train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "train.ipynb",
      "version": "0.3.2",
      "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": [
        "[View in Colaboratory](https://colab.research.google.com/github/akash720/Facial-expression-recognition/blob/master/train.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "p33rQlQOaD0e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Importing necessary libraries and downloading files\n"
      ]
    },
    {
      "metadata": {
        "id": "NG-ft7y0bkCe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten\n",
        "from keras.utils import to_categorical"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gsMBPE6BXZeA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "# Importing dataset\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "n8XjmHBzTL3W",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.options.mode.chained_assignment = None  # default='warn'  #to suppress SettingWithCopyWarning\n",
        "\n",
        "#Reading the dataset\n",
        "dataset = pd.read_csv(\"fer2013.csv\")\n",
        "\n",
        "#Obtaining train data where usage is \"Training\"\n",
        "train = dataset[dataset[\"Usage\"] == \"Training\"]\n",
        "\n",
        "#Obtaining test data where usage is \"PublicTest\"\n",
        "test = dataset[dataset[\"Usage\"] == \"PublicTest\"]\n",
        "\n",
        "#Converting \" \" separated pixel values to list\n",
        "train['pixels'] = train['pixels'].apply(lambda image_px : np.fromstring(image_px, sep = ' '))\n",
        "test['pixels'] = test['pixels'].apply(lambda image_px : np.fromstring(image_px, sep = ' '))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tbZQVDkMYGIc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Preparing X_train, y_train, X_test, y_test"
      ]
    },
    {
      "metadata": {
        "id": "xmgMNL4bpsE4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "955bba23-4ac6-45d0-cfce-2d88050234d1"
      },
      "cell_type": "code",
      "source": [
        "X_train = train.iloc[:, 1].values\n",
        "y_train = train.iloc[:, 0].values\n",
        "X_test = test.iloc[:, 1].values\n",
        "y_test = test.iloc[:, 0].values\n",
        "\n",
        "#np.vstack stack arrays in sequence vertically (picking element row wise)\n",
        "X_train = np.vstack(X_train)\n",
        "X_test = np.vstack(X_test)\n",
        "\n",
        "\n",
        "#Reshape X_train, y_train,X_test,y_test in desired formats\n",
        "X_train = np.reshape(X_train, (X_train.shape[0],48,48,1))\n",
        "y_train = np.reshape(y_train, (y_train.shape[0],1))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0],48,48,1))\n",
        "y_test = np.reshape(y_test, (y_test.shape[0],1))\n",
        "\n",
        "print(\"shape of X_train and y_train is \" + str(X_train.shape) +\" and \" + str(y_train.shape) +\" respectively.\")\n",
        "print(\"shape of X_test and y_test is \" + str(X_test.shape) +\" and \" + str(y_test.shape) +\" respectively.\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape of X_train and y_train is (28709, 48, 48, 1) and (28709, 1) respectively.\n",
            "shape of X_test and y_test is (3589, 48, 48, 1) and (3589, 1) respectively.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "erzuNk6ycifE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Change to float datatype\n",
        "train_data = X_train.astype('float32')\n",
        "test_data = X_test.astype('float32')\n",
        "\n",
        "# Scale the data to lie between 0 to 1\n",
        "train_data /= 255\n",
        "test_data /= 255\n",
        "\n",
        "# Change the labels from integer to categorical data\n",
        "train_labels_one_hot = to_categorical(y_train)\n",
        "test_labels_one_hot = to_categorical(y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jLAloXZsayf8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Getting number of classes and  input_shape"
      ]
    },
    {
      "metadata": {
        "id": "ARcFF_p5cFkK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5c1c641c-8c42-4bee-b0c7-1af8db1f52dd"
      },
      "cell_type": "code",
      "source": [
        "# Find the unique numbers from the train labels\n",
        "\n",
        "classes = np.unique(y_train)\n",
        "nClasses = len(classes)\n",
        "print('Total number of outputs : ', nClasses)\n",
        "print('Output classes : ', classes)\n",
        "\n",
        "# Find the shape of input images and create the variable input_shape\n",
        "nRows,nCols,nDims = X_train.shape[1:]\n",
        "input_shape = (nRows, nCols, nDims)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of outputs :  7\n",
            "Output classes :  [0 1 2 3 4 5 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ql830cJUbvCU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Defining emotions name"
      ]
    },
    {
      "metadata": {
        "id": "yVaXHy3XFpUM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Defining labels \n",
        "\n",
        "def get_label(argument):\n",
        "    labels = {0:'Angry', 1:'Disgust', 2:'Fear', 3:'Happy', 4:'Sad' , 5:'Surprise', 6:'Neutral'}\n",
        "    return(labels.get(argument, \"Invalid emotion\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cYsyIgThbCfY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Plotting an image each from train and test set"
      ]
    },
    {
      "metadata": {
        "id": "E24p6ptlcIDu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "f0fc2b16-a29e-4ccb-a128-e41d322edbb9"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[4,2])\n",
