Emotion_detection_using_CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Emotion detection using CNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPSrNtza6q64JkiO8WxIPFq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/woyeso/py/blob/master/Emotion_detection_using_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKsDjAqD68IJ"
      },
      "source": [
        "Importing library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vg1nAzkTjHgw",
        "outputId": "3f83cddb-8bff-4869-d1eb-4642ff656f31"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hdzo0pVKjQXa"
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/gdrive/My Drive/kaggle\""
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrcCp_6UjYu6",
        "outputId": "d1a818a9-ff89-4dea-c277-c808438487f3"
      },
      "source": [
        "#changing the working directory\n",
        "%cd /content/gdrive/My Drive/kaggle"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/My Drive/kaggle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwtIBcZVjboK",
        "outputId": "7eb93330-ad43-4907-81e1-09153636fa89"
      },
      "source": [
        "!kaggle datasets download -d jonathanoheix/face-expression-recognition-dataset"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading face-expression-recognition-dataset.zip to /content/gdrive/My Drive/kaggle\n",
            " 97% 117M/121M [00:01<00:00, 88.5MB/s]\n",
            "100% 121M/121M [00:01<00:00, 86.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oi66o91ujxgE",
        "outputId": "3f1ad282-2a17-4b36-aa8e-b5b6bf102d65"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "face-expression-recognition-dataset.zip  images  kaggle.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbnlAy6vj8AM"
      },
      "source": [
        "**Unzip your data and remove the zip file**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGN8tgIrj7TI",
        "outputId": "c80c9994-17c3-47ea-ddec-e44a43503e76"
      },
      "source": [
        "#unzipping the zip files and deleting the zip files\n",
        "!unzip \\*.zip  && rm *.zip"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0go0uWPKkUA6"
      },
      "source": [
        "# **Importing Library**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NYuRmYKkTZB"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import os\n",
        "\n",
        "# Importing Deep Learning Libraries\n",
        "\n",
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense,Input,Dropout,GlobalAveragePooling2D,Flatten,Conv2D,BatchNormalization,Activation,MaxPooling2D\n",
        "from keras.models import Model,Sequential\n",
        "from tensorflow.keras.optimizers import Adam,SGD,RMSprop"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdly4PQ9kwls"
      },
      "source": [
        "\n",
        "**Displaying Images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjS_TAUvkv4A"
      },
      "source": [
        "picture_size = 48\n",
        "folder_path = \"/content/gdrive/My Drive/kaggle/images/images/\""
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "E5_YkiRclrgM",
        "outputId": "f1bc963b-c22e-4157-bde4-3456688bdc65"
      },
      "source": [
        "expression = 'disgust'\n",
        "\n",
        "plt.figure(figsize= (12,12))\n",
        "for i in range(1, 10, 1):\n",
        "    plt.subplot(3,3,i)\n",
        "    img = load_img(folder_path+\"train/\"+expression+\"/\"+\n",
        "                  os.listdir(folder_path + \"train/\" + expression)[i], target_size=(picture_size, picture_size))\n",
        "    plt.imshow(img)   \n",
        "plt.show()\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nN6ipSJioz0i"
      },
      "source": [
        "**Making Training and Validation Data¶**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5koDY5jopLU",
        "outputId": "782ccbf5-7212-403c-8ebd-959412f72558"
      },
      "source": [
        "batch_size  = 128\n",
        "\n",
        "datagen_train  = ImageDataGenerator()\n",
        "datagen_val = ImageDataGenerator()\n",
        "\n",
        "train_set = datagen_train.flow_from_directory(folder_path+\"train\",\n",
        "                                              target_size = (picture_size,picture_size),\n",
        "                                              color_mode = \"grayscale\",\n",
        "                                              batch_size=batch_size,\n",
        "                                              class_mode='categorical',\n",
        "                                              shuffle=True)\n",
        "\n",
        "\n",
        "test_set = datagen_val.flow_from_directory(folder_path+\"validation\",\n",
        "                                              target_size = (picture_size,picture_size),\n",
        "                                              color_mode = \"grayscale\",\n",
        "                                              batch_size=batch_size,\n",
        "                                              class_mode='categorical',\n",
        "                                              shuffle=False)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 28821 images belonging to 7 classes.\n",
            "Found 7066 images belonging to 7 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jA0EHAIzpGZz"
      },
      "source": [
        "**Model Building**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "er0g4ZNypBuz",
        "outputId": "7518284b-4e7b-4c3b-8266-c198a927b129"
      },
      "source": [
        "from tensorflow.keras.optimizers import Adam,SGD,RMSprop\n",
        "\n",
        "\n",
        "no_of_classes = 7\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "#1st CNN layer\n",
        "model.add(Conv2D(64,(3,3),padding = 'same',input_shape = (48,48,1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "#2nd CNN layer\n",
        "model.add(Conv2D(128,(5,5),padding = 'same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout (0.25))\n",
        "\n",
        "#3rd CNN layer\n",
        "model.add(Conv2D(512,(3,3),padding = 'same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Dropout (0.25))\n",
        "\n",
        "#4th CNN layer\n",
        "model.add(Conv2D(512,(3,3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "#Fully connected 1st layer\n",
        "model.add(Dense(256))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "\n",
        "# Fully connected layer 2nd layer\n",
        "model.add(Dense(512))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Dense(no_of_classes, activation='softmax'))\n",
        "\n",
        "\n",
        "\n",
        "opt = Adam(lr = 0.0001)\n",
