Image/Model_for_static_image.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtG7TCY31F5h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "774b3b87-9cf2-4751-f81f-0bd074b61752"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import itertools \n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from keras import layers\n",
        "from keras.layers import Conv2D, Input, Dense, MaxPool2D,BatchNormalization,Flatten,ZeroPadding2D,Activation,Dropout\n",
        "from keras.models import Model\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPPEHKF4zitX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv('/content/drive/My Drive/data/FER_preprocessed.csv')\n",
        "datapoints = data['pixels'].tolist()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFCUhipd1DxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "width, height = 48, 48\n",
        "X = []\n",
        "for xseq in datapoints:\n",
        "    x = [int(xp) for xp in xseq.split(' ')]\n",
        "    x = np.asarray(x).reshape(width, height)\n",
        "    X.append(x.astype('float64'))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWe8JTOK1Fps",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = pd.get_dummies(data['emotion']).to_numpy()\n",
        "x = X\n",
        "x -= np.mean(x, axis=0)\n",
        "x /= np.std(x, axis=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D707jhAkxu-P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d8478e1b-086f-4c4a-ebea-0a7c6a855be4"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDI3s5qa1ouL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gg_6ihx3LY6",
        "colab_type": "text"
      },
      "source": [
        "Define the model with input as input_shape i.e the shape of image\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSQkN5FnOs8d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def classifierModel(input_shape):\n",
        "  X_input = Input(input_shape)\n",
        "  '''\n",
        "  Layer 1\n",
        "  '''\n",
        "  X = Conv2D(64,(7,7),strides=(2,2),name = 'conv2')(X_input)\n",
        "  X = Activation('relu')(X)\n",
        "  X = MaxPool2D((3,3),strides=(2,2),name='max_pool2')(X)\n",
        "  X = BatchNormalization()(X)\n",
        "\n",
        "  '''\n",
        "  Layer 2\n",
        "  '''\n",
        "  X1 = Conv2D(96,(1,1),name='conv4')(X)\n",
        "  X1 = Activation('relu')(X1)\n",
        "  X2 = MaxPool2D((3,3),strides=(1,1),name='max_pool3')(X)\n",
        "  X3 = Conv2D(208,(3,3),name='conv5')(X1)\n",
        "  X3 = Activation('relu')(X3)\n",
        "  X4 = Conv2D(64,(1,1),name='conv6')(X2)\n",
        "  X4 = Activation('relu')(X4)\n",
        "  print(X1.shape,X2.shape)\n",
        "  chunk_1 = keras.layers.concatenate([X3,X4],axis=3)\n",
        "  '''\n",
        "  Layer 3\n",
        "  '''\n",
        "  X5 = Conv2D(96,(1,1),name='conv7')(chunk_1)\n",
        "  X5 = Activation('relu')(X5)\n",
        "  X6 = MaxPool2D((3,3),strides=(1,1),name='max_pool4')(chunk_1)\n",
        "  X7 = Conv2D(208, (3,3),name='conv8')(X5)\n",
        "  X7 = Activation('relu')(X7)\n",
        "  X8 = Conv2D(64,(1,1),name='conv9')(X6)\n",
        "  X8 = Activation('relu')(X8)\n",
        "  chunk_2 = keras.layers.concatenate([X7,X8],axis=3)\n",
        "  '''\n",
        "  Layer 4\n",
        "  '''\n",
        "  out = Flatten()(chunk_2)\n",
        "  out = Dropout(0.5)(out)\n",
        "  out = Dense(7,activation='softmax',use_bias=True)(out)\n",
        "  model = Model(inputs=X_input,outputs=out,name='Second')\n",
        "  return model"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RqdSAfPTkCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "991b13bd-74cd-469e-e7a6-e3cb1c9c1185"
      },
      "source": [
        "model1 = classifierModel((48,48,1))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(None, 10, 10, 96) (None, 8, 8, 64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MR7paGNHT796",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVxXZ-p4UQFA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "5c81c318-c964-4e39-e66d-cbe5c5a5f716"
      },
      "source": [
        "model1.fit(X_train,y_train,epochs=1,batch_size=128,validation_data=(X_test,y_test),shuffle=True)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 28709 samples, validate on 7178 samples\n",
