FER2013multiclass_2_heatmaps_v2.ipynb

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      "gpuType": "A100",
      "machine_shape": "hm",
      "name": "FER2013multiclass-2_heatmaps_v2.ipynb",
      "mount_file_id": "1x7pjIe2GvvKf22HIp923om8OBOe9uby2",
      "authorship_tag": "ABX9TyMlIb4ptHz/bQxyTwBiu0ie",
      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/emmyyangqy/ESHNet/blob/main/FER2013multiclass_2_heatmaps_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwfrPJDf3b4b",
        "outputId": "3479cfa4-8c39-4966-f7d4-9098f3a0cb24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/ED\n"
          ]
        }
      ],
      "source": [
        "cd/content/drive/MyDrive/Colab Notebooks/ED"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#### ANKUR FER-Landmarks-CNN-LBP-SVM model\n",
        "\n",
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "import keras.backend as K\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.models import model_from_json\n",
        "from keras.models import load_model, Model\n",
        "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D\n",
        "from keras.layers import Dense, Activation, Dropout, Flatten\n",
        "from keras.layers import BatchNormalization\n",
        "#from keras.layers.normalization import BatchNormalization\n",
        "#from keras.layers.normalization_v2 import BatchNormalization\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.metrics import categorical_accuracy\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import *\n",
        "from keras.optimizers import Adam\n",
        "from keras.utils import to_categorical\n",
        "from keras.utils import plot_model\n",
        "\n",
        "from sklearn.svm import LinearSVC, SVC, NuSVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "# from sklearn.metrics import precision_score, recall_score, f1_score, plot_confusion_matrix\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "\n",
        "#### 1st half with CNN\n",
        "# print(os.listdir(\"data\"))\n",
        "# print(os.listdir(\"fer2013_features/Training\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output.\n",
        "\n",
        "# train_lan=np.load('fer2013_features/Training/landmarks.npy')\n",
        "# print(train_lan.shape)\n",
        "# valid_lan=np.load('fer2013_features/PublicTest/landmarks.npy')\n",
        "\n",
        "emotions = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\n",
        "\n",
        "\n",
        "# get the data, load csv\n",
        "filname = 'data/fer2013_heatmaps_v2_test.csv'\n",
        "label_map = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
        "names=['emotion','pixels','usage']\n",
        "df=pd.read_csv('data/fer2013_heatmaps_v2_test.csv',names=names, na_filter=False)\n",
        "im=df['pixels']\n",
        "df.head(10)\n",
        "\n",
        "#### load images from CSV file\n",
        "def getData(filname):\n",
        "    # images are 100x100\n",
        "    # N = 35887\n",
        "    Y = []\n",
        "    X = []\n",
        "    first = True\n",
        "    for line in open(filname):\n",
        "        if first:\n",
        "            first = False\n",
        "        else:\n",
        "            row = line.split(',')\n",
        "            Y.append(int(row[0]))\n",
        "            X.append([int(p) for p in row[1].split()])\n",
        "\n",
        "    X, Y = np.array(X) / 255.0, np.array(Y)\n",
        "    return X, Y\n",
        "\n",
        "X, Y = getData(filname)\n",
        "num_class = len(set(Y))\n",
        "print(num_class)\n",
        "\n",
        "# keras with tensorflow backend\n",
        "# N, D = X.shape\n",
        "N = len(X)\n",
        "print(N)\n",
        "X = X.reshape(N, 100, 100, 3)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, random_state=0)\n",
        "y_train = (np.arange(num_class) == y_train[:, None]).astype(np.float32)\n",
        "y_test = (np.arange(num_class) == y_test[:, None]).astype(np.float32)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##### load images from png files\n",
        "## load images\n",
        "# base_dir = 'fer2013_heatmaps_v2'\n",
        "\n",
        "# global x_train, y_train, x_test, y_test\n",
        "\n",
        "# x_train = []\n",
        "# y_train = []\n",
        "# x_test = []\n",
        "# y_test = []\n",
        "\n",
        "# labels = os.listdir(os.path.join(base_dir, 'train'))\n",
        "# num_labels = len(labels)\n",
        "\n",
        "# desired_shape = (100, 100)\n",
        "\n",
        "# for phase in ['train', 'test']:\n",
        "#     phase_dir = os.path.join(base_dir, phase)\n",
        "#     for label in os.listdir(phase_dir):\n",
        "#         label_dir = os.path.join(phase_dir, label)\n",
        "#         image_count = 0\n",
        "\n",
        "#         for image in os.listdir(label_dir):\n",
        "# #            if image_count < 10:\n",
        "#                 image_path = os.path.join(label_dir, image)\n",
        "#                 image_data = cv2.imread(image_path, cv2.IMREAD_COLOR)\n",
        "# #                image_data = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
