{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "complete notebook.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p33rQlQOaD0e",
        "colab_type": "text"
      },
      "source": [
        "# Importing necessary libraries and downloading files\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NG-ft7y0bkCe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4a0170e3-85f5-48e6-87a1-8f615f1347ab"
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCgrcnzeIxob",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! wget https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml\n",
        "! wget https://raw.githubusercontent.com/oarriaga/face_classification/master/trained_models/emotion_models/fer2013_mini_XCEPTION.110-0.65.hdf5\n",
        "! wget -O surprise.jpg https://images.unsplash.com/photo-1485546246426-74dc88dec4d9?ixlib=rb-1.2.1\n",
        "\n",
        "! pip install -q kaggle\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download nicolejyt/facialexpressionrecognition\n",
        "! unzip facialexpressionrecognition.zip "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsMBPE6BXZeA",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "# Importing dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8XjmHBzTL3W",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbZQVDkMYGIc",
        "colab_type": "text"
      },
      "source": [
        "# Preparing X_train, y_train, X_test, y_test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmgMNL4bpsE4",
        "colab_type": "code",
        "outputId": "b25ec335-c1cc-4373-99f0-10049bd8c219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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",
        "'''\n",
        "np.vstack stack element in sequence vertically (eg: [2, 3, 4] --> array([[2],\n",
        "                                                                         [3],\n",
        "                                                                         [4]])\n",
        "                                               )\n",
        "'''\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": 4,
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erzuNk6ycifE",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLAloXZsayf8",
        "colab_type": "text"
      },
      "source": [
        "# Getting number of classes and  input_shape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARcFF_p5cFkK",
        "colab_type": "code",
        "outputId": "744a25e4-4757-47f5-ba0a-84579adac2d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of outputs :  7\n",
            "Output classes :  [0 1 2 3 4 5 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ql830cJUbvCU",
        "colab_type": "text"
      },
      "source": [
        "# Defining emotions name"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVaXHy3XFpUM",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYsyIgThbCfY",
        "colab_type": "text"
      },
      "source": [
        "# Plotting an image each from train and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E24p6ptlcIDu",
        "colab_type": "code",
        "outputId": "afc9e290-8b01-4381-9a2e-e1384c64d79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "source": [
        "# resize plots\n",
        "plt.figure(figsize=[6,4])\n",
        "\n",
        "# Display random image from training data\n",
        "plt.subplot(1,3,1)\n",
        "plt.imshow(np.squeeze(X_train[7]), cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(get_label(int(y_train[7]))))\n",
        "\n",
        "# Display random image from testing data\n",
        "plt.subplot(1,3,3)\n",
        "plt.imshow(np.squeeze(X_test[1500]), cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(get_label(int(y_test[1500]))))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ground Truth : Surprise')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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epQHr2fMVC2Qd+NUVoTUjX5F9eiY1e6BPXcNH1UnGtOaDn+/X+fmznq8P4e8z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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s10xyLnfY8Fw",
        "colab_type": "text"
      },
      "source": [
        "# Defining model architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLdj9lQJPboo",
        "colab_type": "code",
        "colab": {}
      },
      "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(128, (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",
        "    model.add(Dropout(0.4))\n",
        "    \n",
        "    #Adding Output Layer\n",
        "    model.add(Dense(nClasses, activation='softmax'))\n",
        "     \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQfeyGnGZK-2",
        "colab_type": "text"
      },
      "source": [
        "# Creating and training 1st model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeqUdkV8Zs_M",
        "colab_type": "code",
        "outputId": "6e710b13-d182-43d9-9602-a2b7328ab337",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        }
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 48, 48, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 46, 46, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 23, 23, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 23, 23, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 23, 23, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 11, 11, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 5, 5, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 5, 5, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 3200)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1638912   \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 7)                 3591      \n",
            "=================================================================\n",
            "Total params: 1,744,423\n",
            "Trainable params: 1,744,423\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrG5Xd-HdTUM",
        "colab_type": "code",
        "outputId": "47415ef1-12f0-44f1-8815-ca29b0850b89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3505
        }
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 28709 samples, validate on 3589 samples\n",
            "Epoch 1/100\n",
            "28709/28709 [==============================] - 8s 272us/step - loss: 1.8059 - acc: 0.2547 - val_loss: 1.7071 - val_acc: 0.3252\n",
            "Epoch 2/100\n",
            "28709/28709 [==============================] - 4s 148us/step - loss: 1.6095 - acc: 0.3713 - val_loss: 1.6421 - val_acc: 0.4096\n",
            "Epoch 3/100\n",
            "28709/28709 [==============================] - 4s 148us/step - loss: 1.4957 - acc: 0.4245 - val_loss: 1.4516 - val_acc: 0.4505\n",
            "Epoch 4/100\n",
            "28709/28709 [==============================] - 4s 147us/step - loss: 1.4042 - acc: 0.4633 - val_loss: 1.4333 - val_acc: 0.4452\n",
            "Epoch 5/100\n",
            "28709/28709 [==============================] - 4s 148us/step - loss: 1.3344 - acc: 0.4881 - val_loss: 1.3703 - val_acc: 0.4784\n",
            "Epoch 6/100\n",
            "28709/28709 [==============================] - 4s 149us/step - loss: 1.2765 - acc: 0.5158 - val_loss: 1.3502 - val_acc: 0.4951\n",
            "Epoch 7/100\n",
            "28709/28709 [==============================] - 4s 149us/step - loss: 1.2202 - acc: 0.5342 - val_loss: 1.2167 - val_acc: 0.5308\n",
            "Epoch 8/100\n",
            "28709/28709 [==============================] - 4s 150us/step - loss: 1.1717 - acc: 0.5563 - val_loss: 1.2173 - val_acc: 0.5389\n",
            "Epoch 9/100\n",
            "28709/28709 [==============================] - 4s 151us/step - loss: 1.1323 - acc: 0.5753 - val_loss: 1.3311 - val_acc: 0.5180\n",
            "Epoch 10/100\n",
            "28709/28709 [==============================] - 4s 152us/step - loss: 1.0985 - acc: 0.5901 - val_loss: 1.1921 - val_acc: 0.5436\n",
            "Epoch 11/100\n",
            "28709/28709 [==============================] - 4s 152us/step - loss: 1.0564 - acc: 0.6052 - val_loss: 1.1854 - val_acc: 0.5561\n",
            "Epoch 12/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 1.0220 - acc: 0.6188 - val_loss: 1.2582 - val_acc: 0.5391\n",
            "Epoch 13/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.9852 - acc: 0.6327 - val_loss: 1.1807 - val_acc: 0.5678\n",
            "Epoch 14/100\n",