            "Epoch 1/1\n",
            "28709/28709 [==============================] - 131s 5ms/step - loss: 1.6418 - accuracy: 0.3528 - val_loss: 1.6131 - val_accuracy: 0.3710\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fd6c64ca438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwXJnRaj-ODu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "0d2402cb-b884-413a-b612-2b83b1671186"
      },
      "source": [
        "model1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"Second\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 48, 48, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2 (Conv2D)                  (None, 21, 21, 64)   3200        input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 21, 21, 64)   0           conv2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "max_pool2 (MaxPooling2D)        (None, 10, 10, 64)   0           activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 10, 10, 64)   256         max_pool2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv4 (Conv2D)                  (None, 10, 10, 96)   6240        batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 10, 10, 96)   0           conv4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "max_pool3 (MaxPooling2D)        (None, 8, 8, 64)     0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5 (Conv2D)                  (None, 8, 8, 208)    179920      activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv6 (Conv2D)                  (None, 8, 8, 64)     4160        max_pool3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 8, 8, 208)    0           conv5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 8, 8, 64)     0           conv6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 8, 8, 272)    0           activation_10[0][0]              \n",
            "                                                                 activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv7 (Conv2D)                  (None, 8, 8, 96)     26208       concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 8, 8, 96)     0           conv7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "max_pool4 (MaxPooling2D)        (None, 6, 6, 272)    0           concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv8 (Conv2D)                  (None, 6, 6, 208)    179920      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv9 (Conv2D)                  (None, 6, 6, 64)     17472       max_pool4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 6, 6, 208)    0           conv8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 6, 6, 64)     0           conv9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 6, 6, 272)    0           activation_13[0][0]              \n",
            "                                                                 activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 9792)         0           concatenate_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 9792)         0           flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 7)            68551       dropout_2[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 485,927\n",
            "Trainable params: 485,799\n",
            "Non-trainable params: 128\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5yvZp31TedO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model1.save('model.h5')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLgUAPdQv0WE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d3e16cb0-0d56-4faf-f7e9-116831aeef47"
      },
      "source": [
        "kf = KFold(n_splits=10)\n",
        "kf.get_n_splits(x)\n",
        "max_accuracy = 0\n",
        "print(kf)\n",
        "model2 = classifierModel((48,48,1))\n",
        "model2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "for train_index,test_index in kf.split(x,np.asarray(data['emotion'])):\n",
        "  #print(\"Running Fold\", i+1, \"/\", n_folds)\n",
        "  #print(train_index,test_index)\n",
        "  X_train, X_test = x[train_index], x[test_index]\n",
        "  y_train, y_test = y[train_index], y[test_index]\n",
        "  X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)\n",
        "  X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)\n",
        "\n",
        "  history = model2.fit(X_train,y_train,epochs=13,batch_size=128,validation_data=(X_test,y_test))\n",