        "#                 image_data = cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB) #Convert BGR to RGB\n",
        "#                 image_data = tf.image.resize(image_data, desired_shape)\n",
        "#                 image_data = image_data / 255.0  # Normalize image data\n",
        "\n",
        "#                 if phase == 'train':\n",
        "#                     x_train.append(image_data)\n",
        "#                     y_train.append(label)\n",
        "#                 else:\n",
        "#                     x_test.append(image_data)\n",
        "#                     y_test.append(label)\n",
        "# #                image_count += 1\n",
        "\n",
        "# x_train = np.array(x_train)\n",
        "# y_train = np.array(y_train)\n",
        "# x_test = np.array(x_test)\n",
        "# y_test = np.array(y_test)\n",
        "\n",
        "# encoder = LabelEncoder()\n",
        "# y_train = encoder.fit_transform(y_train)\n",
        "# y_test = encoder.transform(y_test)\n",
        "\n",
        "# y_train = to_categorical(y_train, num_labels)\n",
        "# y_test = to_categorical(y_test, num_labels)\n",
        "\n",
        "# x_train = tf.convert_to_tensor(x_train, dtype=tf.float32)\n",
        "# y_train = tf.convert_to_tensor(y_train, dtype=tf.float32)\n",
        "\n",
        "# # print(\"x_train shape\", x_train.shape[0])\n",
        "# # print(\"y_train shape\", y_train.shape[0])\n",
        "# # print(\"x_test shape\", x_test.shape[0])\n",
        "# # print(\"y_test shape\", y_test.shape[0])\n",
        "# # print(\"y_train:\", y_train)\n",
        "# # print(\"y_test:\", y_test)\n",
        "\n",
        "\n",
        "##### CNN model architecture\n",
        "# def my_model():\n",
        "#     model = Sequential()\n",
        "#     input_shape = (100,100,3)\n",
        "\n",
        "#     model.add(Conv2D(64, (3,3), padding='same', input_shape=input_shape)) #1 is for grayscale\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 conv\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 conv\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 conv\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",
        "#     model.add(Dense(256))\n",
        "#     model.add(BatchNormalization())\n",
        "#     model.add(Activation('relu'))\n",
        "#     model.add(Dropout(0.25))\n",
        "\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(7, activation='softmax'))\n",
        "\n",
        "#     model.compile(optimizer=keras.optimizers.Adam(learning_rate = 1e-5),\n",
        "#              loss='categorical_crossentropy',\n",
        "#              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "def my_model():\n",
        "    model = Sequential()\n",
        "\n",
        "    input_shape = (100,100,3)\n",
        "    model.add(Conv2D(64, (5, 5), input_shape=input_shape,activation='relu', padding='same'))\n",
        "    model.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(128, (5, 5),activation='relu',padding='same'))\n",
        "    model.add(Conv2D(128, (5, 5),activation='relu',padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(128, name=\"dense_one\"))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    # model.add(Flatten())\n",
        "    # model.add(Dense(256, name=\"dense_two\"))\n",
        "    # model.add(BatchNormalization())\n",
        "    # model.add(Activation('relu'))\n",
        "    # model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Dense(7))\n",
        "    model.add(Activation('softmax'))\n",
        "\n",
        "    # print model plot\n",
        "    plot_model(model, to_file = 'model_plot.png', show_shapes = True, show_layer_names = True)\n",
        "\n",
        "    # model compile\n",
        "    model.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer='adam')\n",
        "\n",
        "#    UNCOMMENT THIS TO VIEW THE ARCHITECTURE\n",
        "    model.summary()\n",
        "    \n",
        "    return model\n",
        "model=my_model()\n",
        "model.summary()\n",
        "\n",
        "\n",
        "###### saving trained model\n",
        "path_model='model_filter_2.h5' # save model at this location after each epoch\n",
        "tf.keras.backend.clear_session() # destroys the current graph and builds a new one\n",
        "model=my_model() # create the model\n",
        "\n",
        "##### set the learning rate\n",
        "# optimizer = Adam(lr=1e-3)\n",
        "# K.set_value(model.optimizer.lr,1e-3)\n",
        "\n",
        "##### fit the model\n",
        "h=model.fit(x=X_train,     \n",
        "            y=y_train, \n",
        "            batch_size=64, \n",
        "            epochs=40, \n",
        "            verbose=1, \n",
        "            validation_data=(X_test,y_test),\n",
        "            shuffle=True,\n",
        "            callbacks=[\n",
        "                ModelCheckpoint(filepath=path_model),\n",
        "            ]\n",
        "            )\n",
        "print(\"model fit is complete.\")\n",
        "print('\\n')\n",
        "\n",
        "\n",
        "##### display model accuracy and loss\n",
        "fig , ax = plt.subplots(1,2)\n",
        "fig.set_size_inches(20, 8)\n",
        "\n",
        "train_acc = h.history['accuracy']\n",