        "\n",
        "# Display the first image in training data\n",
        "plt.subplot(121)\n",
        "plt.imshow(np.squeeze(X_train[2,:,:], axis = 2), cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(get_label(int(y_train[0]))))\n",
        "\n",
        "# Display the first image in testing data\n",
        "plt.subplot(122)\n",
        "plt.imshow(np.squeeze(X_test[1500,:,:], axis = 2), cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(get_label(int(y_test[1500]))))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'Ground Truth : Surprise')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X5e/nK6/5QDbrMOUZWxlTtG3rfNuGb8vPQVVCYzsP/p31TnZltXur2hhK1pFc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\nUBtpwUhISKiNtGAkJCTUxv8BrrpLl4l4AuMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f37079de208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "s10xyLnfY8Fw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Defining model architecture"
      ]
    },
    {
      "metadata": {
        "id": "uLdj9lQJPboo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def createModel():\n",
        "    \n",
        "    #Model Initialization\n",
        "    model = Sequential() \n",
        "    \n",
        "    #Adding Input Layer\n",
        "    model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=input_shape))\n",
        "    \n",
        "    #Adding more layers\n",
        "    model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        " \n",
        "    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        " \n",
        "    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "  \n",
        "    #Flattening\n",
        "    model.add(Flatten())\n",
        "    \n",
        "    #Adding fully connected layer\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    \n",
        "    model.add(Dropout(0.6))\n",
        "    \n",
        "    #Adding Output Layer\n",
        "    model.add(Dense(nClasses, activation='softmax'))\n",
        "     \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NQfeyGnGZK-2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Creating and training 1st model"
      ]
    },
    {
      "metadata": {
        "id": "eeqUdkV8Zs_M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "outputId": "db2b7059-f9d3-4fa6-b31a-14ec48e05089"
      },
      "cell_type": "code",
      "source": [
        "#Creating 1st model\n",
        "\n",
        "model1 = createModel()\n",
        "batch_size = 256\n",
        "epochs = 100\n",
        "model1.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model1.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_55 (Conv2D)           (None, 48, 48, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_56 (Conv2D)           (None, 46, 46, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_30 (MaxPooling (None, 23, 23, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_35 (Dropout)         (None, 23, 23, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_57 (Conv2D)           (None, 23, 23, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_58 (Conv2D)           (None, 21, 21, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_31 (MaxPooling (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_36 (Dropout)         (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_59 (Conv2D)           (None, 10, 10, 64)        36928     \n",
            "_________________________________________________________________\n",
            "conv2d_60 (Conv2D)           (None, 8, 8, 64)          36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_32 (MaxPooling (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_37 (Dropout)         (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 512)               524800    \n",
            "_________________________________________________________________\n",
            "dropout_38 (Dropout)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 7)                 3591      \n",
            "=================================================================\n",
            "Total params: 667,239\n",
            "Trainable params: 667,239\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BrG5Xd-HdTUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3528
        },
        "outputId": "2a30a0f2-5bc2-475d-f281-da4b5e911eb9"
      },
      "cell_type": "code",
      "source": [
        "#Training our 1st model\n",
        "\n",
        "history = model1.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epochs, verbose=1, \n",
        "                   validation_data=(test_data, test_labels_one_hot))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 28709 samples, validate on 3589 samples\n",
            "Epoch 1/100\n",
            "28709/28709 [==============================] - 18s 610us/step - loss: 1.8253 - acc: 0.2426 - val_loss: 1.8045 - val_acc: 0.2561\n",
            "Epoch 2/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 1.7123 - acc: 0.3085 - val_loss: 1.6334 - val_acc: 0.3539\n",
            "Epoch 3/100\n",
            "28709/28709 [==============================] - 12s 407us/step - loss: 1.5560 - acc: 0.3942 - val_loss: 1.4801 - val_acc: 0.4207\n",
            "Epoch 4/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 1.4491 - acc: 0.4431 - val_loss: 1.3861 - val_acc: 0.4625\n",
            "Epoch 5/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 1.3727 - acc: 0.4734 - val_loss: 1.3504 - val_acc: 0.4812\n",
            "Epoch 6/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 1.3043 - acc: 0.5010 - val_loss: 1.3111 - val_acc: 0.4890\n",