        "  if history.history['val_accuracy'][-1]>max_accuracy:\n",
        "    model2.save('my_model_10fold.h5') \n",
        "    max_accuracy = history.history['val_accuracy'][-1]\n",
        "    print(\"New Model Saved\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KFold(n_splits=10, random_state=None, shuffle=False)\n",
            "(None, 10, 10, 96) (None, 8, 8, 64)\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 1.6386 - accuracy: 0.3564 - val_loss: 1.5937 - val_accuracy: 0.4230\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 1.4285 - accuracy: 0.4485 - val_loss: 1.3940 - val_accuracy: 0.4603\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 1.3317 - accuracy: 0.4902 - val_loss: 1.3111 - val_accuracy: 0.4974\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 1.2628 - accuracy: 0.5217 - val_loss: 1.2961 - val_accuracy: 0.5082\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 1.2129 - accuracy: 0.5401 - val_loss: 1.3171 - val_accuracy: 0.5052\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 1.1593 - accuracy: 0.5602 - val_loss: 1.2490 - val_accuracy: 0.5263\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 1.1019 - accuracy: 0.5841 - val_loss: 1.2412 - val_accuracy: 0.5347\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 1.0522 - accuracy: 0.6059 - val_loss: 1.2250 - val_accuracy: 0.5311\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.9981 - accuracy: 0.6227 - val_loss: 1.2351 - val_accuracy: 0.5422\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.9537 - accuracy: 0.6449 - val_loss: 1.2422 - val_accuracy: 0.5495\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.8960 - accuracy: 0.6668 - val_loss: 1.2527 - val_accuracy: 0.5469\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.8448 - accuracy: 0.6873 - val_loss: 1.2642 - val_accuracy: 0.5425\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.8028 - accuracy: 0.7035 - val_loss: 1.2694 - val_accuracy: 0.5489\n",
            "New Model Saved\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.8309 - accuracy: 0.6990 - val_loss: 0.6836 - val_accuracy: 0.7626\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.7719 - accuracy: 0.7171 - val_loss: 0.7048 - val_accuracy: 0.7529\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.7164 - accuracy: 0.7371 - val_loss: 0.7370 - val_accuracy: 0.7364\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.6651 - accuracy: 0.7545 - val_loss: 0.7920 - val_accuracy: 0.7125\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.6284 - accuracy: 0.7684 - val_loss: 0.7821 - val_accuracy: 0.7035\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.5931 - accuracy: 0.7822 - val_loss: 0.8166 - val_accuracy: 0.7122\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.5527 - accuracy: 0.7956 - val_loss: 0.8260 - val_accuracy: 0.6977\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.5249 - accuracy: 0.8062 - val_loss: 0.8351 - val_accuracy: 0.7010\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 134s 4ms/step - loss: 0.5017 - accuracy: 0.8191 - val_loss: 0.8528 - val_accuracy: 0.7069\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.4728 - accuracy: 0.8266 - val_loss: 0.8790 - val_accuracy: 0.7058\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.4566 - accuracy: 0.8337 - val_loss: 0.9299 - val_accuracy: 0.6868\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.4326 - accuracy: 0.8433 - val_loss: 0.9819 - val_accuracy: 0.6865\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.4247 - accuracy: 0.8450 - val_loss: 0.9694 - val_accuracy: 0.6743\n",
            "New Model Saved\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.5242 - accuracy: 0.8159 - val_loss: 0.2479 - val_accuracy: 0.9326\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.4639 - accuracy: 0.8328 - val_loss: 0.2749 - val_accuracy: 0.9195\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.4262 - accuracy: 0.8479 - val_loss: 0.3059 - val_accuracy: 0.8983\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.4094 - accuracy: 0.8542 - val_loss: 0.3230 - val_accuracy: 0.8894\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.3865 - accuracy: 0.8595 - val_loss: 0.3722 - val_accuracy: 0.8696\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.3785 - accuracy: 0.8635 - val_loss: 0.3907 - val_accuracy: 