            "28709/28709 [==============================] - 4s 152us/step - loss: 0.9555 - acc: 0.6468 - val_loss: 1.1344 - val_acc: 0.5854\n",
            "Epoch 15/100\n",
            "28709/28709 [==============================] - 4s 152us/step - loss: 0.9177 - acc: 0.6615 - val_loss: 1.2049 - val_acc: 0.5704\n",
            "Epoch 16/100\n",
            "28709/28709 [==============================] - 4s 152us/step - loss: 0.8850 - acc: 0.6721 - val_loss: 1.1203 - val_acc: 0.5843\n",
            "Epoch 17/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.8560 - acc: 0.6838 - val_loss: 1.1263 - val_acc: 0.6004\n",
            "Epoch 18/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.8350 - acc: 0.6899 - val_loss: 1.1980 - val_acc: 0.5807\n",
            "Epoch 19/100\n",
            "28709/28709 [==============================] - 4s 155us/step - loss: 0.8012 - acc: 0.7028 - val_loss: 1.1360 - val_acc: 0.5999\n",
            "Epoch 20/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.7761 - acc: 0.7166 - val_loss: 1.1148 - val_acc: 0.5926\n",
            "Epoch 21/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.7465 - acc: 0.7236 - val_loss: 1.1154 - val_acc: 0.6147\n",
            "Epoch 22/100\n",
            "28709/28709 [==============================] - 4s 157us/step - loss: 0.7257 - acc: 0.7333 - val_loss: 1.2995 - val_acc: 0.5924\n",
            "Epoch 23/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.7010 - acc: 0.7442 - val_loss: 1.1515 - val_acc: 0.5968\n",
            "Epoch 24/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.6863 - acc: 0.7473 - val_loss: 1.1563 - val_acc: 0.6007\n",
            "Epoch 25/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.6652 - acc: 0.7551 - val_loss: 1.1269 - val_acc: 0.5991\n",
            "Epoch 26/100\n",
            "28709/28709 [==============================] - 5s 160us/step - loss: 0.6472 - acc: 0.7637 - val_loss: 1.2151 - val_acc: 0.6135\n",
            "Epoch 27/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.6316 - acc: 0.7693 - val_loss: 1.0937 - val_acc: 0.6141\n",
            "Epoch 28/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.6156 - acc: 0.7764 - val_loss: 1.1581 - val_acc: 0.6130\n",
            "Epoch 29/100\n",
            "28709/28709 [==============================] - 4s 155us/step - loss: 0.5956 - acc: 0.7844 - val_loss: 1.2190 - val_acc: 0.6155\n",
            "Epoch 30/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.5827 - acc: 0.7871 - val_loss: 1.2275 - val_acc: 0.6194\n",
            "Epoch 31/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.5724 - acc: 0.7932 - val_loss: 1.1230 - val_acc: 0.6043\n",
            "Epoch 32/100\n",
            "28709/28709 [==============================] - 4s 157us/step - loss: 0.5573 - acc: 0.7993 - val_loss: 1.1379 - val_acc: 0.6077\n",
            "Epoch 33/100\n",
            "28709/28709 [==============================] - 5s 157us/step - loss: 0.5452 - acc: 0.8042 - val_loss: 1.4400 - val_acc: 0.5968\n",
            "Epoch 34/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.5394 - acc: 0.8042 - val_loss: 1.3462 - val_acc: 0.6244\n",
            "Epoch 35/100\n",
            "28709/28709 [==============================] - 4s 155us/step - loss: 0.5297 - acc: 0.8101 - val_loss: 1.2495 - val_acc: 0.6275\n",
            "Epoch 36/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.5098 - acc: 0.8158 - val_loss: 1.2757 - val_acc: 0.6096\n",
            "Epoch 37/100\n",
            "28709/28709 [==============================] - 4s 155us/step - loss: 0.5148 - acc: 0.8174 - val_loss: 1.1854 - val_acc: 0.6183\n",
            "Epoch 38/100\n",
            "28709/28709 [==============================] - 4s 155us/step - loss: 0.5020 - acc: 0.8219 - val_loss: 1.3015 - val_acc: 0.6155\n",
            "Epoch 39/100\n",
            "28709/28709 [==============================] - 4s 156us/step - loss: 0.4917 - acc: 0.8241 - val_loss: 1.3194 - val_acc: 0.6255\n",
            "Epoch 40/100\n",
            "28709/28709 [==============================] - 4s 157us/step - loss: 0.4962 - acc: 0.8233 - val_loss: 1.2917 - val_acc: 0.6105\n",
            "Epoch 41/100\n",
            "28709/28709 [==============================] - 5s 157us/step - loss: 0.4833 - acc: 0.8270 - val_loss: 1.4020 - val_acc: 0.6219\n",
            "Epoch 42/100\n",
            "28709/28709 [==============================] - 5s 157us/step - loss: 0.4792 - acc: 0.8313 - val_loss: 1.3681 - val_acc: 0.6121\n",
            "Epoch 43/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4679 - acc: 0.8331 - val_loss: 1.3153 - val_acc: 0.6141\n",
            "Epoch 44/100\n",
            "28709/28709 [==============================] - 4s 155us/step - loss: 0.4634 - acc: 0.8399 - val_loss: 1.2858 - val_acc: 0.6174\n",
            "Epoch 45/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4642 - acc: 0.8390 - val_loss: 1.3630 - val_acc: 0.6208\n",
            "Epoch 46/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4481 - acc: 0.8416 - val_loss: 1.1711 - val_acc: 0.6102\n",
            "Epoch 47/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4571 - acc: 0.8391 - val_loss: 1.3159 - val_acc: 0.6208\n",
            "Epoch 48/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4490 - acc: 0.8427 - val_loss: 1.3379 - val_acc: 0.6314\n",
            "Epoch 49/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4503 - acc: 0.8442 - val_loss: 1.2647 - val_acc: 0.6102\n",
            "Epoch 50/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4360 - acc: 0.8472 - val_loss: 1.5707 - val_acc: 0.6255\n",
            "Epoch 51/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4443 - acc: 0.8450 - val_loss: 1.1912 - val_acc: 0.6094\n",
            "Epoch 52/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4352 - acc: 0.8489 - val_loss: 1.5359 - val_acc: 0.6152\n",
            "Epoch 53/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4350 - acc: 0.8487 - val_loss: 1.1897 - val_acc: 0.6002\n",
            "Epoch 54/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4289 - acc: 0.8521 - val_loss: 1.2724 - val_acc: 0.6094\n",
            "Epoch 55/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4299 - acc: 0.8511 - val_loss: 1.2837 - val_acc: 0.6230\n",
            "Epoch 56/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4271 - acc: 0.8519 - val_loss: 1.3167 - val_acc: 0.6110\n",
            "Epoch 57/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4266 - acc: 0.8535 - val_loss: 1.6740 - val_acc: 0.6283\n",
            "Epoch 58/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4114 - acc: 0.8587 - val_loss: 1.2658 - val_acc: 0.6191\n",
            "Epoch 59/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4176 - acc: 0.8559 - val_loss: 1.6507 - val_acc: 0.6247\n",
            "Epoch 60/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4194 - acc: 0.8578 - val_loss: 1.4157 - val_acc: 0.6191\n",
            "Epoch 61/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4097 - acc: 0.8635 - val_loss: 1.5263 - val_acc: 0.6219\n",
            "Epoch 62/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4010 - acc: 0.8633 - val_loss: 1.3642 - val_acc: 0.6052\n",
            "Epoch 63/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4136 - acc: 0.8596 - val_loss: 1.5766 - val_acc: 0.6127\n",
            "Epoch 64/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4059 - acc: 0.8627 - val_loss: 1.1919 - val_acc: 0.6060\n",
            "Epoch 65/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4087 - acc: 0.8603 - val_loss: 1.2951 - val_acc: 0.6141\n",
            "Epoch 66/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4078 - acc: 0.8622 - val_loss: 1.4083 - val_acc: 0.6252\n",
            "Epoch 67/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4017 - acc: 0.8655 - val_loss: 1.2263 - val_acc: 0.6197\n",
            "Epoch 68/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.4022 - acc: 0.8615 - val_loss: 1.6270 - val_acc: 0.6169\n",
            "Epoch 69/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3981 - acc: 0.8642 - val_loss: 1.3811 - val_acc: 0.6152\n",
            "Epoch 70/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3950 - acc: 0.8648 - val_loss: 1.5038 - val_acc: 0.6155\n",
            "Epoch 71/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3903 - acc: 0.8681 - val_loss: 1.5040 - val_acc: 0.6077\n",
            "Epoch 72/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4068 - acc: 0.8622 - val_loss: 1.3108 - val_acc: 0.6163\n",