        "model.compile(optimizer=opt,loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 48, 48, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 48, 48, 64)       256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 48, 48, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 24, 24, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 24, 24, 128)       204928    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 24, 24, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 24, 24, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 12, 12, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 12, 12, 512)       590336    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 12, 12, 512)      2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 12, 12, 512)       0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 512)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 6, 6, 512)         0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 6, 6, 512)         2359808   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 6, 6, 512)        2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 6, 6, 512)         0         \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 3, 3, 512)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 3, 3, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4608)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               1179904   \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               131584    \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 512)              2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 7)                 3591      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,478,727\n",
            "Trainable params: 4,474,759\n",
            "Non-trainable params: 3,968\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lAp0MKWpZpS"
      },
      "source": [
        "**Fitting the Model with Training and Validation Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7alfRnApVHL",
        "outputId": "f26afc78-25df-44ce-9892-779a1259dc2b"
      },
      "source": [
        "from tensorflow.keras.optimizers import RMSprop,SGD,Adam\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "checkpoint = ModelCheckpoint(\"./model.h5\", monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss',\n",
        "                          min_delta=0,\n",
        "                          patience=3,\n",
        "                          verbose=1,\n",
        "                          restore_best_weights=True\n",
        "                          )\n",
        "\n",
        "reduce_learningrate = ReduceLROnPlateau(monitor='val_loss',\n",
        "                              factor=0.2,\n",
        "                              patience=3,\n",
        "                              verbose=1,\n",
        "                              min_delta=0.0001)\n",
        "\n",
        "callbacks_list = [early_stopping,checkpoint,reduce_learningrate]\n",
        "\n",
        "epochs = 48\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer = Adam(lr=0.001),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpKiy493pplG",
        "outputId": "9165c9f6-a2f6-4e34-b00b-a3850b85246f"
      },
      "source": [
        "history = model.fit_generator(generator=train_set,\n",
        "                                steps_per_epoch=train_set.n//train_set.batch_size,\n",
        "                                epochs=epochs,\n",
        "                                validation_data = test_set,\n",
        "                                validation_steps = test_set.n//test_set.batch_size,\n",
        "                                callbacks=callbacks_list\n",
        "                                )"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.7809 - accuracy: 0.3188WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 104s 322ms/step - loss: 1.7809 - accuracy: 0.3188 - val_loss: 1.6365 - val_accuracy: 0.3783 - lr: 0.0010\n",
            "Epoch 2/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.4376 - accuracy: 0.4511WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 60s 266ms/step - loss: 1.4376 - accuracy: 0.4511 - val_loss: 1.4050 - val_accuracy: 0.4625 - lr: 0.0010\n",
            "Epoch 3/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.2808 - accuracy: 0.5116WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 65s 289ms/step - loss: 1.2808 - accuracy: 0.5116 - val_loss: 1.2261 - val_accuracy: 0.5188 - lr: 0.0010\n",
            "Epoch 4/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.1967 - accuracy: 0.5421WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 62s 274ms/step - loss: 1.1967 - accuracy: 0.5421 - val_loss: 1.1948 - val_accuracy: 0.5430 - lr: 0.0010\n",
            "Epoch 5/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.1262 - accuracy: 0.5733WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 63s 278ms/step - loss: 1.1262 - accuracy: 0.5733 - val_loss: 1.1864 - val_accuracy: 0.5474 - lr: 0.0010\n",
            "Epoch 6/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.0730 - accuracy: 0.5925WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 61s 271ms/step - loss: 1.0730 - accuracy: 0.5925 - val_loss: 1.2891 - val_accuracy: 0.5068 - lr: 0.0010\n",
            "Epoch 7/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 1.0280 - accuracy: 0.6123WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 63s 277ms/step - loss: 1.0280 - accuracy: 0.6123 - val_loss: 1.1610 - val_accuracy: 0.5523 - lr: 0.0010\n",
            "Epoch 8/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.9844 - accuracy: 0.6276WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 61s 270ms/step - loss: 0.9844 - accuracy: 0.6276 - val_loss: 1.1175 - val_accuracy: 0.5736 - lr: 0.0010\n",
            "Epoch 9/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.9513 - accuracy: 0.6418WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 60s 267ms/step - loss: 0.9513 - accuracy: 0.6418 - val_loss: 1.0824 - val_accuracy: 0.5994 - lr: 0.0010\n",
            "Epoch 10/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.9062 - accuracy: 0.6587WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 60s 267ms/step - loss: 0.9062 - accuracy: 0.6587 - val_loss: 1.1861 - val_accuracy: 0.5554 - lr: 0.0010\n",
            "Epoch 11/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.8627 - accuracy: 0.6785WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "225/225 [==============================] - 59s 261ms/step - loss: 0.8627 - accuracy: 0.6785 - val_loss: 1.1327 - val_accuracy: 0.5868 - lr: 0.0010\n",
            "Epoch 12/48\n",
            "225/225 [==============================] - ETA: 0s - loss: 0.8213 - accuracy: 0.6939Restoring model weights from the end of the best epoch: 9.\n",
            "WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "\n",
            "Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
            "225/225 [==============================] - 58s 260ms/step - loss: 0.8213 - accuracy: 0.6939 - val_loss: 1.1422 - val_accuracy: 0.5849 - lr: 0.0010\n",
            "Epoch 00012: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "u_Uhf-qe2TRu",
        "outputId": "f3097297-69a6-43e1-fb47-d6de62532038"
      },
      "source": [
        "plt.style.use('dark_background')\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.suptitle('Optimizer : Adam', fontsize=10)\n",
        "plt.ylabel('Loss', fontsize=16)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.ylabel('Accuracy', fontsize=16)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}