0.8562\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.3669 - accuracy: 0.8660 - val_loss: 0.4048 - val_accuracy: 0.8501\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.3521 - accuracy: 0.8735 - val_loss: 0.4374 - val_accuracy: 0.8370\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.3331 - accuracy: 0.8796 - val_loss: 0.4572 - val_accuracy: 0.8331\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.3330 - accuracy: 0.8804 - val_loss: 0.4667 - val_accuracy: 0.8275\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.3186 - accuracy: 0.8858 - val_loss: 0.5438 - val_accuracy: 0.8044\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.3256 - accuracy: 0.8846 - val_loss: 0.5272 - val_accuracy: 0.8066\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.3155 - accuracy: 0.8895 - val_loss: 0.5266 - val_accuracy: 0.8116\n",
            "New Model Saved\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.3828 - accuracy: 0.8653 - val_loss: 0.1584 - val_accuracy: 0.9507\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.3401 - accuracy: 0.8805 - val_loss: 0.1829 - val_accuracy: 0.9395\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.3215 - accuracy: 0.8873 - val_loss: 0.1753 - val_accuracy: 0.9471\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.3081 - accuracy: 0.8913 - val_loss: 0.1940 - val_accuracy: 0.9415\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.3000 - accuracy: 0.8940 - val_loss: 0.2244 - val_accuracy: 0.9237\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2927 - accuracy: 0.8975 - val_loss: 0.2364 - val_accuracy: 0.9120\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.2868 - accuracy: 0.8982 - val_loss: 0.2642 - val_accuracy: 0.9086\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2884 - accuracy: 0.8984 - val_loss: 0.2741 - val_accuracy: 0.9053\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2765 - accuracy: 0.9019 - val_loss: 0.3007 - val_accuracy: 0.8933\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2701 - accuracy: 0.9033 - val_loss: 0.3239 - val_accuracy: 0.8858\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2684 - accuracy: 0.9045 - val_loss: 0.3473 - val_accuracy: 0.8716\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2723 - accuracy: 0.9051 - val_loss: 0.3657 - val_accuracy: 0.8721\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2641 - accuracy: 0.9063 - val_loss: 0.3798 - val_accuracy: 0.8646\n",
            "New Model Saved\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.3226 - accuracy: 0.8880 - val_loss: 0.1016 - val_accuracy: 0.9716\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2888 - accuracy: 0.8975 - val_loss: 0.1037 - val_accuracy: 0.9716\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 134s 4ms/step - loss: 0.2793 - accuracy: 0.9018 - val_loss: 0.1247 - val_accuracy: 0.9615\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2702 - accuracy: 0.9047 - val_loss: 0.1327 - val_accuracy: 0.9588\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2632 - accuracy: 0.9082 - val_loss: 0.1519 - val_accuracy: 0.9532\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2500 - accuracy: 0.9126 - val_loss: 0.1665 - val_accuracy: 0.9485\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 132s 4ms/step - loss: 0.2428 - accuracy: 0.9131 - val_loss: 0.1776 - val_accuracy: 0.9395\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2397 - accuracy: 0.9176 - val_loss: 0.1892 - val_accuracy: 0.9370\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2425 - accuracy: 0.9156 - val_loss: 0.2082 - val_accuracy: 0.9256\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2337 - accuracy: 0.9179 - val_loss: 0.2291 - val_accuracy: 0.9225\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2363 - accuracy: 0.9180 - val_loss: 0.2255 - val_accuracy: 0.9186\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2342 - accuracy: 0.9193 - val_loss: 0.2724 - val_accuracy: 0.9053\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2334 - accuracy: 0.9183 - val_loss: 0.2581 - val_accuracy: 0.9097\n",