            "Epoch 7/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 1.2610 - acc: 0.5196 - val_loss: 1.2788 - val_acc: 0.5132\n",
            "Epoch 8/100\n",
            "28709/28709 [==============================] - 12s 407us/step - loss: 1.2201 - acc: 0.5338 - val_loss: 1.2553 - val_acc: 0.5054\n",
            "Epoch 9/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 1.1851 - acc: 0.5504 - val_loss: 1.2001 - val_acc: 0.5411\n",
            "Epoch 10/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 1.1577 - acc: 0.5611 - val_loss: 1.2590 - val_acc: 0.5199\n",
            "Epoch 11/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 1.1335 - acc: 0.5701 - val_loss: 1.2150 - val_acc: 0.5453\n",
            "Epoch 12/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 1.1062 - acc: 0.5816 - val_loss: 1.1489 - val_acc: 0.5637\n",
            "Epoch 13/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 1.0849 - acc: 0.5908 - val_loss: 1.1447 - val_acc: 0.5731\n",
            "Epoch 14/100\n",
            "28709/28709 [==============================] - 12s 403us/step - loss: 1.0673 - acc: 0.5972 - val_loss: 1.2038 - val_acc: 0.5483\n",
            "Epoch 15/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 1.0461 - acc: 0.6073 - val_loss: 1.1383 - val_acc: 0.5692\n",
            "Epoch 16/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 1.0265 - acc: 0.6112 - val_loss: 1.1984 - val_acc: 0.5511\n",
            "Epoch 17/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 1.0162 - acc: 0.6151 - val_loss: 1.1303 - val_acc: 0.5706\n",
            "Epoch 18/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.9906 - acc: 0.6252 - val_loss: 1.2706 - val_acc: 0.5567\n",
            "Epoch 19/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.9803 - acc: 0.6279 - val_loss: 1.0970 - val_acc: 0.5885\n",
            "Epoch 20/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.9605 - acc: 0.6386 - val_loss: 1.2638 - val_acc: 0.5534\n",
            "Epoch 21/100\n",
            "28709/28709 [==============================] - 12s 401us/step - loss: 0.9474 - acc: 0.6442 - val_loss: 1.1349 - val_acc: 0.6010\n",
            "Epoch 22/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.9269 - acc: 0.6488 - val_loss: 1.0905 - val_acc: 0.6035\n",
            "Epoch 23/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.9190 - acc: 0.6536 - val_loss: 1.1688 - val_acc: 0.5745\n",
            "Epoch 24/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.9112 - acc: 0.6588 - val_loss: 1.0925 - val_acc: 0.5965\n",
            "Epoch 25/100\n",
            "28709/28709 [==============================] - 12s 401us/step - loss: 0.8934 - acc: 0.6639 - val_loss: 1.1803 - val_acc: 0.5868\n",
            "Epoch 26/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.8838 - acc: 0.6671 - val_loss: 1.0985 - val_acc: 0.5999\n",
            "Epoch 27/100\n",
            "28709/28709 [==============================] - 12s 401us/step - loss: 0.8729 - acc: 0.6713 - val_loss: 1.1633 - val_acc: 0.6063\n",
            "Epoch 28/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.8662 - acc: 0.6705 - val_loss: 1.1064 - val_acc: 0.5952\n",
            "Epoch 29/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.8559 - acc: 0.6823 - val_loss: 1.1245 - val_acc: 0.6021\n",
            "Epoch 30/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.8386 - acc: 0.6851 - val_loss: 1.1078 - val_acc: 0.5924\n",
            "Epoch 31/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.8362 - acc: 0.6880 - val_loss: 1.1054 - val_acc: 0.6060\n",
            "Epoch 32/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.8207 - acc: 0.6903 - val_loss: 1.0932 - val_acc: 0.6063\n",
            "Epoch 33/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.8187 - acc: 0.6946 - val_loss: 1.1865 - val_acc: 0.5996\n",
            "Epoch 34/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.7989 - acc: 0.7012 - val_loss: 1.2085 - val_acc: 0.6074\n",
            "Epoch 35/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.7994 - acc: 0.7004 - val_loss: 1.1866 - val_acc: 0.6113\n",
            "Epoch 36/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.7925 - acc: 0.7023 - val_loss: 1.1415 - val_acc: 0.6060\n",
            "Epoch 37/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.7912 - acc: 0.7028 - val_loss: 1.0888 - val_acc: 0.6021\n",
            "Epoch 38/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.7855 - acc: 0.7065 - val_loss: 1.2223 - val_acc: 0.6147\n",
            "Epoch 39/100\n",
            "28709/28709 [==============================] - 11s 398us/step - loss: 0.7794 - acc: 0.7074 - val_loss: 1.1836 - val_acc: 0.5882\n",
            "Epoch 40/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.7676 - acc: 0.7117 - val_loss: 1.1540 - val_acc: 0.5921\n",
            "Epoch 41/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.7682 - acc: 0.7118 - val_loss: 1.1807 - val_acc: 0.6147\n",
            "Epoch 42/100\n",
            "28709/28709 [==============================] - 12s 409us/step - loss: 0.7660 - acc: 0.7132 - val_loss: 1.2066 - val_acc: 0.5720\n",
            "Epoch 43/100\n",
            "28709/28709 [==============================] - 12s 409us/step - loss: 0.7612 - acc: 0.7117 - val_loss: 1.2156 - val_acc: 0.5996\n",
            "Epoch 44/100\n",
            "28709/28709 [==============================] - 12s 412us/step - loss: 0.7497 - acc: 0.7197 - val_loss: 1.3979 - val_acc: 0.5793\n",
            "Epoch 45/100\n",
            "28709/28709 [==============================] - 12s 412us/step - loss: 0.7417 - acc: 0.7214 - val_loss: 1.0908 - val_acc: 0.6172\n",