            "Epoch 73/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.4036 - acc: 0.8648 - val_loss: 1.6851 - val_acc: 0.6144\n",
            "Epoch 74/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3913 - acc: 0.8668 - val_loss: 1.2352 - val_acc: 0.6113\n",
            "Epoch 75/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.3936 - acc: 0.8669 - val_loss: 1.3240 - val_acc: 0.6188\n",
            "Epoch 76/100\n",
            "28709/28709 [==============================] - 4s 154us/step - loss: 0.3927 - acc: 0.8677 - val_loss: 1.6657 - val_acc: 0.6152\n",
            "Epoch 77/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3935 - acc: 0.8681 - val_loss: 1.6306 - val_acc: 0.6197\n",
            "Epoch 78/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3878 - acc: 0.8671 - val_loss: 1.3114 - val_acc: 0.5977\n",
            "Epoch 79/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3808 - acc: 0.8742 - val_loss: 1.1785 - val_acc: 0.6021\n",
            "Epoch 80/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3923 - acc: 0.8676 - val_loss: 1.4000 - val_acc: 0.6250\n",
            "Epoch 81/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3806 - acc: 0.8755 - val_loss: 1.7251 - val_acc: 0.6264\n",
            "Epoch 82/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3926 - acc: 0.8675 - val_loss: 1.4732 - val_acc: 0.6149\n",
            "Epoch 83/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3951 - acc: 0.8678 - val_loss: 1.5307 - val_acc: 0.6160\n",
            "Epoch 84/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3926 - acc: 0.8678 - val_loss: 1.6326 - val_acc: 0.6152\n",
            "Epoch 85/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3839 - acc: 0.8736 - val_loss: 1.3166 - val_acc: 0.6105\n",
            "Epoch 86/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3884 - acc: 0.8699 - val_loss: 1.3229 - val_acc: 0.6077\n",
            "Epoch 87/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3953 - acc: 0.8701 - val_loss: 1.7312 - val_acc: 0.6211\n",
            "Epoch 88/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3855 - acc: 0.8723 - val_loss: 1.4215 - val_acc: 0.6158\n",
            "Epoch 89/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3824 - acc: 0.8714 - val_loss: 1.1957 - val_acc: 0.6041\n",
            "Epoch 90/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3850 - acc: 0.8720 - val_loss: 1.5321 - val_acc: 0.6283\n",
            "Epoch 91/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3818 - acc: 0.8734 - val_loss: 1.7693 - val_acc: 0.6239\n",
            "Epoch 92/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3813 - acc: 0.8737 - val_loss: 1.3262 - val_acc: 0.6188\n",
            "Epoch 93/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3888 - acc: 0.8697 - val_loss: 1.1692 - val_acc: 0.6121\n",
            "Epoch 94/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3762 - acc: 0.8752 - val_loss: 1.6392 - val_acc: 0.6294\n",
            "Epoch 95/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3742 - acc: 0.8761 - val_loss: 1.3905 - val_acc: 0.6213\n",
            "Epoch 96/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3802 - acc: 0.8736 - val_loss: 1.2199 - val_acc: 0.6191\n",
            "Epoch 97/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3797 - acc: 0.8744 - val_loss: 1.5578 - val_acc: 0.6291\n",
            "Epoch 98/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3783 - acc: 0.8759 - val_loss: 1.4038 - val_acc: 0.6116\n",
            "Epoch 99/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3806 - acc: 0.8765 - val_loss: 1.2943 - val_acc: 0.6172\n",
            "Epoch 100/100\n",
            "28709/28709 [==============================] - 4s 153us/step - loss: 0.3683 - acc: 0.8784 - val_loss: 1.5607 - val_acc: 0.6183\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQm4EBjAZQ7c",
        "colab_type": "text"
      },
      "source": [
        "# Evaluating 1st model and plotting curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8Zf-86jd7Hk",
        "colab_type": "code",
        "outputId": "0f124ca1-7547-4744-9e7d-d464bd10e0a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Evaluating model performance\n",
        "\n",
        "model1.evaluate(test_data, test_labels_one_hot)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 0s 84us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.560699536827621, 0.6182780718946231]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWVMn94TeCOy",
        "colab_type": "code",
        "outputId": "dcb4328e-762a-42d8-e394-b4afe6269899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        }
      },
      "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": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gD8r05ZfqQUQfPK19e+/2JYtkV92fJaXsKqXMklJ2l1I+\nLaWcKqWc6rHvBVLKV5LZvpj5zW8gO5vhfES7+k4Fv/yiutoZmib6ze+oo+zuYj/+6D2rVbxZv952\nBPn5MGWKfQPavBn+/OfEt0EnXD/6VBfjRSv04UL3QSa2cQs92OF7v9B9797qfvDXv8Ldd4dvX6yh\ne68hWv1IF0fvDtt7tdkrRx80cuH3UORVcW/h5+jDVfm7HX1JiZ1iy893djdNBWkVum+ytGoFw4eT\nRQ2nMrth9emnw8cfp7BdhpjRHWznzsqRWXz+eeLPr7v5QYPUV+xvf7PXPfaY9+xabrZtg2uuUf2+\nGxNhcjv6nBxVpQ7KSe7a5bz5du1q580rK23XFpRoHH24/uJeBHX0XpGIqirvERLXrVM3dr+8L6gO\nOv/3f5Er2L1C9+5xAiwqK+2hmaMZrjbIpDbJFnq/BzYvRx/0swZx9HrFPcQWunc7+nQK24MR+vhR\nH76/ldvp2lK5+p071WozxWnTw118Vj8IIpCcPL1b6EE9OB58sFqurobnn498nFtugQcfhKuvhg8/\njL097ushhPPGXFISKs76zTCa8H1NjX0jdo9vHw9H37WrLZYZGdGF7r//3rtHzS+/OEWrVSs7VB8t\nkRx9RobzIcJyp0EHy4H0cfThhr+18HL0QfrQg7+jD/dApgv92rX2A3I4oe/Z077mW7faeXpI/ah4\nYIQ+ftQLfS/W8n7GsXTpor4dltgvWJDKxhmixe1gLYGF1Am9EHDxxfb6Z58NfwwpVVdPC/3mEw1S\nhhbjgVMUN260b4h5eSpnGnQqVDfuiVz0cG48cvQZGeoBKC8Prr3W+f5IQu8VtodQoY+2TTr6g4eF\nW7i9BCwaR69f1x07vB9e9Ar35uzo3ULfvr3t8svK7HOFO6cQTlevj2RpHH1zYsCAhpjggPKlzL/v\n84YvaFkZnHBC6HzFhvSkri608EYX+k8/TXyhpZfQgxrtzXKjX3zhHKHNzerVztCjPjZ8NOzcaQtB\nbq5dMKWLj160ZN0E9e3RCH24aup4OHpQBY2lpfCXvzjXR8rR60Kvh9h/+SVy9XhQsrNDZ+BzH88r\nTx+N0GdkhB8zoKrKftjNyIhPMVk8HX1jc/ThhF4I7/B9pHPqefr58+1lI/TNifpudhYDvp7J/Pn2\nl3THjsgOzJAelJTYQp6fr9xp3762k92yRRXlJYpdu5x5YH3krYICGDvWfh2uq517fPx162Jrjzts\nb6GLj96X3LoJxhq6Dyf08cjRW3j1vY+Uo9eF/gRt1o54Cj2Ehu/DXYdYhB7Ch+91N19YGN04BdGc\nLx6OPtzwvLE4enBW3v/0U7AeDfoD+YoV9rIR+uaGJvS89RZ77w333GOv+u675DfJED1eYWohYOhQ\ne/3ixYk7/7ff2g66d+/Qm/yFF9rLzz/vX+jmFvqgjv6dd+Af/7CP63U9wNmuSEIfjaMPJ1jxcvR+\nRBO61//d3aH7xsz0BqFCH87RW+cNOliORTihj3d+3u98QRx9hw52mmHLFvW/kciqe3A6+p9/Dtaj\nQRd6d/tTjRH6eDJihN2PYvlyWLeOAQPszdEIfWVlfJtmCI47P2+hC30iK+/1sL0eDrT4zW9sx7Fl\nC/znP6H7SBmbo3/wQTjuOPjd7+Cuu9Q6v+vh5+itB4BEhO7jkaMPRzihLy1VxVmgehzo/eHdc8c3\ntk3uyvtwOfpEOPpECH3r1naB4s6dyiUHcfSZmaHD0iay6h5CQ/dBzqcPhatjhL650bo1HHmk/fqN\nNygqsl9+/33k3G5tLRx9tPonnD49Ia00RMAvVJ0KofdyCRkZcP759muv8P2KFaGzfUVy9C+8oLri\nWcyYoX77zWIWTeg+3XL0foQTen0imwED1EOPdT2qqpzpnHiH7t3HS3ToPhFCn5ERes4gjh5C8/SJ\nzNFDbELfurUa9dCNEfrmyCmn2MsvvUSHDrYLKi+PfLP96CPVDWrXLnjggcQ10+CPn4MdMsReXrrU\n7r8cbyIJPcAFF9jLb74ZKupe89eXlfnnyt95x3lMgJUrlYP1ux6RivESkaP3uinHU+jDFajpYXtr\nWOTu3e11el1FvEP38a66h+QLvdc5g9Y1uPP0iay6h9iEHrwjcEbomyOnn27Hbz74ALFxQ4irD4d+\nk1+50gyjmwr8hK1bN9UHG9TNtbE1F3V18MgjKue+apW9Xu8G5yf0e+1lB49qa0P71HsJPXg/aC5Z\nogr8rLG/dd55J/ZivFhD9+FuqpmZ0LKlc12sxXhehHP0kYReL8BKRehe/zs1BaE/4wxnDVM0jj5o\nP/rsbDubqqcKoi3GCyr0Xv+vRuibI127qtg7KJV++WX697c3RxJ6/Wayc6dz9iRDcvATenC6+sYU\n5FVVwXnnwVVXqd4YJ56obkK//mrfZPPyoE8f/2PoDnzaNPuhsLYWPvjA3qbfqN15+l9/tc8NSriu\nvtre/vbbwYrx9BtnPIvxvETcLaLJCt0nU+hjCd3rUyiH+95YpFroly1zRsXCTd0bq6MXwinaVo1F\npGI8/e+6bp1z3Hrj6CZa19gAACAASURBVA2KM8+0l198MSpHr+cBAX74IX7NMgTDT9ggPnn6HTtg\n1CinC1+5Uom+LiYDB4bv1nT66XYh0Vdfwb/+pZa/+MJ+WOnc2X7uhFBH//zz9lC67drBvHlw0UX2\n9vfecw7n6VeMp2OJcyyh+8pKuzbAar8bt/NLhtBLGVno9SGJkx2637TJvldkZzuHbPYjFUKvXy+L\nQw+Fv//d2YvBTaw5elA9VyzWrFG/Izn63Fz7nLW1zrRMtI7ejIzXXBk71p5OaeFCitranVLDCX1d\nXajQr1yZgPYZwhLU0cci9L/+qqrm3347dNszz9iV7uAftrfIz4eJE+3XV1yh3Iceth8xwhkGdjt6\n/QY2eTLss496wLBGZ9u6VYm9hV/oXqcxjv4vf7GL2tq1g9NOC90nkY7eL0e/fr39vSgosF2il3DF\no02FhXaKokWLUHFxh+4XLbJfH3RQ+GlwLfTvtt5vHhIn9NdfD8OHqxkhn3hCfR8XLlQjPoabhCec\now/Xjx6ckxhZg5ZFqroHZ55ef8gLJ/S9ejn/9tnZ3g8SycYIfSLo2FHNYFFP0Uq7/1O4vO6aNaHz\nSzcXof/hB7j1VpUPTnf8ctLgdPTLlnnntf2oqFA3Of0a/PnPcNZZ9ms95B5J6AHuuMMO05aUqBum\nLsxuoXc7er02wEoxCeH4+jpC0n6he51Yc/Q//ujM2d59t7cbcrvleOboW7a0oyj6FKruSIslSokS\n+owMuPlme5hed12CW+gXLrRfH354sHPo4X33fSZRQr/vvqrYeO5cuPRS73H9vWiMo9eFPqijh9iE\n3j0Urj4GQCoxQp8otPB93w/+0bD844/+4uA1jnZzCd2PH69E7aSTVI+CdCaco+/Y0b5x7NoVGoEJ\nx6uv2hGdjAwVrrzlFjUFrXvqVAgm9K1bqxy/dTOZO9c5PfKIEc6bqdvR60Kvdw3ShV4nVkcfJHR/\n