            "New Model Saved\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2873 - accuracy: 0.9023 - val_loss: 0.0841 - val_accuracy: 0.9788\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2523 - accuracy: 0.9139 - val_loss: 0.0875 - val_accuracy: 0.9755\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2458 - accuracy: 0.9151 - val_loss: 0.0850 - val_accuracy: 0.9774\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 133s 4ms/step - loss: 0.2279 - accuracy: 0.9204 - val_loss: 0.0968 - val_accuracy: 0.9713\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2349 - accuracy: 0.9191 - val_loss: 0.1141 - val_accuracy: 0.9654\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2224 - accuracy: 0.9219 - val_loss: 0.1224 - val_accuracy: 0.9607\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2207 - accuracy: 0.9240 - val_loss: 0.1270 - val_accuracy: 0.9576\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2196 - accuracy: 0.9237 - val_loss: 0.1489 - val_accuracy: 0.9501\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2116 - accuracy: 0.9262 - val_loss: 0.1522 - val_accuracy: 0.9507\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.2162 - accuracy: 0.9242 - val_loss: 0.1551 - val_accuracy: 0.9482\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.2073 - accuracy: 0.9276 - val_loss: 0.1845 - val_accuracy: 0.9342\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.2053 - accuracy: 0.9294 - val_loss: 0.1873 - val_accuracy: 0.9384\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2079 - accuracy: 0.9297 - val_loss: 0.1963 - val_accuracy: 0.9312\n",
            "New Model Saved\n",
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2449 - accuracy: 0.9171 - val_loss: 0.0612 - val_accuracy: 0.9838\n",
            "Epoch 2/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.2328 - accuracy: 0.9199 - val_loss: 0.0690 - val_accuracy: 0.9819\n",
            "Epoch 3/13\n",
            "32298/32298 [==============================] - 128s 4ms/step - loss: 0.2263 - accuracy: 0.9217 - val_loss: 0.0837 - val_accuracy: 0.9769\n",
            "Epoch 4/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2281 - accuracy: 0.9229 - val_loss: 0.0911 - val_accuracy: 0.9744\n",
            "Epoch 5/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2074 - accuracy: 0.9289 - val_loss: 0.0866 - val_accuracy: 0.9758\n",
            "Epoch 6/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2018 - accuracy: 0.9304 - val_loss: 0.1083 - val_accuracy: 0.9702\n",
            "Epoch 7/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2047 - accuracy: 0.9292 - val_loss: 0.1227 - val_accuracy: 0.9627\n",
            "Epoch 8/13\n",
            "32298/32298 [==============================] - 130s 4ms/step - loss: 0.2043 - accuracy: 0.9290 - val_loss: 0.1214 - val_accuracy: 0.9635\n",
            "Epoch 9/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.1988 - accuracy: 0.9321 - val_loss: 0.1276 - val_accuracy: 0.9588\n",
            "Epoch 10/13\n",
            "32298/32298 [==============================] - 134s 4ms/step - loss: 0.1945 - accuracy: 0.9327 - val_loss: 0.1532 - val_accuracy: 0.9468\n",
            "Epoch 11/13\n",
            "32298/32298 [==============================] - 131s 4ms/step - loss: 0.2002 - accuracy: 0.9318 - val_loss: 0.1679 - val_accuracy: 0.9459\n",
            "Epoch 12/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.2021 - accuracy: 0.9314 - val_loss: 0.1544 - val_accuracy: 0.9504\n",
            "Epoch 13/13\n",
            "32298/32298 [==============================] - 129s 4ms/step - loss: 0.1938 - accuracy: 0.9331 - val_loss: 0.1676 - val_accuracy: 0.9434\n",
            "New Model Saved\n",
            "Train on 32299 samples, validate on 3588 samples\n",
            "Epoch 1/13\n",
            "32299/32299 [==============================] - 132s 4ms/step - loss: 0.2331 - accuracy: 0.9229 - val_loss: 0.0472 - val_accuracy: 0.9852\n",
            "Epoch 2/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.2194 - accuracy: 0.9237 - val_loss: 0.0596 - val_accuracy: 0.9827\n",
            "Epoch 3/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.2112 - accuracy: 0.9269 - val_loss: 0.0645 - val_accuracy: 0.9838\n",
            "Epoch 4/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.2010 - accuracy: 0.9315 - val_loss: 0.0659 - val_accuracy: 0.9841\n",
            "Epoch 5/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1931 - accuracy: 0.9339 - val_loss: 0.0695 - val_accuracy: 0.9785\n",
            "Epoch 6/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.1918 - accuracy: 0.9357 - val_loss: 0.0852 - val_accuracy: 0.9752\n",