            "Epoch 46/100\n",
            "28709/28709 [==============================] - 12s 410us/step - loss: 0.7393 - acc: 0.7248 - val_loss: 1.2434 - val_acc: 0.6141\n",
            "Epoch 47/100\n",
            "28709/28709 [==============================] - 12s 412us/step - loss: 0.7324 - acc: 0.7265 - val_loss: 1.1985 - val_acc: 0.6197\n",
            "Epoch 48/100\n",
            "28709/28709 [==============================] - 12s 412us/step - loss: 0.7261 - acc: 0.7297 - val_loss: 1.1528 - val_acc: 0.5918\n",
            "Epoch 49/100\n",
            "28709/28709 [==============================] - 12s 413us/step - loss: 0.7181 - acc: 0.7315 - val_loss: 1.2435 - val_acc: 0.6135\n",
            "Epoch 50/100\n",
            "28709/28709 [==============================] - 12s 413us/step - loss: 0.7190 - acc: 0.7323 - val_loss: 1.1264 - val_acc: 0.6060\n",
            "Epoch 51/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.7095 - acc: 0.7343 - val_loss: 1.1666 - val_acc: 0.6016\n",
            "Epoch 52/100\n",
            "28709/28709 [==============================] - 12s 410us/step - loss: 0.7157 - acc: 0.7342 - val_loss: 1.1962 - val_acc: 0.5904\n",
            "Epoch 53/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.7169 - acc: 0.7341 - val_loss: 1.0670 - val_acc: 0.6147\n",
            "Epoch 54/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 0.7065 - acc: 0.7378 - val_loss: 1.1994 - val_acc: 0.6121\n",
            "Epoch 55/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.7078 - acc: 0.7397 - val_loss: 1.2656 - val_acc: 0.6158\n",
            "Epoch 56/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6983 - acc: 0.7405 - val_loss: 1.2967 - val_acc: 0.5996\n",
            "Epoch 57/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.7006 - acc: 0.7402 - val_loss: 1.1854 - val_acc: 0.6152\n",
            "Epoch 58/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6970 - acc: 0.7402 - val_loss: 1.1674 - val_acc: 0.6030\n",
            "Epoch 59/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 0.6860 - acc: 0.7454 - val_loss: 1.1767 - val_acc: 0.6133\n",
            "Epoch 60/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 0.6910 - acc: 0.7389 - val_loss: 1.2671 - val_acc: 0.6119\n",
            "Epoch 61/100\n",
            "28709/28709 [==============================] - 12s 403us/step - loss: 0.6932 - acc: 0.7433 - val_loss: 1.2939 - val_acc: 0.6077\n",
            "Epoch 62/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6887 - acc: 0.7453 - val_loss: 1.3656 - val_acc: 0.6144\n",
            "Epoch 63/100\n",
            "28709/28709 [==============================] - 12s 401us/step - loss: 0.6907 - acc: 0.7439 - val_loss: 1.4743 - val_acc: 0.6230\n",
            "Epoch 64/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6793 - acc: 0.7489 - val_loss: 1.2701 - val_acc: 0.6199\n",
            "Epoch 65/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.6710 - acc: 0.7537 - val_loss: 1.1830 - val_acc: 0.6077\n",
            "Epoch 66/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.6825 - acc: 0.7465 - val_loss: 1.5220 - val_acc: 0.5756\n",
            "Epoch 67/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.6747 - acc: 0.7518 - val_loss: 1.2327 - val_acc: 0.6147\n",
            "Epoch 68/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.6725 - acc: 0.7515 - val_loss: 1.1740 - val_acc: 0.5940\n",
            "Epoch 69/100\n",
            "28709/28709 [==============================] - 12s 403us/step - loss: 0.6711 - acc: 0.7502 - val_loss: 1.2638 - val_acc: 0.6138\n",
            "Epoch 70/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6695 - acc: 0.7526 - val_loss: 1.1926 - val_acc: 0.6108\n",
            "Epoch 71/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6665 - acc: 0.7527 - val_loss: 1.4561 - val_acc: 0.6046\n",
            "Epoch 72/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6668 - acc: 0.7552 - val_loss: 1.2017 - val_acc: 0.6007\n",
            "Epoch 73/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6728 - acc: 0.7552 - val_loss: 1.2770 - val_acc: 0.6088\n",
            "Epoch 74/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6552 - acc: 0.7584 - val_loss: 1.2147 - val_acc: 0.6096\n",
            "Epoch 75/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6560 - acc: 0.7559 - val_loss: 1.2394 - val_acc: 0.6230\n",
            "Epoch 76/100\n",
            "28709/28709 [==============================] - 12s 403us/step - loss: 0.6604 - acc: 0.7593 - val_loss: 1.5306 - val_acc: 0.6082\n",
            "Epoch 77/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6556 - acc: 0.7596 - val_loss: 1.7169 - val_acc: 0.6013\n",
            "Epoch 78/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6579 - acc: 0.7578 - val_loss: 1.2137 - val_acc: 0.6147\n",
            "Epoch 79/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6558 - acc: 0.7575 - val_loss: 1.4767 - val_acc: 0.6155\n",
            "Epoch 80/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6558 - acc: 0.7577 - val_loss: 1.2074 - val_acc: 0.6121\n",
            "Epoch 81/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6488 - acc: 0.7602 - val_loss: 1.5341 - val_acc: 0.6088\n",
            "Epoch 82/100\n",
            "28709/28709 [==============================] - 12s 406us/step - loss: 0.6588 - acc: 0.7598 - val_loss: 1.0893 - val_acc: 0.6163\n",
            "Epoch 83/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6563 - acc: 0.7611 - val_loss: 1.3760 - val_acc: 0.6172\n",