        "train_loss = h.history['loss']\n",
        "val_acc = h.history['val_accuracy']\n",
        "val_loss = h.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(train_acc) + 1)\n",
        "\n",
        "ax[0].plot(epochs , train_acc , 'g-o' , label = 'Training Accuracy')\n",
        "ax[0].plot(epochs , val_acc , 'y-o' , label = 'Validation Accuracy')\n",
        "ax[0].set_title('Model Training & Validation Accuracy')\n",
        "ax[0].legend(loc = 'lower right')\n",
        "ax[0].set_xlabel(\"Epochs\")\n",
        "ax[0].set_ylabel(\"Accuracy\")\n",
        "\n",
        "ax[1].plot(epochs , train_loss , 'g-o' , label = 'Training Loss')\n",
        "ax[1].plot(epochs , val_loss , 'y-o' , label = 'Validation Loss')\n",
        "ax[1].set_title('Model Training & Validation & Loss')\n",
        "ax[1].legend()\n",
        "ax[1].set_xlabel(\"Epochs\")\n",
        "ax[1].set_ylabel(\"Loss\")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#### Print Train and Test accuracy & loss\n",
        "print('Train loss & accuracy:', model.evaluate(X_train, y_train))\n",
        "print('\\n')\n",
        "print('Test loss & accuracy:', model.evaluate(X_test, y_test))\n",
        "print('\\n')\n",
        "\n",
        "\n",
        "#### make prediction\n",
        "y_test = np.argmax(y_test, axis=1)\n",
        "yhat_test_probs = model.predict(X_test)\n",
        "yhat_test = np.argmax(yhat_test_probs, axis=1)\n",
        "\n",
        "top_two_preds = np.argsort(yhat_test_probs, axis=1)[:, -2:]  # Get indices of top two predicted labels\n",
        "correct_preds = np.logical_or(top_two_preds[:, 0] == y_test, top_two_preds[:, 1] == y_test)\n",
        "accuracy = np.mean(correct_preds)\n",
        "\n",
        "print('Modified accuracy:', accuracy)\n",
        "\n",
        "\n",
        "#### get confusion matrix\n",
        "cm = confusion_matrix(y_test, yhat_test)\n",
        "print(\"confusion_matrix:\\n\")\n",
        "print(cm)\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=cm,\n",
        "                                show_normed=True,\n",
        "                                show_absolute=False,\n",
        "                                figsize=(8, 8))\n",
        "plt.show()\n",
        "\n",
        "\n",
        "print(\"part 1 is complete!\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "c3bt7fQ0LiSm",
        "outputId": "5ccca8a0-b7db-479a-d1cb-e519fd8a83ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "6678\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 100, 100, 64)      4864      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 100, 100, 64)      102464    \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 100, 100, 64)     256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 50, 50, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 50, 50, 128)       204928    \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 50, 50, 128)       409728    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 50, 50, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 25, 25, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 25, 25, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 25, 25, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 12, 12, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 36864)             0         \n",
            "                                                                 \n",
            " dense_one (Dense)           (None, 128)               4718720   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 128)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 7)                 903       \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 7)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,329,159\n",
            "Trainable params: 6,328,007\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 100, 100, 64)      4864      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 100, 100, 64)      102464    \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 100, 100, 64)     256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 50, 50, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 50, 50, 128)       204928    \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 50, 50, 128)       409728    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 50, 50, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 25, 25, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 25, 25, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 25, 25, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 12, 12, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 36864)             0         \n",
            "                                                                 \n",
            " dense_one (Dense)           (None, 128)               4718720   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 128)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 7)                 903       \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 7)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,329,159\n",