            "Epoch 7/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1761 - accuracy: 0.9391 - val_loss: 0.1004 - val_accuracy: 0.9674\n",
            "Epoch 8/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1914 - accuracy: 0.9333 - val_loss: 0.0885 - val_accuracy: 0.9752\n",
            "Epoch 9/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1888 - accuracy: 0.9338 - val_loss: 0.1082 - val_accuracy: 0.9666\n",
            "Epoch 10/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1837 - accuracy: 0.9378 - val_loss: 0.1098 - val_accuracy: 0.9688\n",
            "Epoch 11/13\n",
            "32299/32299 [==============================] - 133s 4ms/step - loss: 0.1799 - accuracy: 0.9387 - val_loss: 0.1314 - val_accuracy: 0.9551\n",
            "Epoch 12/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1748 - accuracy: 0.9412 - val_loss: 0.1313 - val_accuracy: 0.9571\n",
            "Epoch 13/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1794 - accuracy: 0.9391 - val_loss: 0.1379 - val_accuracy: 0.9546\n",
            "New Model Saved\n",
            "Train on 32299 samples, validate on 3588 samples\n",
            "Epoch 1/13\n",
            "32299/32299 [==============================] - 132s 4ms/step - loss: 0.2190 - accuracy: 0.9271 - val_loss: 0.0501 - val_accuracy: 0.9852\n",
            "Epoch 2/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.2090 - accuracy: 0.9286 - val_loss: 0.0549 - val_accuracy: 0.9844\n",
            "Epoch 3/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1876 - accuracy: 0.9376 - val_loss: 0.0620 - val_accuracy: 0.9819\n",
            "Epoch 4/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1921 - accuracy: 0.9354 - val_loss: 0.0633 - val_accuracy: 0.9830\n",
            "Epoch 5/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1760 - accuracy: 0.9427 - val_loss: 0.0701 - val_accuracy: 0.9799\n",
            "Epoch 6/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1741 - accuracy: 0.9401 - val_loss: 0.0822 - val_accuracy: 0.9763\n",
            "Epoch 7/13\n",
            "32299/32299 [==============================] - 132s 4ms/step - loss: 0.1824 - accuracy: 0.9383 - val_loss: 0.0872 - val_accuracy: 0.9724\n",
            "Epoch 8/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1724 - accuracy: 0.9407 - val_loss: 0.0847 - val_accuracy: 0.9766\n",
            "Epoch 9/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1782 - accuracy: 0.9407 - val_loss: 0.0993 - val_accuracy: 0.9679\n",
            "Epoch 10/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1813 - accuracy: 0.9411 - val_loss: 0.1039 - val_accuracy: 0.9685\n",
            "Epoch 11/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1681 - accuracy: 0.9430 - val_loss: 0.1237 - val_accuracy: 0.9624\n",
            "Epoch 12/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.1684 - accuracy: 0.9429 - val_loss: 0.1270 - val_accuracy: 0.9610\n",
            "Epoch 13/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1677 - accuracy: 0.9433 - val_loss: 0.1363 - val_accuracy: 0.9535\n",
            "Train on 32299 samples, validate on 3588 samples\n",
            "Epoch 1/13\n",
            "32299/32299 [==============================] - 133s 4ms/step - loss: 0.1946 - accuracy: 0.9349 - val_loss: 0.0351 - val_accuracy: 0.9900\n",
            "Epoch 2/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1947 - accuracy: 0.9329 - val_loss: 0.0515 - val_accuracy: 0.9866\n",
            "Epoch 3/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.1860 - accuracy: 0.9385 - val_loss: 0.0502 - val_accuracy: 0.9844\n",
            "Epoch 4/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1750 - accuracy: 0.9395 - val_loss: 0.0551 - val_accuracy: 0.9830\n",
            "Epoch 5/13\n",
            "32299/32299 [==============================] - 130s 4ms/step - loss: 0.1664 - accuracy: 0.9431 - val_loss: 0.0551 - val_accuracy: 0.9849\n",
            "Epoch 6/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1689 - accuracy: 0.9420 - val_loss: 0.0595 - val_accuracy: 0.9844\n",
            "Epoch 7/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1620 - accuracy: 0.9445 - val_loss: 0.0651 - val_accuracy: 0.9827\n",
            "Epoch 8/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.1668 - accuracy: 0.9439 - val_loss: 0.0829 - val_accuracy: 0.9730\n",