            "Epoch 84/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6441 - acc: 0.7619 - val_loss: 1.2017 - val_acc: 0.6049\n",
            "Epoch 85/100\n",
            "28709/28709 [==============================] - 12s 405us/step - loss: 0.6494 - acc: 0.7643 - val_loss: 1.2591 - val_acc: 0.6102\n",
            "Epoch 86/100\n",
            "28709/28709 [==============================] - 12s 404us/step - loss: 0.6422 - acc: 0.7660 - val_loss: 1.2882 - val_acc: 0.5960\n",
            "Epoch 87/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6445 - acc: 0.7625 - val_loss: 1.3386 - val_acc: 0.6108\n",
            "Epoch 88/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6430 - acc: 0.7658 - val_loss: 1.3504 - val_acc: 0.5921\n",
            "Epoch 89/100\n",
            "28709/28709 [==============================] - 12s 403us/step - loss: 0.6440 - acc: 0.7660 - val_loss: 1.3946 - val_acc: 0.6069\n",
            "Epoch 90/100\n",
            "28709/28709 [==============================] - 12s 403us/step - loss: 0.6495 - acc: 0.7637 - val_loss: 1.3443 - val_acc: 0.6208\n",
            "Epoch 91/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.6466 - acc: 0.7656 - val_loss: 1.1518 - val_acc: 0.6082\n",
            "Epoch 92/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6422 - acc: 0.7661 - val_loss: 1.3942 - val_acc: 0.6216\n",
            "Epoch 93/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.6447 - acc: 0.7637 - val_loss: 1.2502 - val_acc: 0.6138\n",
            "Epoch 94/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6494 - acc: 0.7647 - val_loss: 1.3138 - val_acc: 0.6016\n",
            "Epoch 95/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6383 - acc: 0.7676 - val_loss: 1.5614 - val_acc: 0.6141\n",
            "Epoch 96/100\n",
            "28709/28709 [==============================] - 12s 402us/step - loss: 0.6377 - acc: 0.7702 - val_loss: 1.1652 - val_acc: 0.5960\n",
            "Epoch 97/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.6362 - acc: 0.7676 - val_loss: 1.6270 - val_acc: 0.6116\n",
            "Epoch 98/100\n",
            "28709/28709 [==============================] - 11s 399us/step - loss: 0.6406 - acc: 0.7677 - val_loss: 1.5629 - val_acc: 0.6088\n",
            "Epoch 99/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6311 - acc: 0.7717 - val_loss: 1.4131 - val_acc: 0.6152\n",
            "Epoch 100/100\n",
            "28709/28709 [==============================] - 11s 400us/step - loss: 0.6303 - acc: 0.7718 - val_loss: 1.4771 - val_acc: 0.6049\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bQm4EBjAZQ7c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Evaluating 1st model and plotting curves"
      ]
    },
    {
      "metadata": {
        "id": "j8Zf-86jd7Hk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "85903045-9bd6-4910-c554-c654dc8dbbb6"
      },
      "cell_type": "code",
      "source": [
        "#Evaluating model performance\n",
        "\n",
        "model1.evaluate(test_data, test_labels_one_hot)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 1s 355us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4770588522071657, 0.6049038729534139]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "metadata": {
        "id": "mWVMn94TeCOy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "outputId": "2305ebf4-3969-4eeb-a9ba-35eea092dff1"
      },
      "cell_type": "code",
      "source": [
        "#Plotting accuracy and loss curves for 1st model\n",
        "\n",
        "# Loss Curves\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)\n",
        " \n",
        "# Accuracy Curves\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['acc'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_acc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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haUI7X1DQgcLCUm67LYNnnjGza+/jRm7kAXbfegcVV12byMulLMH7KDQNuY+J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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f367fc58550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J98KLpc5clAmbNin07JnKrl1mvubxxx0MGOBn8GA/9esfPwMjDR3qZP588+E6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\nf3rMej6oPTvXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f367fc0f7f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gwK03BE6WYtY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ">***As you can see that our model performs well on training set but fails to do so on test set, there must be overfitting. To remove overfitting, we can do data augmentation.***\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "jv8pXMQwZZoI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data augmentation "
      ]
    },
    {
      "metadata": {
        "id": "Lr0dW7KxeHbc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "        zoom_range=0.2,          # randomly zoom into images\n",
        "        rotation_range=10,       # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        width_shift_range=0.1,   # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "        horizontal_flip=True,    # randomly flip images\n",
        "        vertical_flip=False)     # randomly flip images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O7Iox6xhbQpg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Creating and training 2nd model"
      ]
    },
    {
      "metadata": {
        "id": "eTiS-2KBe5-S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3510
        },
        "outputId": "fe59b7e3-8927-44e2-ec66-5fb2f1427f7b"
      },
      "cell_type": "code",
      "source": [
        "#Creating 2nd model and training(fitting)\n",
        "\n",