1VX2+BEHHaT68nuRSEfvnqzHCt97he3BX+gbG7oHFWEpLYX77gt//LIyp9Afdliw47trh6waj5oa\nO10jhIoupBr9YWPDhtD5EMIRS+genLl9r2JTP/Q8fTrk58EIfeI49dSG+FmrLxbQvYtS99pa/zHv\nvUSjOTj6mhq7cO3XX53iko6EE3qIvSBP71Hxf/9nT1BTUKDy6+7ZzoIIPajq+6uuCl3fs6e6yfk5\n+qoqu1ucEM4b4jHHeJ8riKP3ytFHcvRvvAFz5thteeIJ//oEXeT0qup4EQ+hj9fDh9810MWppMQ5\n0VJQoS8stP9GZWVqYiJQtQaW6HfoYGchU4ku9KtW2e1r3Try8LyRQvdBHL1OJKF3O/p0wAh9oigo\ncFSXFOXaj4R+eXo/R9/Uu9ht3KjqDywSOU58PIgk9LEW5OkPcgcd5Nx2xBHwpz/Zr3v2jDxvuc7d\nd+Mo+gTl5oXwd/Rr1th/l+7dneHhrl2dgmYRpBgv2tB9RQVceaX9+uKLnZMIufGbbS5eeE1so1e0\n6zfyNm0SP1qfF7o4LVyoriEocQoaDhfC+Z2x0oqJCts3Bj10r7v5IN0IvUL3karuIXahP+44++Fz\n+PDI7UsGRugTiRa+L9r2acNyNEJfXt70u9jpYS9oXo4+VqHXJ6qxmDxZiVzXrnDvvcGPC6pK+J//\ndEYFrCFa9Rv/hg22uPuF7S2OOy50XTShe7dg6g97Fv/9r7oZWjfg9u3VQ0s4Is1P31jcE9tUVNh1\nCkI4Q7NCeLv6eITuw6EfXy+kC+rmLby6/qaj0Ofmev+tgwh9ly52ceLWrepvGm0xnk6k4r/evVVx\n5IwZ6n86HTBCn0hOPrnhW1S0PbzQ79rlLNTT+5Q29fC9Oy+czo7eb+51Hd2Nf/WV7aZ27oTbblMi\n7Ra10lJ7ususLOjXL/S4mZkqb79+vbOHZlAOPVSJpBBKuE89Va3PzbU/R02N3Q0sktC78/QtWjhv\ntpGK8Vq0sAVJSucQpGvXqs84bJgz/XHPPZG7IwVpQ2Nwh+6/+sru792vX6jgeAl9Mh29TtBCPAuv\nrr/pKPQQOnUvBBP6jIyGGcSB0KLneDt6UN0bzzwzWO+HZGCEPpG0atUwJG5/bBX3EvrvvrNvJr17\nO/vBNvWCvKbk6MvK7L9DXp53/regwL5B1tSofutSwrnnwu23q7nO3fMUfP21vTxggBL7RHDDDSrv\n/vXXTrHxytPr3ysvoT/ySOfnb9vWWUGsV6hbZGc7UwBe4fvXXlPX4N//trfl5KgeBH4FeDrJdPQ7\ndqjhji28+qenk9BH6+ibSugevNsSRHQhtCAviNB36uT//9/UMEKfaOr7ThVhq7tXFzt3sU/fvvbr\npu7o3UKfzo4+XNc6HXee/uGHYdYse527n3yksH086dYt1El45ekjOfq8POccTe7rIUSoo3a/9irI\nu/NO5+yM/+//qf78N98crCtSooXenXLQ8/MHHhi6f6pD9xYtW3qPzBaOphK6B29HHymMbuEuyAsi\n9BkZ3n9bI/SGUE44Adq1oxdryERV3q9f7wxjQqjQ66HdIEIfTX/uZOMO3a9e7Z2vTQci5ectdKF/\n9lm47jrndr27EziFft99Y25ezHg5+khCD87wvdf1cAut+ybo7mJXWmoLpxBqEqcXX/TuXuhHoovx\n3Dl6XeiDOPrc3MiV4I3FS5yGDIm+B4JuKKyuv+kq9I1x9Pr3a8UKu8A5Ozt8rwJ3+D4jI/EPcYnA\nCH2iyc6GcePIpJa9sO+sbvF2D8ihC32k0P0996h//AsvjEN7E4Db0VdWpm+BYVChd89kV1Pj3L56\ntfOGqYfuE+3ovXA7+ro6Z2TFT+hHj7ZvhAccELo9GqEvKVEPQNZD3v77OyMGQRk50k59hBs2NVb0\nz7Rtm7NbZBChT3TYHryFPtr8PCjRsh4Ca2rU91Yv7ksnoY81Rw/O0P0339jLkUatcwt9mzbpMQBO\ntBihTwbjxwPO8L07T687PnfoPlwXu4oKNRBNdbVylo2dUS0RuIUe0jd8H1ToBw8O7fferp3KPVt8\natdfJjV074Xb0a9fbw9c1L69fwi0f3/Vv/3uu717AoQL1bu3l5QoB28Ri8iDuvmuWqX6jrun1o0H\nulAvXmynGfbc07tQMBVC7+VEo83PW7gL8pq7o9cfuv0q7i3cQt8Uw/ZghD45HHkkdOvmK/QlJc6K\n7KIiQuax93PAn3zizHfOnx/ntjcSKUND95C+BXmRKu4t8vJCQ/DPPadG/rOwwvebN9s3z9xcp7tI\nFm5Hr19//aHSi+OPVwWGXoN/uEUtnPCXljqFvjF9jPfcU6VPEuGu9M/0ySf2st9EMW6hT0ZoV4hQ\nNxqr0Lvz9Okq9PFy9PpMdNE6eiP0Bn8yMuCss3yFXnd7ekV2kII8d9FXugl9cbE9XrhOU3f0YPdT\nBzVZx8knO2+21kQjuoPYd9/QSEAycDv6IPn5IERy9PrrX391juAWq6NPNH4T2/gJffv2zp4GyXD0\n4BSp3r1jF2Xd0a9Y4ZyFz0tcU0VjHH1hoXrIdhNJ6PVhcKM5X7phhD5ZjB/vFPpv7YmY/YbXDFKQ\n5yX06VTo5uXmoWkIfaSK3ltvVTPG3XefPevcoYfa2z/7TOU9Ux22h1BHH6lrXVAi5eh10Xz3Xfuh\nr6govdyijp9Qe1XcQ+igOakQ+ljdPDiFfuFCu3tpu3bxH164MTTG0QvhXfBpHL0hvuy/P0V97UT7\nd9/UNuTd3fl5i0gFeRs3OguFQD2N6w4y1ej5ef2fKl1D99E4+nbt4JFHlJu38qXdutkuoLxc/W1T\nXXEPSlStSEJxsXNGukQKvf76v/+1l9NlaFAv/IQ63BzvutAnqypbP08shXgWeuhev3ek24NYYxw9\neKfMjKM3xBch6HresbRGDdRcUp7dECJzV9xbRArdv/uu96nSKXyvC73uOpLt6NetU2PJn3aaM3zs\nJhqh90N39YsWpYejb9HCnmMenLnnZIXua+0gVtqG7cFb6Dt08J/ABlLj6K3Cz4wM1RMhVnr18u5i\nlm5C37Zt6EBT0QhvLI4+P995HzBCb4iIGH+2M3z/eQlSxh6618P2+tzS778fh8bGCT10f8gh9j/q\npk2hYwnEGylVKPKss9Q/+R13qKliJ070f0+8hX7hwvQQenDm6YuL7eV4OvpIA+hYpLOj92rzgQeG\nL/xLhdDfe6+atfCFF5y9PaIlM9P7O5BuQi9EaPg+6IA54O3oI1XdgzN8b4TeEJk+fSjqsKXh5Z3X\nlzBhgi0ubdo4v1Tu0L3exU5Kp9D/+c/28gcfON1TKtEdvTVtqkW0rn7HjuAPBzU1qgL+8MPVgCx6\nP/cvv/Q/TjyEXo9czJljjwhXUOAU22TjNatZbq6aRCdWonH0Ft27O8ceTze8hDpc2B7g6KPt5UMO\niWtzfOnRAx56SI0s2Fj08L1Fugk9hAp9oh09OMP3RugNgSgaaj+CzvumB9Om2dsGDnS6hvbtbbGp\nqHDOJb58ud0NpkMHNYGCdcMuKYFlyxL0AaJEF/pu3ZyRh2iEfskS9U++xx7+s//pvPkmvPWWc50V\nnpQytLbBImj3unAMHmxHLvSuPO6/b7Lxesjo06dxbYomR28xfHh6DzoSi9Afdxy8/jq8/DKccUZi\n2pVI3FMcQ3oKvbtN0URPYhV6va4mmhEc04mkCr0Q4hkhxCYhxFc+28cLIb4UQiwXQiwQQkQ5cnP6\nc8LV/jG2004LXedXkKe7+WOOUTlYvbtXpPB9dXVyqvP10H337s4QYTQFeVOmqPECduxQ07FG4pVX\n7OVjj1XDmOrORx/WVCeaqns/Wrb0rtBOVSGehZejj9SHPhKxCH065+fBu5jOr+LeQggYNQrGjUv8\n8LeJoKkIve7o8/Oju9axFOMBXH21+rtOnAhjxgQ/XzqRbEf/LHBCmO2rgaOklPsBdwBPJaNRyeSw\n49sw//h7uY/reYirmHLoP3nmGfjwQ/WFcuNXkKcLvTVv+G9+Y68LJ/QLFqh/mL33djrORBAvR79g\ngb2sV4x7UVWl3JXFPfeo4Vt1V+YV8ZAyPqF7cObpLVKZnwdvR9+Y/DxEzsl75bvTOT8PoeOZt27d\n+AeidKepCL3epmjD6O3bhz7EBRH6Ll1UpObJJ9Oru2E0JFXopZQfAb7SIqVcIKW0brWLgDB1rk2X\no/88guv5K1fxCJOWTeLCU7cyfLj3QCpeBXkVFc4RxqyJR3RH//HH/hPd3H23ClF//30wdwzqS37t\ntbBlS+R9LUpLlQMH5XLbt49N6LdudYq7Pla1F++9Z+fFe/Wy3Zgu9F6OvqLC7uedne09wEZQ0lHo\nvRx9Y4U+WkffsaN6wEx39M+1//6pGeQomTTFHH20Qu/Vlz6I0DcH0vnrOwF4y2+jEGKiEGKxEGLx\nZn0op6bA0KG2+lRWqkHqfdCF3hrH/uOP7XHK997bLhbp3dv+Iu/cqaZPdVNZ6ex+p3ez8mP+fJg0\nCR54IHSWtnC4w/ZCxBa618eMB5XCsD6/F3rYftw4Ox+sT8qyfHnog5DbzTcmj+w1gEmqhT4Zjt59\n823Z0umChg1L7/y8hS70kcL2zYHOnUMf2tJR6Bvj6CE0fB+k6r45kJZCL4T4DUrob/DbR0r5lJRy\niJRySGFhYfIaFw+EgEsvtV9PneqbMNdDhrNnw6BBcNtt9jorbG8RKXz/ySdqIBf9td+EORb6POsz\nZ4YXWR132B6c/2hr1gTrHaCH7UG9x2+kwOpqeO01+7Ve99C+vd2roaoqNAUQr7A9qPPo/dY7dVLD\ncKaSRDt6vyk89YeBdA/bW+ifK1IhXnNAiNDwfToKvd7GWOaMMI4+TRBCDAL+AZwqpYwiUNzEOOss\n+5F05UrfpPq++zpvOsuXO+c6dwu9Hr73GjjHXYm+aVPkaXDnzbOXS0uD99N3O3pQQmDdQKqrvWe2\nc+MWevAP33/4oV130L07HHywc3u4PH08Ku4thHC6+lS7eVDFhXo6okWLxndz03uG9O3r7db1B5x0\nL8SzsKI/WVnOrnPNGT1836aNc/z+dOHgg9XAV6ecAjffHP373Q8HRuhTgBCiB/AqcK6UMkAnqiZM\nq1Zw3nn26ylTPHdr3VqNgHfaaaH/eNnZoQ5Jd/T//a9zZjuAuXNDzxEufL9mTWh3tldf9d9fRxdx\nfUCRaPL0NTXeI9n5Cb0eth87NjS3qofv3Xn6eDp6gCOO8D5vqhDC6ep79AgdaSxasrNh+nTVvfMf\n//De57rr1Pd4/Hg46KDGnS9Z3Huvmr9gzpzUzDaYCnS3nI5uHtR3+PbbVXQzlkGCjKNPAkKIGcBC\noL8Q4hchxAQhxCQhxKT6Xf4EdACeEEIsE0IsTmb7ks6kSfby7Nm+M8AcfLASsM2bYcYM1cWjVy+4\n//7QUGm3bvY/7K5dTlH+6SdvgQwn9O5Jc0CFxoOE3L1C9+AMF0cS+q++8h7cxutz1NY60wzjxoXu\nE64gLx5d63QmTlRTvB5+uBrBLB3w+zs0hpNOUt9LP7d+4YWqOPJf/2oa+XlQRYN//GNoxKw50xSE\nvrEYoU8CUsqzpJRdpZRZUsruUsqnpZRTpZRT67dfLKVsJ6U8oP5nSDLbl3T22QeOOkot19bCU+F7\nE7ZurZzTq6/C6tVw+eXe+517rr38yCP2sh6C18Op4YRef49FcXGwIj6v0D04HX2kgjw9bK//k/o9\nsGzapJa7dPGe6MMdutfrE+Lt6PPzVQTlv/8NnQUrVeiOPpldxpp71Xpz4Jhj7HqKk05KbVsShQnd\nG1KDXpT36KN2v7BGMHGiXen86ad21boetr/mGnuf77+3BVKnpkZ1VbPQc5VBwvfxCN3r9QgXXWQv\nf/99aNX8zJn28pgx3oNp7LmnLeIlJSo1YRFvoU9HdNeW6gF8DOlFYSF8+616YL7Btwy6adO2rbNa\n3wi9ITmcdpptrbZtg4cfbvQhO3VSzt/i0UeVKOqz3Y0erXr5WehTiFp8+qn93NG9O0yebG979dXI\n1fq6ow8Suvc6nu7ojzvO7kpYXe2MBtTVOYXeK2wPKnTsF77fHYT+0ktVOmH0aLjgglS3xpBudO2q\nakuaSoolFsaPV78PPDC951yIJ0boU01mJtxyi/36gQec5d8xcuWV9vJLL6ncdWmpet2zp6qwHTbM\n3ufjj0OPoYftjz9eZRnat1evf/kFFoepoKispGEa3hYtnDk/3dF/952KLgwdCjk56iZjPVz8+qv9\nIJCTowR6n33s9+rh+08/tecC6NAhfDeu3Vnou3RRkZ1Zs5I3b7rBkE489hh88YWKFjbnBxodI/Tp\nwNln2yPjlJSoKakayUEH2Tnq6mr4/e/tbSecoL7gutB75dzdQp+Vpbq1WIQL3+sT8HTt6gyjd+1q\n9yAoLYUHH1QPDdXVysH/4Q9qmx62HzpUpRr0UdV0odf7zo8e7T2/toVeAa93sYtn9zqDwZCeCKHG\nI2mqw9nGQqOFXgixjxDiNCGEx3AchkBkZsKtt9qvH3zQaS9jRHf1+tC1J9TPNqB3/1q6VI2mp+9v\njayXkaEKdUB1WbOYOdM/fO9XiAfqHy1c15hp01S3Jj1sb/VJ93P0s2fby6NH+x8bgjn6eFTdGwwG\nQzoQldALIR4TQkzVXo8FvgBeBr4RQgz1fbMhPGeeaaufZXMbydixoaOhZWbag+q0a2cP5FJb6xxq\n9t13bRE/+GDb4R57rF3AsnKlf392v0I8izvuUIVhhx+uCn/mzIHTT7e3/+53zoiCFZ3Qhd4a2e67\n7+zhgfPyYORI7zZZ9O9vRxTWrbNTDLtD6N5gMOx+ROvoTwT0ccpuB+YA+wOfAbd6vckQgBYt1JBP\nFg891Oip5bKynCF7UC5eH5LUL3zvDttbtGzp7Hrz8sve5/brQ29x8slKnP/7XzU4yUknqTGDrFz+\nxo3OOeMtR6+H7r/9Vj2g6G7++OMjT0aTmalCd/+/vfuOk7K8+j/+OSxFioIiAoIoKGqwBdjHLsEW\ngajYlaCxo7ErFtQkBmIXG4rmZwsqsYFGUUk0KvbGWh4iiEhEWlBAkBpA4Pz+OLPPzDZ2l53dmZ35\nvl+vec1c99xzz9lx5Mx93dd1rmKffRaxzJ6d3KZELyK5orqJvj3wLYCZdQR2AW5y938BIwCd0dfE\nCSckM9myZTB8eI0POWhQDGQr1rdvyefLS/TuFSd6KNl9f/PN8OabZd93Q133FWnduvxSAl26JH8A\nbLFFsob8qlUxPS410ffvX7X3Sr1OP3x4jGkoHqzYpEnJVbJEROqz6ib6lUDxWN1fAEuB4rHXy4FN\ny3uRVFFBQclr9XfcEZVxaqBNGzjzzHjcuHHJRV6gZKL/4IOopT90aHIwXatWJafhQQzIKx47uHo1\nHHFE2ZXyKuu6r8iRR5asDAxlC9+kdt9PmJActNegQdULfaRep//nP5PjE5o0gQcfzM463yIiG6O6\nif5T4Hwz2xU4H/inuxcvu9YZmJfO4PLS8cdDYaIg4OrVsQh8Dd1xBzz2WHSTl66G1qlTMhEvXx5d\n2kOHJp8/5JCyI9ibNo0z/uLr/8uXxwC/yZOT+1Q0h74q7r675GtSBw1CyUQ/fHhyLMH++0fp0qoo\nb0Wyrl3hww9LVhYUEanvqpvorwX2Jgbg7QT8KeW5o4jr9FITDRpEhZtif/tbnHLWQJMmkbwKyyko\nbFZxjfJmzeDCC8t/rnPnCKt162gvWhQD9a66Kuq6pw7Sq84ZPUQvwtix8aOkd++yZ/ipib54EB5U\nvdseYLfdSlbFGjAAPvkkOxafERFJJ/PKypuVfoFZc2Bn4Gt3X5qy/VeJbXW+6lxhYaEXbah6S310\n2mnw6KPxeOedY2RaTZcaq8Dzz0fJWIgiKocdFl3ov/pVMpFXpKgoRvEvW1bxPqtWlRwnUFNvvVX+\n0qHTp1dvoZYxY2Iq34knxo+JfCmeISK5wcw+qcqaMNVO9BW8WetMrh2fk4n+u+9i/llxBr399igh\nV0s++ghWrozr4dVNym+9FV33pZfEhej6r2GHRBkLFpQdLNetW8lLByIiua6qiX4D9cPKPejZQCt3\nvy3R3g34O9DezD4DDnf37zYmYCmlXbsYmHf55dH+4x+jgl7xkPM022uvjX/tL34RA+Jefjmu52+y\nSdxat4Z+/dIXY7E2beLYqUWAqtNtLyKST6p1Rm9mk4AH3P3eRPufxJS7/wdcBExw90G1EeiG5OQZ\nPcCaNTE6rvhC9BlnwMMPZzamLNGrV8n6/B9+WLMfKyIi9U1Vz+irOxhvW2Bq4g1aElPsrnT3e4hi\nOYdt4LVSXY0bl1zN7tFHazzdLlekDshr377sFEAREQnVTfQNgOLpdPsDDryZaM8GVGYk3Q47DA48\nMB6vW5eWIjq5oEeP5OP+/WOygoiIlFXdfx6/BopLkpwEvO/uKxPtrYGa1WyV8l19dfLxI4/E+q15\n7pRTYrR8v34xfEFERMpX3UQ/HLjEzBYCvwZSJnxzIDCp3FdJzRxySNRohRjantqdn6eaNoWnnooB\ngKlr3YuISEnVSvTu/gRxXf4m4EB3T12R/HtKJn5JF7OSZ/UjR8a69SIiIpWo9pVNd3/X3W9397dL\nbb/O3cenLzQp4eijY31ViNVX7r8/s/GIiEi9UO1Eb2bNzOwCMxtjZq8n7s8zs0oWB5UaadAg6ssW\nu/NO+O9/MxePiIjUC9VK9GbWjljYZgRQCDRL3N8LfGpmulpamwYOTBaOnz8/6reKiIhsQHXP6G8F\nNgcOcPfO7r6Pu3cmptq1Am5Jd4CSonHjZKU8gFtu0Vm9iIhsUHUTfV/gand/L3Wju78P/I7k1Dup\nLWedlVyLddasWINWRESkAtVN9C2A/1Tw3JzE81KbmjeHP6WsDnzTTSUXfxcREUlR3UT/FXBKBc+d\nTKI8rtSys8+OGvgAK1aUnHonIiKSYmMK5gwws9fM7Awz62tmp5vZK0QBndvSH6KUUVAAd92VbD/+\neKzqIiIiUkp1C+aMBs4FdgUeAl4GHgZ2B85JFNSRunDggXDMMcn2xRfD+vUV7y8iInlpYwrmPEDU\ntd8FOCBx3wH4NrGMrdSV226DJk3i8ccfw+jRmY1HRESyzkat+eXu6939S3d/L3G/HmhJJH2pK126\nwODByfaQIXHNXkREJEGLe9Z3V18NW28dj+fNK3ntXkRE8l6dJnoze8TM5pvZFxU8b2Y2wsymm9kk\nM+tR3n6SokULGDYs2b7lFli4MHPxiIhIVqnrM/pRQJ8NPN8X6Jq4DQK0cktVnHoq/Oxn8XjZMrjh\nhszGIyIiWaPSRG9mXapyA9pVdqzEineLNrBLf+AxDx8CrcysfZX/mnzVsGEUzik2ciTMmJG5eERE\nJGs0rMI+0wGvwn5Wxf02pAMwO6U9J7FtXg2Pm/uOPBL23Rfefx9++gl+/3uNwhcRkSol+tNrPYqN\nYGaDiO59OnXqlOFosoBZXJ8/4IBo//WvMSK/e/fMxiUiIhlVaaJ390frIpCEucA2Ke2OiW1lJObz\nPwBQWFhY056E3LD//nFmP25ctIcMgVdeyWxMIiKSUdk2vW4c8JvE6Pu9gSXurm776rjxRmiQ+M/6\n6qswZkxm4xERkYyq6+l1TwIfADuZ2RwzO9PMzjWzcxO7jAe+IcYFPAicV5fx5YRddomlbIuddx7M\nn5+5eEREJKPMvf73ehcWFnpRUVGmw8geS5bArrvCnDnRPu44ndmLiOQYM/vE3Qsr2y/buu4lHVq2\nhIceSrbHjoVnnslcPCIikjFK9LnqsMPgzDOT7fPPVxe+iEgeUqLPZbffDh07xuOFC+N6fQ5cqhER\nkapTos9lpbvwn31W0+1ERPKMEn2uO+wwOOOMZPuaa2D9+szFIyIidUqJPh9cfz00bRqPP/ssBueJ\niEheUKLPB+3bw0UXJdu//z2sXZu5eEREpM4o0eeLq66Ka/YA06bBqFEZDUdEROqGEn2+2HxzuPLK\nZHvoUFi1KnPxiJdNxwAAHHxJREFUiIhInVCizycXXwxt28bjOXPg/vszG4+IiNQ6Jfp80rw5/O53\nyfaNN8LSpZmLR0REap0Sfb4ZNAi22y4eL1wIf/pTRsMREZHapUSfbxo3hhtuSLbvugsmT85cPCIi\nUquU6PPRgAHwi1/E47VrVRpXRCSHKdHnIzO47z5o2DDab78No0dnNiYREakVSvT5qls3uPTSZPvy\ny+HHHzMXj4iI1Aol+nz2hz8kV7ebPz8q5omISE5Ros9nLVrAnXcm2/fdB0VFmYtHRETSTok+3x17\nbKxwB7Gq3amnqmKeiEgOUaLPd2YwcmQU0wGYMkVd+CIiOUSJXmD77WH48GT79ttjJL6IiNR7SvQS\nzjkn2YXvDqedBsuWZTQkERGpOSV6CWbw8MPQqlW0Z8yAwYMzG5OIiNSYEr0kdegQ1+uLPfggjBmT\nuXhERKTGlOilpAED4Ljjku2BA+GVVzIXj4iI1IgSvZRkBn/+M+y4Y7R/+gmOPlqD80RE6ikleimr\ndWt47TXYdtto//e/cPjhMHFiZuMSEZFqU6KX8m2zTST7du2ivWwZ9OkT8+xFRKTeUKKXiu2wQyT7\n1q2jvWhRTLtbvz6jYYmISNUp0cuG7bJLDMZr0iTaEyfCqFEZDUlERKpOiV4q17MnXHFFsj1kiJa0\nFRGpJ5TopWquvjqu2wMsWABDh2Y2HhERqZI6T/Rm1sfMvjKz6WY2pJznO5nZBDP7zMwmmVm/uo5R\nytGsWcl6+PfcA5MnZy4eERGpkjpN9GZWAIwE+gLdgAFm1q3Ubr8DnnH37sBJwH11GaNswPHHQ+/e\n8XjdOrj44qiLLyIiWauuz+j3BKa7+zfuvgZ4Cuhfah8HNks8bgn8pw7jkw0xgxEjoKAg2q+/Ds8+\nm9mYRERkg+o60XcAZqe05yS2pfojcLKZzQHGAxfWTWhSJbvtBuedl2yfc04sgCMiIlkpGwfjDQBG\nuXtHoB/wuJmVidPMBplZkZkVLViwoM6DzGtDh8LWW8fjRYvgmGNg5crMxiQiIuWq60Q/F9gmpd0x\nsS3VmcAzAO7+AbAJsGXpA7n7A+5e6O6Fbdq0qaVwpVybbw5jx0KjRtH+/HM491xdrxcRyUJ1negn\nAl3NrLOZNSYG240rtc8s4GAAM/sZkeh1yp5t9tknRt4Xe/zxkkvciohIVqjTRO/ua4ELgFeAL4nR\n9ZPNbJiZHZnYbTBwtpn9L/AkcJq7ThWz0qBBcOaZyfall8I772QuHhERKcNyIYcWFhZ6UVFRpsPI\nT6tWQa9eyZXtOnaEL76Ali0zG5eISI4zs0/cvbCy/bJxMJ7UJ5tsElPstkwMo5gzBy67LLMxiYjI\n/1Gil5rbZhu4L6Wu0SOPwPjxmYtHRET+jxK9pMfxx8OJJybbZ58NixdnLh4REQGU6CWd7r0Xttoq\nHv/nPzE4T0REMkqJXtJnyy3hz39Oth99FF58MXPxiIiIEr2k2dFHw8CByfapp8KkSZmLR0QkzynR\nS/qNGAHt28fjxYvh0ENh6tTMxiQikqeU6CX9ttgCXnopOZd+/nw45BAtfiMikgFK9FI7evSIKXbN\nm0d77lw4+OCYZy8iInVGiV5qz777wrhx0KRJtGfMgF/+EpYuzWxcIiJ5RIleatdBB8FzzyVXuvvy\nSzjlFFi/PrNxiYjkCSV6qX39+sFf/pJsjxsHw4ZlLh4RkTyiRC91Y+BAGDw42R46FF54IXPxiIjk\nCSV6qTs33xwD8oqdckp05YuISK1Rope607AhPP00bLddtJcti279zz7LaFgiIrlMiV7qVuvW8Pzz\n0LRptL/9FvbeG+65B9wzGpqISC5Sope6t8ce8Mwz0KJFtNesgYsugmOOgUWLMhubiEiOUaKXzDj8\ncPj0U+jePbnt+eehZ0+YOTNzcYmI5Bglesmcrl3hgw/ibL7Yt99GudzvvstYWCIiuUSJXjKrSRO4\n++4oqtO4cWybPj0WwlE3vohIjSnRS3Y4+ui4bl9QEO0vvoC+fWNkvoiIbDQleske/fvDqFHJ9scf\nx7YVKzIWkohIfadEL9nl5JNh5Mhke8KEuGb/ww+Zi0lEpB5Topfsc955UUWv2Icfwv77w6xZmYtJ\nRKSeUqKX7HTVVTBiBJhFe+rUWPZ28uTMxiUiUs8o0Uv2uvBCeOqp5BK3c+fGmf2rr2Y2LhGRekSJ\nXrLbCSfA3/8Om24a7R9/hD594MYbtaa9iEgVKNFL9jv4YHjzTdh662i7w7XXRsncJUsyGpqISLZT\nopf6oUcP+OQT6NUrue2FF2DPPWHKlMzFJSKS5ZTopf5o1w5eew0uuSS5bdo02GuvqJMvIiJlKNFL\n/dKoEdx5JzzxBDRrFtuWL4/Ketddp+v2IiKlKNFL/TRgALz/PnTunNw2bBgcdRQsXZq5uEREsowS\nvdRfe+wBEyfGAjjFXnwRDjtMyV5EJKHOE72Z9TGzr8xsupkNqWCfE8xsiplNNrMn6jpGqUdat4bx\n4+Hyy5PbPvwwpuBpQRwRkbpN9GZWAIwE+gLdgAFm1q3UPl2Bq4H93H0X4JIyBxJJ1bAh3HYb3Htv\nctsHHyjZi4hQ92f0ewLT3f0bd18DPAX0L7XP2cBId18M4O7z6zhGqa/OPz/K5hZ7/30tdSsiea+u\nE30HYHZKe05iW6odgR3N7D0z+9DM+pR3IDMbZGZFZla0YMGCWgpX6p0LL4S77kq233svruW//nrm\nYhIRyaBsHIzXEOgK9AYGAA+aWavSO7n7A+5e6O6Fbdq0qeMQJatdfHFMwSs2Y0YsdXvWWVFCV0Qk\nj9R1op8LbJPS7pjYlmoOMM7df3L3GcA0IvGLVN0ll8Do0bD55sltDz8M3brF9nXrMhebiEgdqutE\nPxHoamadzawxcBIwrtQ+zxNn85jZlkRX/jd1GaTkiIEDozzuccclt82bB6ecArvvDs89F3XzRURy\nWJ0mendfC1wAvAJ8CTzj7pPNbJiZHZnY7RXgBzObAkwArnD3H+oyTskh7drBmDHw7LPxuNiUKXDs\nsVBYGNfxRURylHkOnNEUFhZ6UVFRpsOQbLdkCdxxR9yWL09uLyiAG26AK66ABtk4bEVEpCwz+8Td\nCyvbT/+qSf5o2RKGDo3BeZdfDptsEtvXrYMhQ+CII+AHdR6JSG5Ropf8s+WWUWDnq69gv/2S28eP\nh+7d4d13MxebiEiaKdFL/urUCSZMiC77YrNnwwEHwKBBOrsXkZygRC/5rVEjuPVWGDeu5FS8Bx+E\nnXaKKXla+lZE6jElehGI6/Offx73xX74IYrs7LQTDB4Mb7wBa9ZkLkYRkY2gRC9SrFOnOLN/4QXY\ndtvk9unTY6T+wQfH9f3TT4epUzMXp4hINSjRi5R25JExz/6aa6BZs5LPLVsGo0ZFhb2TToIvvshI\niCIiVaVEL1KeZs1ibv3ChTEa//zzS57lu8PTT8Nuu8GAASXn5YuIZBElepENado0lrq9996Yf//u\nu9FO9dRT0K+fkr2IZCUlepGqMot59+PHQ1ERHHVU8rl33lGyF5GspEQvsjF69oS//Q1uvz25LTXZ\nT50K998f3fonnwzTpmUuVhHJaw0zHYBIvXbZZXE/eHDcv/MOtGkDq1aV3O/ZZ6P87mWXQUP9byci\ndUdn9CI1ddllJc/sSyf54m1XXQV77RXz9UVE6ogSvUg6XHZZzLU3i3arVtC/f9TU79Ejud+nn8bS\nuOecE+V2RURqmZapFUmnadPgv/+FXXeN5W8B1q6NM/7rroPVq5P7Nm4cCf+3v4Wvv4YPPoD334d5\n86LW/iWXqJtfRCpU1WVqlehF6sq0aZHU33ijavsXFkat/d13r924RKRe0nr0Itlmxx3htdfitvfe\nle9fVBSj+6+7DhYvrv34RCQn6YxeJBPc4e9/h+uvj3K7u+wC++wTt6lTYdiwsgvobL89/M//xO24\n46I2v4jkLXXdi9RnU6fCmWfGNfvyNGkCl14KV18Nm21Wt7GJSFZQ171IfbbzzjEn/777ovu+UaOS\nz69eDTffDF27woMPwrp1mYlTRLKezuhF6oPVq2HSpLhuP2oUfPxxyee32CK6/ffdN+433zxG/69c\nGXP4CwuhbduMhC4itUNd9yK5av16ePJJGDIE5syp2msaNoQRI+Dcc5Nz/UWkXlPXvUiuatAABg6E\nr76KQXtt2lT+mrVr4bzzYt5+8SC/9evhpZeiPv+BB8Ijj0QPgIjkFJ3Ri9R37iUL7hQVRWJv1iyW\n2Z0zJ54vtu++sdDOiBEx6C9Vy5Zw6qlw7LHxg2DxYvjxx9h++OFxTBHJCuq6F5GwciWcfTY88UTN\njrPjjnGMnj1Lbl+zBmbMgB12SFYDFJFap657EQnNmsHo0TB8eHT7p9p001h579ZbY57+hkybFgP9\nbr01uv1nzYJrr4VttolZAjvuCCNHwooVtfe3iEi16YxeJJ+8+moMyCsoiPuzzopueYjk/frrUXZ3\n+vTY3qoVtGgBzz0Hy5cnj9O1K/z73/Ga0rbYIsYD7LFHvE9BQdT133132Hrruvk7RfKAuu5FJH2m\nT4df/xomTiz/ebMYK1CZPfeMVf3694cOHWDp0rgtWxaFf7p0iXEFIlIpJXoRSa+ffoI//hFuuimZ\n1A89NM7eDzwQHn88luqdMaNm79OxY1zvb9s2egKaNIn7bt3gjDP0Q0AkQYleRGpHURG8+y707Qs7\n7VTyubVro5v/pZfiWv26dXFbvBg+/LDmFfx+/nMYO7by8QTF79u4cc3eTySLKdGLSHZZvBjGj4cX\nXoixAOvWRXf9ZpvFOIAFC+Dbb8u/7p+qZcuoDnjUUXGMjz6CcePg00/h++/jtmBBDDzs3x9+97v4\ngSCSY5ToRaT+WbMGZs6MMQFLlkTp3zVrYPZsuOWWkiv69e0Ln3wC8+dXftwjjoDf/z5W/itt3Tq4\n994oGNStWywmdNBBZWcoiGSZrE30ZtYHuBsoAB5y95sr2O9YYCzwP+6+wSyuRC+SB4qKYnnemTM3\n/hgHHQSXXAK/+lUk8kmTYuZB6UGG220Hp58OW20VPzr+/W/45ptkIaLmzePWpUv8iOjdO/2XCebM\ngb/9Ddq1g5/9LGY6NGmS3veQei0rE72ZFQDTgEOBOcBEYIC7Tym136bAy0Bj4AIlehEBYNEi+M1v\n4OWXk9u22iqSbd++kaDbto1tU6bA9dfDs8+WPc4OO0CvXvDYY5G8a2qzzaKU8F57xaDF1atjMaFm\nzaLAUGEhtG5d9eNNmBA/ahYtSm4rKIixCccfH8sTN29e87ilXsvWRL8P8Ed3PyzRvhrA3W8qtd9d\nwD+BK4DLlehF5P+sXx8JetYsOOSQSK4bqsg3eTLceCM8/XTFgwEbN4arroqpfo8/XjLBpkuXLpHw\nd945bjvtFEWGWrQoud/998NFF234B0inTnDPPXDkkemPs6ZWroyZERu7eJK7Fl6qomxN9McBfdz9\nrET7FGAvd78gZZ8ewLXufqyZvUkFid7MBgGDADp16tRzZk2680Qk982aFZX7Hngg6vcX69UrthXP\nIFi1Cp5/Hl58MX4A7LBDnElvv30ksBUr4rZkCbz1Vuy7sf/+mEUhof33hwMOgLffhvvuSz7frh10\n7w5ffhnvUfrf6/79ozdj9uy4Fa9m2KJFVD1s0SJes2JFJOAVK6Kg0SGHQJ8+lS9d/NFHMcjx6KMj\nltImTYqqi9Onx7TKb7+Nz3aTTaJ3pfjWuzcccww0alT++6xaBf/4Bzz1VMzYaNw4yjYPHhy9M1Ku\nepnozawB8AZwmrt/u6FEn0pn9CJSZStWwKOPwhtvRHf7aafVbOCdeyS8F1+E776LJNekSdy+/z6u\n///v/5YcSFgVPXvGj4iOHaO9ciU88wxccQUsXLjx8abq0SM+g5NOgl12SW6fOTPeZ8yYaG+6adRQ\nuPDCSNY//hiDG++7r/JZEsU6dIALLoBBg6Li4pQpsQjT22/HZ7d0adnXNG0aFRyvuALat6/xn8v8\n+bFewxtvxI+P4r+nnsrWRL/Brnszawn8GyiutdkOWAQcuaFkr0QvIllt9Wr417/iB8FXXyVv06aV\nnyhPPDFmAZS3WuAPP8CQIfDQQ+mNcY89Yvnj5ctjPYNVq8rus/POMUbirrsqnu3QsOGGLzs0bRpn\n7EuWVC++4jUVdtopeiVmzowBkt98E8faYYeYNdGtW1wSad483qdRo4h19OiY3pka2+67w4MPRsXG\ndFq7NnpranmRp2xN9A2JwXgHA3OJwXi/dvfJFez/JjqjF5FctWxZFBJ6550oQjRjRpzBXnll5dep\n33svkv1PP8U1+222iVvDhpGsly2LW4MGyZkCTZvGmfQ//hGvr8pAxHbtoqeiIoceGr0inTvHrW3b\nSLwzZ0ZXflFRJNPvv9/w+2y/ffQsnHBCzHIYNgw+/7zy+GrKLMZEXHVVxF7cu7NkCbzySvQ2vPpq\n1IFo3DhZrbF58xhgucUWce8ef/OsWTB3bnzmv/0tXHddrQ2crGqix93r9Ab0I5L9v4lr8QDDiLP2\n0vu+CRRWdsyePXu6iIhUw48/uo8d637CCe6bbOIeqSp5697d/Z133Fevdh8+3H3TTUs+37Gj+5gx\n7uvXV/5eq1a5jxrl/vOfJ1/ftq370Ue733abe1FR2eOsX+8+bpz7fvu5FxSUjW9jb/vt537ZZe5N\nm5Z9rmFD9w4d3Hfbzb1Ro/S837bbur/4Yq38JwSKvAp5VwVzRETy3dKlUbr4ySdh3ry4dn3GGSW7\nnufNi+vyb74ZU/yuvbbsjIHKuMfAvYKCOPuv6uj6n36KLvqpU+O2dClsu20co0uXmN44bVr0VkyZ\nEj0jq1fH69asiffp1SsuO3TtGsecMSPOuF95pXp/w8Y65hi4++7kmIs0yMqu+9qiRC8iItXmHj9u\n7rwzLhcsXlzy+R49YlbDEUfArrsmaySsXh2XRX74IXmDuITSqVMMPHz6abj88uRzED+MJk6MsQZp\noEQvIiJSHatWxXiE+fPjzHvrrWt2vIULY7zFX/4S7d69Y8R/muoEVDXRq5iziIgIJOf/77lnzZM8\nwJZbxuyJt96KhZXuvz8jxYAa1vk7ioiI5JNevaLwUIYq/umMXkREpLZlsKyvEr2IiEgOU6IXERHJ\nYUr0IiIiOUyJXkREJIcp0YuIiOQwJXoREZEcpkQvIiKSw5ToRUREcpgSvYiISA5TohcREclhSvQi\nIiI5LCeWqTWzBcDMNB5yS2BhGo+Xr/Q5poc+x/TQ55ge+hzTIx2f47bu3qaynXIi0aebmRVVZY1f\n2TB9jumhzzE99Dmmhz7H9KjLz1Fd9yIiIjlMiV5ERCSHKdGX74FMB5Aj9Dmmhz7H9NDnmB76HNOj\nzj5HXaMXERHJYTqjFxERyWFK9KWYWR8z+8rMppvZkEzHU1+Y2TZmNsHMppjZZDO7OLF9CzP7p5l9\nnbjfPNOx1gdmVmBmn5nZS4l2ZzP7KPG9fNrMGmc6xmxnZq3MbKyZTTWzL81sH30fq8/MLk38P/2F\nmT1pZpvo+1g5M3vEzOab2Rcp28r9/lkYkfg8J5lZj3TGokSfwswKgJFAX6AbMMDMumU2qnpjLTDY\n3bsBewPnJz67IcDr7t4VeD3RlspdDHyZ0r4FuNPddwAWA2dmJKr65W7gH+6+M7AH8Xnq+1gNZtYB\nuAgodPddgQLgJPR9rIpRQJ9S2yr6/vUFuiZug4D70xmIEn1JewLT3f0bd18DPAX0z3BM9YK7z3P3\nTxOPlxH/qHYgPr9HE7s9ChyVmQjrDzPrCPwKeCjRNuAgYGxiF32OlTCzlkAv4GEAd1/j7j+i7+PG\naAg0NbOGQDNgHvo+Vsrd3wYWldpc0fevP/CYhw+BVmbWPl2xKNGX1AGYndKek9gm1WBm2wHdgY+A\ntu4+L/HUd0DbDIVVn9wFXAmsT7RbAz+6+9pEW9/LynUGFgB/SVwCecjMmqPvY7W4+1xgODCLSPBL\ngE/Q93FjVfT9q9Xco0QvaWVmLYBngUvcfWnqcx5TPDTNYwPM7HBgvrt/kulY6rmGQA/gfnfvDqyg\nVDe9vo+VS1xD7k/8cNoaaE7Z7mjZCHX5/VOiL2kusE1Ku2Nim1SBmTUikvxf3f25xObvi7ugEvfz\nMxVfPbEfcKSZfUtcOjqIuNbcKtF1CvpeVsUcYI67f5RojyUSv76P1XMIMMPdF7j7T8BzxHdU38eN\nU9H3r1ZzjxJ9SROBrokRpY2JQSfjMhxTvZC4jvww8KW735Hy1Djg1MTjU4EX6jq2+sTdr3b3ju6+\nHfH9e8PdBwITgOMSu+lzrIS7fwfMNrOdEpsOBqag72N1zQL2NrNmif/Hiz9HfR83TkXfv3HAbxKj\n7/cGlqR08deYCuaUYmb9iGukBcAj7n5DhkOqF8xsf+Ad4F8kry1fQ1ynfwboRKwweIK7lx6gIuUw\ns97A5e5+uJl1Ic7wtwA+A05299WZjC/bmdnPiQGNjYFvgNOJkxt9H6vBzIYCJxIzaz4DziKuH+v7\nuAFm9iTQm1il7nvgOuB5yvn+JX5E3UtcFlkJnO7uRWmLRYleREQkd6nrXkREJIcp0YuIiOQwJXoR\nEZEcpkQvIiKSw5ToRUREcpgSvUgOMLPTzMwruP2Y4dhGmdmcTMYgks8aVr6LiNQjxxNV4VKtLW9H\nEckPSvQiueVzd5+e6SBEJHuo614kj6R08fcys+fNbLmZ/WBmI82saal925vZY2a20MxWm9kkMzu5\nnGN2NrPHzey7xH7fmNnd5ezX3czeMbOVZva1mZ1b6vl2Zvaomf0ncZx5ZvaSmW2V/k9CJH/ojF4k\ntxSkLDZSbL27ry+1bTRRivM+YE/gD8TKZKcBJJZ0fQvYnChlPBs4GXjczJq5+wOJ/ToDHxNlO/8A\nfE2U9/xlqffbDHiCKC89jChHe7+ZfeXuExL7PA5sC1yReL+2RG31ZhvzQYhIUKIXyS1Ty9n2MnB4\nqW3j3f3yxONXzcyBYWZ2o7tPIxJxV+BAd38zsd/fzawtcL2ZPezu64ChQFNgD3f/T8rxHy31fpsC\n5xUndTN7GzgMGEAskAKwD3CNu/815XVjqvRXi0iFlOhFcsvRlB2MV96o+2dKtZ8CrifO7qcBvYC5\nKUm+2GjgL0A3YgGjXwIvlUry5VmZcuaOu682s2nE2X+xicAViQU+3gC+cC3GIVJjSvQiueWLKg7G\n+76CdofE/RZAectkfpfyPEBryv6wKM/icratBjZJaZ9IrPB1JdHFP8/M/gxcX86lBxGpIg3GE8lP\nbStoz03cLwLalfO6dinPAywk+eOgRtx9vruf7+4dgJ2BUcSlgXPScXyRfKVEL5KfTijVPglYD3yU\naL8FdDSz/Urt92tgPjAl0X4VONzM2qczOHf/yt2vIXoCdk3nsUXyjbruRXLLz81sy3K2F7l7auGc\nfmZ2G5Go9yS6zB9z968Tz48CLgaeM7Nrie75gcChwDmJgXgkXtcPeN/MbgSmE2f4fdy9zFS8iphZ\nS+A14K/EgMKfgP7EqP9Xq3ocESlLiV4kt1Q0Sr0N0c1e7GRgMPBbYA3wIFA8Ch93X2FmvwBuBW4m\nRs1/BZzi7qNT9vvWzPYmBvLdBLQguv9fqGbcq4BPgbOJKXbrE+830N2reywRSWEa1CqSP8zsNGLU\nfFdV0BPJD7pGLyIiksOU6EVERHKYuu5FRERymM7oRUREcpgSvYiISA5TohcREclhSvQiIiI5TIle\nREQkhynRi4iI5LD/D23+pvFLI6EUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XX/ePnYP77vP/iohIjaZEXxsUFPjafMQZZ8B222UvHhERyRgl+tpg+HCYOdM/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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwK03BE6WYtY",
        "colab_type": "text"
      },
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jv8pXMQwZZoI",
        "colab_type": "text"
      },
      "source": [
        "# Data augmentation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr0dW7KxeHbc",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7Iox6xhbQpg",
        "colab_type": "text"
      },
      "source": [
        "# Creating and training 2nd model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTiS-2KBe5-S",
        "colab_type": "code",
        "outputId": "48d9917d-af35-4266-8dde-9d34a6e787f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3437
        }
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "113/113 [==============================] - 10s 87ms/step - loss: 1.8276 - acc: 0.2455 - val_loss: 1.8093 - val_acc: 0.2708\n",
            "Epoch 2/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.7515 - acc: 0.2834 - val_loss: 1.7041 - val_acc: 0.3583\n",
            "Epoch 3/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.6789 - acc: 0.3256 - val_loss: 1.5595 - val_acc: 0.4113\n",
            "Epoch 4/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.6113 - acc: 0.3652 - val_loss: 1.5092 - val_acc: 0.4166\n",
            "Epoch 5/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.5454 - acc: 0.3986 - val_loss: 1.4147 - val_acc: 0.4553\n",
            "Epoch 6/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.4936 - acc: 0.4212 - val_loss: 1.4186 - val_acc: 0.4413\n",
            "Epoch 7/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.4372 - acc: 0.4460 - val_loss: 1.3468 - val_acc: 0.4974\n",
            "Epoch 8/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.3967 - acc: 0.4641 - val_loss: 1.2571 - val_acc: 0.5199\n",
            "Epoch 9/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.3611 - acc: 0.4778 - val_loss: 1.2255 - val_acc: 0.5341\n",
            "Epoch 10/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.3342 - acc: 0.4905 - val_loss: 1.1971 - val_acc: 0.5400\n",
            "Epoch 11/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.3115 - acc: 0.5002 - val_loss: 1.2649 - val_acc: 0.5194\n",
            "Epoch 12/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.2915 - acc: 0.5061 - val_loss: 1.2760 - val_acc: 0.5160\n",
            "Epoch 13/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.2736 - acc: 0.5163 - val_loss: 1.1524 - val_acc: 0.5662\n",
            "Epoch 14/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.2549 - acc: 0.5247 - val_loss: 1.1514 - val_acc: 0.5637\n",
            "Epoch 15/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.2492 - acc: 0.5271 - val_loss: 1.1542 - val_acc: 0.5598\n",
            "Epoch 16/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.2341 - acc: 0.5347 - val_loss: 1.1252 - val_acc: 0.5743\n",
            "Epoch 17/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.2236 - acc: 0.5367 - val_loss: 1.1832 - val_acc: 0.5606\n",
            "Epoch 18/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.2111 - acc: 0.5440 - val_loss: 1.1586 - val_acc: 0.5587\n",
            "Epoch 19/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1991 - acc: 0.5419 - val_loss: 1.1330 - val_acc: 0.5653\n",
            "Epoch 20/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.2025 - acc: 0.5451 - val_loss: 1.1314 - val_acc: 0.5701\n",
            "Epoch 21/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1906 - acc: 0.5510 - val_loss: 1.0998 - val_acc: 0.5829\n",
            "Epoch 22/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1859 - acc: 0.5538 - val_loss: 1.0566 - val_acc: 0.6066\n",
            "Epoch 23/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1794 - acc: 0.5509 - val_loss: 1.0851 - val_acc: 0.5952\n",
            "Epoch 24/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.1643 - acc: 0.5596 - val_loss: 1.0701 - val_acc: 0.5991\n",
            "Epoch 25/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.1690 - acc: 0.5614 - val_loss: 1.1645 - val_acc: 0.5587\n",
            "Epoch 26/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.1569 - acc: 0.5631 - val_loss: 1.0851 - val_acc: 0.6018\n",
            "Epoch 27/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1556 - acc: 0.5641 - val_loss: 1.0731 - val_acc: 0.5921\n",
            "Epoch 28/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1589 - acc: 0.5625 - val_loss: 1.1182 - val_acc: 0.5801\n",
            "Epoch 29/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1469 - acc: 0.5653 - val_loss: 1.1100 - val_acc: 0.5843\n",
            "Epoch 30/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1335 - acc: 0.5729 - val_loss: 1.0964 - val_acc: 0.5932\n",
            "Epoch 31/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1392 - acc: 0.5710 - val_loss: 1.1115 - val_acc: 0.5924\n",
            "Epoch 32/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1327 - acc: 0.5739 - val_loss: 1.0601 - val_acc: 0.6077\n",