            "Epoch 9/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1752 - accuracy: 0.9417 - val_loss: 0.0839 - val_accuracy: 0.9741\n",
            "Epoch 10/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1687 - accuracy: 0.9428 - val_loss: 0.0847 - val_accuracy: 0.9738\n",
            "Epoch 11/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.1711 - accuracy: 0.9425 - val_loss: 0.0949 - val_accuracy: 0.9682\n",
            "Epoch 12/13\n",
            "32299/32299 [==============================] - 129s 4ms/step - loss: 0.1619 - accuracy: 0.9454 - val_loss: 0.0860 - val_accuracy: 0.9730\n",
            "Epoch 13/13\n",
            "32299/32299 [==============================] - 131s 4ms/step - loss: 0.1548 - accuracy: 0.9489 - val_loss: 0.1049 - val_accuracy: 0.9682\n",
            "New Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2iiujhLDrsn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
        "def plot_confusion_matrix(\n",
        "        cm,\n",
        "        classes,\n",
        "        normalize=False,\n",
        "        title='Confusion matrix',\n",
        "        cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "    print(cm)\n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, fontsize=12)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45,color = 'black')\n",
        "    plt.yticks(tick_marks, classes,color = 'black')\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label', fontsize=12,color='black')\n",
        "    plt.xlabel('Predicted label', fontsize=12,color='black')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('confusion_matrix.png')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfqfQpaDETPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "79a1d16e-7594-47b7-e90d-7b209cd21c77"
      },
      "source": [
        "print(y_test)\n",
        "ans = np.argmax(model2.predict(X_test),axis=-1)\n",
        "y_test = np.argmax(y_test,axis=1)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 1 0]\n",
            " [0 0 0 ... 0 0 1]\n",
            " [0 0 0 ... 1 0 0]\n",
            " ...\n",
            " [1 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 1 ... 0 0 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMfp_oiqILWh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "outputId": "db69f6e7-e20b-43d8-c770-8aabe00fe8ee"
      },
      "source": [
        "matrix = confusion_matrix(y_test,ans)\n",
        "plot_confusion_matrix(\n",
        "        matrix,\n",
        "        classes=class_names,\n",
        "        normalize=True,\n",
        "        title='Confusion Matrix')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n",
            "[[0.96326531 0.         0.01020408 0.00204082 0.00612245 0.00204082\n",
            "  0.01632653]\n",
            " [0.05454545 0.87272727 0.01818182 0.01818182 0.03636364 0.\n",
            "  0.        ]\n",
            " [0.00757576 0.         0.96022727 0.00757576 0.01893939 0.00189394\n",
            "  0.00378788]\n",
            " [0.00455063 0.         0.00227531 0.97724687 0.00568828 0.00455063\n",
            "  0.00568828]\n",
            " [0.00841751 0.0016835  0.01515152 0.0016835  0.95959596 0.0016835\n",
            "  0.01178451]\n",
            " [0.         0.         0.01923077 0.00961538 0.00240385 0.96634615\n",
            "  0.00240385]\n",
            " [0.00319489 0.         0.00159744 0.00479233 0.00479233 0.00159744\n",
            "  0.98402556]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ku1hTIrwI370",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "db3e19af-afa2-441e-e11c-a03710f899f3"
      },
      "source": [
        "for key in model1.history.history.keys():\n",
        "    print(key)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "val_loss\n",
            "val_accuracy\n",
            "loss\n",
            "accuracy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_ML4FlA4lP2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "9d562869-59bb-4ab6-8de3-1c89264a7157"
      },
      "source": [
        "plt.plot(model2.history.history['accuracy'],'r')\n",
        "plt.plot(model2.history.history['val_accuracy'],'b')\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.savefig('model_accuracy.png')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}