        "model2 = createModel()\n",
        "model2.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        " \n",
        "batch_size = 256\n",
        "epochs = 100\n",
        "\n",
        "# Fit the model on the batches generated by datagen.flow().\n",
        "history2 = model2.fit_generator(datagen.flow(train_data, train_labels_one_hot, batch_size=batch_size),\n",
        "                              steps_per_epoch=int(np.ceil(train_data.shape[0] / float(batch_size))),\n",
        "                              epochs=epochs,\n",
        "                              validation_data=(test_data, test_labels_one_hot),\n",
        "                              )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "113/113 [==============================] - 22s 197ms/step - loss: 1.8909 - acc: 0.2420 - val_loss: 1.7932 - val_acc: 0.2502\n",
            "Epoch 2/100\n",
            "113/113 [==============================] - 16s 141ms/step - loss: 1.7688 - acc: 0.2700 - val_loss: 1.6760 - val_acc: 0.3174\n",
            "Epoch 3/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.7021 - acc: 0.3121 - val_loss: 1.5655 - val_acc: 0.3982\n",
            "Epoch 4/100\n",
            "113/113 [==============================] - 17s 147ms/step - loss: 1.6341 - acc: 0.3546 - val_loss: 1.5058 - val_acc: 0.4163\n",
            "Epoch 5/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.5543 - acc: 0.3935 - val_loss: 1.4159 - val_acc: 0.4439\n",
            "Epoch 6/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.4931 - acc: 0.4178 - val_loss: 1.3745 - val_acc: 0.4592\n",
            "Epoch 7/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.4377 - acc: 0.4425 - val_loss: 1.3016 - val_acc: 0.4946\n",
            "Epoch 8/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.4007 - acc: 0.4589 - val_loss: 1.2565 - val_acc: 0.5166\n",
            "Epoch 9/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.3561 - acc: 0.4791 - val_loss: 1.2487 - val_acc: 0.5135\n",
            "Epoch 10/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.3321 - acc: 0.4898 - val_loss: 1.2566 - val_acc: 0.5205\n",
            "Epoch 11/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.3141 - acc: 0.4964 - val_loss: 1.1600 - val_acc: 0.5598\n",
            "Epoch 12/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.2919 - acc: 0.5036 - val_loss: 1.1482 - val_acc: 0.5584\n",
            "Epoch 13/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.2688 - acc: 0.5136 - val_loss: 1.1525 - val_acc: 0.5617\n",
            "Epoch 14/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.2548 - acc: 0.5238 - val_loss: 1.1513 - val_acc: 0.5631\n",
            "Epoch 15/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.2428 - acc: 0.5282 - val_loss: 1.1313 - val_acc: 0.5634\n",
            "Epoch 16/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.2304 - acc: 0.5321 - val_loss: 1.1547 - val_acc: 0.5531\n",
            "Epoch 17/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.2128 - acc: 0.5407 - val_loss: 1.0905 - val_acc: 0.5843\n",
            "Epoch 18/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.2058 - acc: 0.5432 - val_loss: 1.0990 - val_acc: 0.5815\n",
            "Epoch 19/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.1960 - acc: 0.5452 - val_loss: 1.1257 - val_acc: 0.5773\n",
            "Epoch 20/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.1940 - acc: 0.5485 - val_loss: 1.0948 - val_acc: 0.5829\n",
            "Epoch 21/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.1773 - acc: 0.5586 - val_loss: 1.0805 - val_acc: 0.5910\n",
            "Epoch 22/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1761 - acc: 0.5562 - val_loss: 1.0709 - val_acc: 0.5949\n",
            "Epoch 23/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1664 - acc: 0.5564 - val_loss: 1.0609 - val_acc: 0.6024\n",
            "Epoch 24/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1680 - acc: 0.5586 - val_loss: 1.0596 - val_acc: 0.6038\n",
            "Epoch 25/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1579 - acc: 0.5632 - val_loss: 1.0887 - val_acc: 0.5988\n",
            "Epoch 26/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1503 - acc: 0.5664 - val_loss: 1.0742 - val_acc: 0.5974\n",
            "Epoch 27/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1468 - acc: 0.5685 - val_loss: 1.0552 - val_acc: 0.6080\n",
            "Epoch 28/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.1417 - acc: 0.5701 - val_loss: 1.0457 - val_acc: 0.5996\n",
            "Epoch 29/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1308 - acc: 0.5767 - val_loss: 1.0399 - val_acc: 0.6035\n",
            "Epoch 30/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1341 - acc: 0.5761 - val_loss: 1.0742 - val_acc: 0.5949\n",
            "Epoch 31/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1313 - acc: 0.5729 - val_loss: 1.0368 - val_acc: 0.6177\n",
            "Epoch 32/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1204 - acc: 0.5795 - val_loss: 1.0446 - val_acc: 0.6160\n",
            "Epoch 33/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1237 - acc: 0.5803 - val_loss: 1.0311 - val_acc: 0.6135\n",
            "Epoch 34/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.1210 - acc: 0.5792 - val_loss: 1.0363 - val_acc: 0.6141\n",
            "Epoch 35/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1104 - acc: 0.5830 - val_loss: 1.0598 - val_acc: 0.6052\n",
            "Epoch 36/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1039 - acc: 0.5837 - val_loss: 1.0472 - val_acc: 0.6010\n",