            "Epoch 33/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.1355 - acc: 0.5713 - val_loss: 1.0887 - val_acc: 0.5857\n",
            "Epoch 34/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1271 - acc: 0.5778 - val_loss: 1.1022 - val_acc: 0.5988\n",
            "Epoch 35/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.1235 - acc: 0.5759 - val_loss: 1.0419 - val_acc: 0.6169\n",
            "Epoch 36/100\n",
            "113/113 [==============================] - 9s 82ms/step - loss: 1.1196 - acc: 0.5790 - val_loss: 1.0347 - val_acc: 0.6144\n",
            "Epoch 37/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.1153 - acc: 0.5804 - val_loss: 1.0629 - val_acc: 0.6105\n",
            "Epoch 38/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1137 - acc: 0.5787 - val_loss: 1.0889 - val_acc: 0.5940\n",
            "Epoch 39/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1084 - acc: 0.5821 - val_loss: 1.0868 - val_acc: 0.5946\n",
            "Epoch 40/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.1100 - acc: 0.5838 - val_loss: 1.0821 - val_acc: 0.5999\n",
            "Epoch 41/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1123 - acc: 0.5835 - val_loss: 1.0342 - val_acc: 0.6108\n",
            "Epoch 42/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0996 - acc: 0.5839 - val_loss: 1.0789 - val_acc: 0.5952\n",
            "Epoch 43/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.1025 - acc: 0.5866 - val_loss: 1.0708 - val_acc: 0.6004\n",
            "Epoch 44/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0964 - acc: 0.5876 - val_loss: 1.0546 - val_acc: 0.6149\n",
            "Epoch 45/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.0917 - acc: 0.5881 - val_loss: 1.0265 - val_acc: 0.6177\n",
            "Epoch 46/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0951 - acc: 0.5906 - val_loss: 1.0618 - val_acc: 0.6041\n",
            "Epoch 47/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0935 - acc: 0.5906 - val_loss: 1.0712 - val_acc: 0.6091\n",
            "Epoch 48/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0918 - acc: 0.5903 - val_loss: 1.0090 - val_acc: 0.6216\n",
            "Epoch 49/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0957 - acc: 0.5904 - val_loss: 1.0810 - val_acc: 0.5982\n",
            "Epoch 50/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0845 - acc: 0.5917 - val_loss: 1.0337 - val_acc: 0.6186\n",
            "Epoch 51/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0869 - acc: 0.5941 - val_loss: 1.0537 - val_acc: 0.6046\n",
            "Epoch 52/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0846 - acc: 0.5925 - val_loss: 1.0039 - val_acc: 0.6258\n",
            "Epoch 53/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0788 - acc: 0.5966 - val_loss: 1.0467 - val_acc: 0.6124\n",
            "Epoch 54/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0843 - acc: 0.5941 - val_loss: 1.0764 - val_acc: 0.6016\n",
            "Epoch 55/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0788 - acc: 0.5986 - val_loss: 1.0358 - val_acc: 0.6119\n",
            "Epoch 56/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0798 - acc: 0.6001 - val_loss: 1.0657 - val_acc: 0.6082\n",
            "Epoch 57/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0739 - acc: 0.5985 - val_loss: 1.0741 - val_acc: 0.6147\n",
            "Epoch 58/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0807 - acc: 0.5966 - val_loss: 1.0413 - val_acc: 0.6239\n",
            "Epoch 59/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0792 - acc: 0.5975 - val_loss: 1.0217 - val_acc: 0.6205\n",
            "Epoch 60/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0777 - acc: 0.5971 - val_loss: 1.0168 - val_acc: 0.6227\n",
            "Epoch 61/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0730 - acc: 0.5991 - val_loss: 0.9884 - val_acc: 0.6408\n",
            "Epoch 62/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0732 - acc: 0.5968 - val_loss: 1.0618 - val_acc: 0.6080\n",
            "Epoch 63/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0718 - acc: 0.5974 - val_loss: 1.0827 - val_acc: 0.6035\n",
            "Epoch 64/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0707 - acc: 0.5984 - val_loss: 1.0303 - val_acc: 0.6222\n",
            "Epoch 65/100\n",
            "113/113 [==============================] - 9s 79ms/step - loss: 1.0708 - acc: 0.5960 - val_loss: 0.9941 - val_acc: 0.6358\n",
            "Epoch 66/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0672 - acc: 0.6032 - val_loss: 1.0880 - val_acc: 0.6110\n",
            "Epoch 67/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0710 - acc: 0.6016 - val_loss: 1.0895 - val_acc: 0.6043\n",
            "Epoch 68/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0637 - acc: 0.5978 - val_loss: 1.0060 - val_acc: 0.6222\n",
            "Epoch 69/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0655 - acc: 0.6007 - val_loss: 1.0656 - val_acc: 0.5999\n",
            "Epoch 70/100\n",
            "113/113 [==============================] - 9s 82ms/step - loss: 1.0674 - acc: 0.6038 - val_loss: 1.0189 - val_acc: 0.6230\n",
            "Epoch 71/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0684 - acc: 0.5997 - val_loss: 1.0127 - val_acc: 0.6202\n",
            "Epoch 72/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0701 - acc: 0.5984 - val_loss: 0.9822 - val_acc: 0.6386\n",
            "Epoch 73/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0593 - acc: 0.6043 - val_loss: 1.0280 - val_acc: 0.6133\n",
            "Epoch 74/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0594 - acc: 0.6049 - val_loss: 1.1052 - val_acc: 0.6133\n",
            "Epoch 75/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0582 - acc: 0.6078 - val_loss: 1.0246 - val_acc: 0.6213\n",
            "Epoch 76/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0661 - acc: 0.5986 - val_loss: 1.0092 - val_acc: 0.6205\n",
            "Epoch 77/100\n",
            "113/113 [==============================] - 9s 82ms/step - loss: 1.0639 - acc: 0.6053 - val_loss: 1.0265 - val_acc: 0.6177\n",
            "Epoch 78/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0589 - acc: 0.6014 - val_loss: 0.9939 - val_acc: 0.6317\n",
            "Epoch 79/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0557 - acc: 0.6023 - val_loss: 1.0034 - val_acc: 0.6222\n",
            "Epoch 80/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0637 - acc: 0.6026 - val_loss: 1.0093 - val_acc: 0.6261\n",
            "Epoch 81/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0594 - acc: 0.6069 - val_loss: 1.0054 - val_acc: 0.6247\n",
            "Epoch 82/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0542 - acc: 0.6096 - val_loss: 1.0641 - val_acc: 0.5963\n",
            "Epoch 83/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0525 - acc: 0.6056 - val_loss: 1.0892 - val_acc: 0.5952\n",
            "Epoch 84/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0568 - acc: 0.6067 - val_loss: 1.0052 - val_acc: 0.6255\n",
            "Epoch 85/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0533 - acc: 0.6064 - val_loss: 0.9907 - val_acc: 0.6303\n",
            "Epoch 86/100\n",
            "113/113 [==============================] - 9s 82ms/step - loss: 1.0519 - acc: 0.6052 - val_loss: 1.0258 - val_acc: 0.6283\n",
            "Epoch 87/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0551 - acc: 0.6052 - val_loss: 0.9753 - val_acc: 0.6414\n",
            "Epoch 88/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0504 - acc: 0.6112 - val_loss: 1.0011 - val_acc: 0.6250\n",
            "Epoch 89/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0575 - acc: 0.6061 - val_loss: 1.0701 - val_acc: 0.6021\n",
            "Epoch 90/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0521 - acc: 0.6075 - val_loss: 1.0054 - val_acc: 0.6247\n",
            "Epoch 91/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0569 - acc: 0.6083 - val_loss: 1.0250 - val_acc: 0.6233\n",
            "Epoch 92/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0552 - acc: 0.6074 - val_loss: 1.0831 - val_acc: 0.6127\n",
            "Epoch 93/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0513 - acc: 0.6086 - val_loss: 1.2403 - val_acc: 0.5876\n",
            "Epoch 94/100\n",
            "113/113 [==============================] - 9s 82ms/step - loss: 1.0521 - acc: 0.6077 - val_loss: 1.0529 - val_acc: 0.6225\n",
