.7784 - val_loss: 0.6503 - val_accuracy: 0.8273\n",
            "Epoch 40/40\n",
            "22257/22257 [==============================] - 10s 441us/sample - loss: 0.7631 - accuracy: 0.7778 - val_loss: 0.6166 - val_accuracy: 0.8361\n",
            "13704/13704 [==============================] - 3s 205us/sample - loss: 0.6161 - accuracy: 0.8366\n",
            "Test score: 0.6160558667088278 \n",
            "Test accuracy: 0.83661705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLYr4zR5w-EI",
        "colab_type": "code",
        "outputId": "ceaecf15-01df-4999-cc8e-3c9651cd31ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "\n",
        "# summarize history for accuracy\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', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\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', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PpiQTt8ZiETkd+K6qfsibvxVAVX8Qsc29wGZV/aG3/Y9V9ZAdhA/XWDzcDZq/dflD8MxX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\njDFJzhKBMcYkuf8f7FMO5eHq15sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mAxg3AgG44P9BMAteuc09q+C877n9wyAifOZ9VZx+RAnX/nYVX7h3BSfPmsS1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\nZ7Eq5cefMioAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clZPLfE13N5z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}