            "Epoch 37/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.1070 - acc: 0.5825 - val_loss: 1.0423 - val_acc: 0.6105\n",
            "Epoch 38/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.1050 - acc: 0.5893 - val_loss: 1.0434 - val_acc: 0.6110\n",
            "Epoch 39/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0956 - acc: 0.5897 - val_loss: 1.0161 - val_acc: 0.6158\n",
            "Epoch 40/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0983 - acc: 0.5857 - val_loss: 1.0289 - val_acc: 0.6144\n",
            "Epoch 41/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.1025 - acc: 0.5872 - val_loss: 1.0375 - val_acc: 0.6177\n",
            "Epoch 42/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0902 - acc: 0.5915 - val_loss: 1.0263 - val_acc: 0.6174\n",
            "Epoch 43/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0911 - acc: 0.5919 - val_loss: 1.0311 - val_acc: 0.6183\n",
            "Epoch 44/100\n",
            "113/113 [==============================] - 17s 146ms/step - loss: 1.0842 - acc: 0.5920 - val_loss: 1.0613 - val_acc: 0.6102\n",
            "Epoch 45/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0950 - acc: 0.5891 - val_loss: 1.0001 - val_acc: 0.6264\n",
            "Epoch 46/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0887 - acc: 0.5929 - val_loss: 1.0326 - val_acc: 0.6186\n",
            "Epoch 47/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0826 - acc: 0.5951 - val_loss: 1.0251 - val_acc: 0.6188\n",
            "Epoch 48/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0864 - acc: 0.5959 - val_loss: 1.0318 - val_acc: 0.6166\n",
            "Epoch 49/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0765 - acc: 0.5952 - val_loss: 1.0193 - val_acc: 0.6180\n",
            "Epoch 50/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0797 - acc: 0.5967 - val_loss: 1.0870 - val_acc: 0.6021\n",
            "Epoch 51/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0806 - acc: 0.5952 - val_loss: 1.0173 - val_acc: 0.6219\n",
            "Epoch 52/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0791 - acc: 0.5945 - val_loss: 1.0138 - val_acc: 0.6186\n",
            "Epoch 53/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0751 - acc: 0.5970 - val_loss: 1.0000 - val_acc: 0.6191\n",
            "Epoch 54/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0727 - acc: 0.5949 - val_loss: 1.0583 - val_acc: 0.6096\n",
            "Epoch 55/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.0693 - acc: 0.6004 - val_loss: 1.0254 - val_acc: 0.6191\n",
            "Epoch 56/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0681 - acc: 0.6004 - val_loss: 1.0429 - val_acc: 0.6127\n",
            "Epoch 57/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0764 - acc: 0.5967 - val_loss: 1.0261 - val_acc: 0.6197\n",
            "Epoch 58/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0658 - acc: 0.6018 - val_loss: 1.0227 - val_acc: 0.6216\n",
            "Epoch 59/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0693 - acc: 0.5994 - val_loss: 1.0250 - val_acc: 0.6205\n",
            "Epoch 60/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0695 - acc: 0.6013 - val_loss: 1.0102 - val_acc: 0.6188\n",
            "Epoch 61/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0596 - acc: 0.6040 - val_loss: 1.0358 - val_acc: 0.6130\n",
            "Epoch 62/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0685 - acc: 0.5979 - val_loss: 1.0748 - val_acc: 0.6177\n",
            "Epoch 63/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0674 - acc: 0.6045 - val_loss: 0.9957 - val_acc: 0.6317\n",
            "Epoch 64/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0651 - acc: 0.5994 - val_loss: 1.0201 - val_acc: 0.6319\n",
            "Epoch 65/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0662 - acc: 0.6035 - val_loss: 1.0205 - val_acc: 0.6289\n",
            "Epoch 66/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0622 - acc: 0.6028 - val_loss: 1.0145 - val_acc: 0.6364\n",
            "Epoch 67/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0569 - acc: 0.6064 - val_loss: 1.0201 - val_acc: 0.6135\n",
            "Epoch 68/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0633 - acc: 0.6068 - val_loss: 0.9905 - val_acc: 0.6280\n",
            "Epoch 69/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0554 - acc: 0.6094 - val_loss: 0.9970 - val_acc: 0.6347\n",
            "Epoch 70/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0571 - acc: 0.6035 - val_loss: 1.0270 - val_acc: 0.6191\n",
            "Epoch 71/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0581 - acc: 0.6044 - val_loss: 1.0002 - val_acc: 0.6227\n",
            "Epoch 72/100\n",
            "113/113 [==============================] - 16s 141ms/step - loss: 1.0583 - acc: 0.6052 - val_loss: 1.0463 - val_acc: 0.6163\n",
            "Epoch 73/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0558 - acc: 0.6099 - val_loss: 0.9889 - val_acc: 0.6347\n",
            "Epoch 74/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0539 - acc: 0.6080 - val_loss: 1.0120 - val_acc: 0.6138\n",
            "Epoch 75/100\n",
            "113/113 [==============================] - 16s 141ms/step - loss: 1.0568 - acc: 0.6058 - val_loss: 1.0435 - val_acc: 0.6247\n",
            "Epoch 76/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0560 - acc: 0.6054 - val_loss: 1.1515 - val_acc: 0.5896\n",
            "Epoch 77/100\n",
            "113/113 [==============================] - 16s 141ms/step - loss: 1.0481 - acc: 0.6086 - val_loss: 0.9964 - val_acc: 0.6289\n",