            "Epoch 95/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0509 - acc: 0.6062 - val_loss: 1.0055 - val_acc: 0.6252\n",
            "Epoch 96/100\n",
            "113/113 [==============================] - 9s 80ms/step - loss: 1.0522 - acc: 0.6088 - val_loss: 0.9993 - val_acc: 0.6286\n",
            "Epoch 97/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0554 - acc: 0.6087 - val_loss: 1.0356 - val_acc: 0.6211\n",
            "Epoch 98/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0549 - acc: 0.6081 - val_loss: 1.0446 - val_acc: 0.6057\n",
            "Epoch 99/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0500 - acc: 0.6066 - val_loss: 1.1026 - val_acc: 0.6158\n",
            "Epoch 100/100\n",
            "113/113 [==============================] - 9s 81ms/step - loss: 1.0546 - acc: 0.6045 - val_loss: 1.0568 - val_acc: 0.6043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_gIIyLDbZws",
        "colab_type": "text"
      },
      "source": [
        "# Evaluating 2nd model and plotting curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-WznumziU3k",
        "colab_type": "code",
        "outputId": "500e692b-da4b-47a9-f749-e0c319ffcbab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Evaluating model performance\n",
        "\n",
        "model2.evaluate(test_data, test_labels_one_hot)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 0s 79us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0567782257685392, 0.6043466146641968]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WOF1I14foGu",
        "colab_type": "code",
        "outputId": "938ba887-dd8a-45d2-fd8d-333138900e8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        }
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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awH7xxRdFfo/+3xFQ5OOtt97Kv/att96yJ598sk1JSbHVq1e3p556qn3vvfcC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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I0K9fUN+mTVD/44/pn3HccUHfK66wugEDgrp//atsr6GgQHXyZNU1a4K6fv2C+z/+uNUN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\nNAFQSimlqiBNAJRSSqkq6P8B6k/px8grdp4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2b6yGRcPblXE",
        "colab_type": "text"
      },
      "source": [
        "#Saving our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3QawkGqZLes",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Saving our model\n",
        "model2.save('my_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WV8YAojBcnO2",
        "colab_type": "text"
      },
      "source": [
        "#Cropping image to get face"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5QLuaJGuJe6",
        "colab_type": "code",
        "outputId": "4ac6f425-cc05-451c-980e-8d13979d6f02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "#Cropping image\n",
        "\n",
        "from PIL import Image\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "faces = []\n",
        "\n",
        "img = Image.open(\"surprise.jpg\")\n",
        "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "faces = face_cascade.detectMultiScale(np.asarray(img), 1.3, 5)\n",
        "for (x, y, w, h) in faces:\n",
        "  if len(faces) == 1: #Use simple check if one face is detected, or multiple (measurement error unless multiple persons on image)\n",
        "    crop_img = img.crop((x,y,x+w,y+h))\n",
        "  else:\n",
        "    print(\"multiple faces detected, passing over image\")\n",
        "\n",
        "plt.imshow(crop_img)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd04cf964a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BpTIiQAi8oIwATGEQZIVIT4JTJteVl0YUkYSwWCsQwhbVldztKx8DhlZJjKAyXJXiF5HL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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tB3qbXrRcztm",
        "colab_type": "text"
      },
      "source": [
        "#Resizing(acc. to model) and predicting results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIFJO_5oInO4",
        "colab_type": "code",
        "outputId": "2839c917-504a-4672-a3c1-f37490e41c2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#Resizing image to required size and processing\n",
        "test_image = crop_img.resize((48,48),Image.ANTIALIAS)\n",
        "test_image = np.array(test_image)\n",
        "gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "#scale pixels values to lie between 0 and 1 because we did same to our train and test set\n",
        "gray = gray/255\n",
        "\n",
        "#reshaping image (-1 is used to automatically fit an integer at it's place to match dimension of original image)\n",
        "gray = gray.reshape(-1, 48, 48, 1)\n",
        "\n",
        "res = model2.predict(gray)\n",
        "\n",
        "#argmax returns index of max value\n",
        "result_num = np.argmax(res)\n",
        "\n",
        "print(\"Probabilities are \" + str(res[0])+\"\\n\")\n",
        "print(\"Emotion is \"+get_label(result_num))"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probabilities are [8.6966500e-02 3.1855168e-05 5.3059530e-02 1.3219425e-02 1.3421694e-02\n",
            " 8.0609381e-01 2.7207172e-02]\n",
            "\n",
            "Emotion is Surprise\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o39xb9HMfES4",
        "colab_type": "text"
      },
      "source": [
        "**Here the probabilities are as follows :**\n",
        "\n",
        "* 8.6966500e-02 - Angry\n",
        "* 3.1855168e-05 - Disgust\n",
        "* 5.3059530e-02 - Fear\n",
        "* 1.3219425e-02 - Happy\n",
        "* 1.3421694e-02 - Sad\n",
        "* 8.0609381e-01 - Surprise\n",
        "* 2.7207172e-02 - Neutral\n",
        "\n",
        "Since probability of being surprise is highest (0.81), emotion is **surprise** ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eANsJ1XdTv8",
        "colab_type": "text"
      },
      "source": [
        "#Loading pre-trained model\n",
        "\n",
        "*Here I'm using fer2013_mini_XCEPTION.110-0.65 model.*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD0AT3bvbSVI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "pretrained_model = load_model(\"fer2013_mini_XCEPTION.110-0.65.hdf5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHyVMbwR3r3w",
        "colab_type": "text"
      },
      "source": [
        "#Prediction using pre-trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74wtXaCi35ue",
        "colab_type": "code",
        "outputId": "6f743103-4822-4da2-edcf-70fc84eb3373",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Resizing image to required size\n",
        "test_image = crop_img.resize((64,64),Image.ANTIALIAS)\n",
        "\n",
        "#Converting image to array\n",
        "test_image = np.array(test_image)\n",
        "\n",
        "#converting to grayscale\n",
        "gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "#scale pixels values to lie between 0 and 1 because we did same to our train and test set\n",
        "gray = gray/255\n",
        "\n",
        "#reshaping image (-1 is used to automatically fit an integer at it's place to match dimension of original image)\n",
        "gray = gray.reshape(-1, 64, 64, 1)\n",
        "\n",
        "res = pretrained_model.predict(gray)\n",
        "\n",
        "#argmax returns index of max value\n",
        "result_num = np.argmax(res[0])\n",
        "\n",
        "# print predictions\n",
        "print(\"\\nProbabilities are \" + str(res[0])+\"\\n\")\n",
        "print(\"Emotion is \"+ get_label(result_num))"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Probabilities are [2.3769777e-02 6.4130991e-06 2.7361888e-02 2.9007331e-02 7.8126509e-03\n",
            " 7.5060266e-01 1.6143923e-01]\n",
            "\n",
            "Emotion is Surprise\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sU2TaWzlfNDA",
        "colab_type": "text"
      },
      "source": [
        "**Here the probabilities are as follows :**\n",
        "\n",
        "* 2.3769777e-02  - Angry\n",
        "* 6.4130991e-06 - Disgust\n",
        "* 2.7361888e-02 - Fear\n",
        "* 2.9007331e-02 - Happy\n",
        "* 7.8126509e-03 - Sad\n",
        "* 7.5060266e-01 - Surprise\n",
        "* 1.6143923e-01 - Neutral\n",
        "\n",
        "Since probability of being surprise is highest (0.75), emotion is **surprise** .\n",
        "\n",
        "***Our model is predicting better than pre-trained model on this image !***\n"
      ]
    }
  ]
}