            "Epoch 78/100\n",
            "113/113 [==============================] - 16s 141ms/step - loss: 1.0507 - acc: 0.6077 - val_loss: 1.0121 - val_acc: 0.6275\n",
            "Epoch 79/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0544 - acc: 0.6077 - val_loss: 0.9960 - val_acc: 0.6244\n",
            "Epoch 80/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0572 - acc: 0.6046 - val_loss: 1.0121 - val_acc: 0.6300\n",
            "Epoch 81/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0521 - acc: 0.6063 - val_loss: 1.0262 - val_acc: 0.6275\n",
            "Epoch 82/100\n",
            "113/113 [==============================] - 17s 146ms/step - loss: 1.0567 - acc: 0.6065 - val_loss: 1.0152 - val_acc: 0.6319\n",
            "Epoch 83/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0556 - acc: 0.6061 - val_loss: 1.0061 - val_acc: 0.6314\n",
            "Epoch 84/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0520 - acc: 0.6087 - val_loss: 0.9787 - val_acc: 0.6436\n",
            "Epoch 85/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0468 - acc: 0.6093 - val_loss: 1.0068 - val_acc: 0.6411\n",
            "Epoch 86/100\n",
            "113/113 [==============================] - 16s 146ms/step - loss: 1.0445 - acc: 0.6099 - val_loss: 0.9770 - val_acc: 0.6317\n",
            "Epoch 87/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0467 - acc: 0.6092 - val_loss: 0.9986 - val_acc: 0.6344\n",
            "Epoch 88/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0491 - acc: 0.6096 - val_loss: 1.0003 - val_acc: 0.6467\n",
            "Epoch 89/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0466 - acc: 0.6089 - val_loss: 1.0192 - val_acc: 0.6244\n",
            "Epoch 90/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0524 - acc: 0.6105 - val_loss: 0.9885 - val_acc: 0.6317\n",
            "Epoch 91/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0407 - acc: 0.6118 - val_loss: 1.0395 - val_acc: 0.6119\n",
            "Epoch 92/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0457 - acc: 0.6085 - val_loss: 0.9957 - val_acc: 0.6303\n",
            "Epoch 93/100\n",
            "113/113 [==============================] - 16s 142ms/step - loss: 1.0533 - acc: 0.6073 - val_loss: 1.0093 - val_acc: 0.6213\n",
            "Epoch 94/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0482 - acc: 0.6102 - val_loss: 0.9973 - val_acc: 0.6367\n",
            "Epoch 95/100\n",
            "113/113 [==============================] - 16s 143ms/step - loss: 1.0439 - acc: 0.6099 - val_loss: 0.9892 - val_acc: 0.6305\n",
            "Epoch 96/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0433 - acc: 0.6106 - val_loss: 1.0406 - val_acc: 0.6297\n",
            "Epoch 97/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0434 - acc: 0.6128 - val_loss: 0.9897 - val_acc: 0.6350\n",
            "Epoch 98/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0390 - acc: 0.6112 - val_loss: 1.0033 - val_acc: 0.6342\n",
            "Epoch 99/100\n",
            "113/113 [==============================] - 16s 145ms/step - loss: 1.0485 - acc: 0.6098 - val_loss: 1.0040 - val_acc: 0.6286\n",
            "Epoch 100/100\n",
            "113/113 [==============================] - 16s 144ms/step - loss: 1.0447 - acc: 0.6119 - val_loss: 1.0248 - val_acc: 0.6317\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q_gIIyLDbZws",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Evaluating 2nd model and plotting curves"
      ]
    },
    {
      "metadata": {
        "id": "y-WznumziU3k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "436d2015-c65e-456d-ce8d-ef6becb728e3"
      },
      "cell_type": "code",
      "source": [
        "#Evaluating model performance\n",
        "\n",
        "model2.evaluate(test_data, test_labels_one_hot)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 1s 307us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.024771803034551, 0.6316522708607437]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "id": "_WOF1I14foGu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "outputId": "f1ff0af2-54e2-4046-85b3-dc5f661c1a89"
      },
      "cell_type": "code",
      "source": [
        "#Plotting accuracy and loss curves for 2nd model\n",
        "\n",
        "# Loss Curves\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history2.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history2.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)\n",
        " \n",
        "# Accuracy Curves\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history2.history['acc'],'r',linewidth=3.0)\n",
        "plt.plot(history2.history['val_acc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5,1,'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2nId2xlPygHr7tq0YJY/QExERiTRK6H6aNIGEBO/ic3l5Btl5dpxd/aav/aJm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\nIpEUAqSgSyQSiURSCPg/A2/svfHJRL8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f36a6dd4b70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b7r470nu/d9/tZPLkNJo3930GPXo4WbnSFOD+Tqd8eY2lS225ChDTdRg6NIzv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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f369f706160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2b6yGRcPblXE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Saving our model"
      ]
    },
    {
      "metadata": {
        "id": "b3QawkGqZLes",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Saving our model\n",
        "\n",
        "model2.save('my_model3.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}