            "Trainable params: 6,328,007\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 100, 100, 64)      4864      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 100, 100, 64)      102464    \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 100, 100, 64)     256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 50, 50, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 50, 50, 128)       204928    \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 50, 50, 128)       409728    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 50, 50, 128)      512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 25, 25, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 25, 25, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 25, 25, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 12, 12, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 36864)             0         \n",
            "                                                                 \n",
            " dense_one (Dense)           (None, 128)               4718720   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 128)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 7)                 903       \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 7)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,329,159\n",
            "Trainable params: 6,328,007\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n",
            "Epoch 1/40\n",
            "94/94 [==============================] - 21s 59ms/step - loss: 1.5810 - accuracy: 0.4145 - val_loss: 3.6102 - val_accuracy: 0.1138\n",
            "Epoch 2/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.4209 - accuracy: 0.4546 - val_loss: 4.0339 - val_accuracy: 0.2620\n",
            "Epoch 3/40\n",
            "94/94 [==============================] - 3s 32ms/step - loss: 1.3807 - accuracy: 0.4697 - val_loss: 3.8999 - val_accuracy: 0.2620\n",
            "Epoch 4/40\n",
            "94/94 [==============================] - 3s 32ms/step - loss: 1.3310 - accuracy: 0.4950 - val_loss: 4.3315 - val_accuracy: 0.1228\n",
            "Epoch 5/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.2963 - accuracy: 0.5040 - val_loss: 3.1922 - val_accuracy: 0.2440\n",
            "Epoch 6/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.2527 - accuracy: 0.5231 - val_loss: 3.1676 - val_accuracy: 0.2605\n",
            "Epoch 7/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.2096 - accuracy: 0.5386 - val_loss: 1.8222 - val_accuracy: 0.2680\n",
            "Epoch 8/40\n",
            "94/94 [==============================] - 3s 32ms/step - loss: 1.1732 - accuracy: 0.5512 - val_loss: 2.6041 - val_accuracy: 0.2425\n",
            "Epoch 9/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.1403 - accuracy: 0.5676 - val_loss: 1.3498 - val_accuracy: 0.4925\n",
            "Epoch 10/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.0905 - accuracy: 0.5805 - val_loss: 1.4151 - val_accuracy: 0.4431\n",
            "Epoch 11/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.0569 - accuracy: 0.5985 - val_loss: 2.7526 - val_accuracy: 0.2350\n",
            "Epoch 12/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 1.0109 - accuracy: 0.6116 - val_loss: 1.5972 - val_accuracy: 0.4611\n",
            "Epoch 13/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.9660 - accuracy: 0.6376 - val_loss: 9.3563 - val_accuracy: 0.1766\n",
            "Epoch 14/40\n",
            "94/94 [==============================] - 3s 32ms/step - loss: 0.9172 - accuracy: 0.6544 - val_loss: 1.4896 - val_accuracy: 0.4237\n",
            "Epoch 15/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.8374 - accuracy: 0.6920 - val_loss: 2.0336 - val_accuracy: 0.2829\n",
            "Epoch 16/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.7481 - accuracy: 0.7208 - val_loss: 3.0125 - val_accuracy: 0.1811\n",
            "Epoch 17/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.6518 - accuracy: 0.7709 - val_loss: 1.6901 - val_accuracy: 0.4626\n",
            "Epoch 18/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.5793 - accuracy: 0.7938 - val_loss: 2.7606 - val_accuracy: 0.3383\n",
            "Epoch 19/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.4845 - accuracy: 0.8298 - val_loss: 1.6765 - val_accuracy: 0.4746\n",
            "Epoch 20/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.3887 - accuracy: 0.8697 - val_loss: 1.6632 - val_accuracy: 0.4356\n",
            "Epoch 21/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.2998 - accuracy: 0.9035 - val_loss: 1.7844 - val_accuracy: 0.4401\n",
            "Epoch 22/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.2314 - accuracy: 0.9278 - val_loss: 2.2013 - val_accuracy: 0.4416\n",
            "Epoch 23/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.1736 - accuracy: 0.9512 - val_loss: 1.9446 - val_accuracy: 0.4940\n",
            "Epoch 24/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.1416 - accuracy: 0.9627 - val_loss: 2.0868 - val_accuracy: 0.4760\n",
            "Epoch 25/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0952 - accuracy: 0.9762 - val_loss: 2.1608 - val_accuracy: 0.4731\n",
            "Epoch 26/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0734 - accuracy: 0.9852 - val_loss: 2.0119 - val_accuracy: 0.4805\n",
            "Epoch 27/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0466 - accuracy: 0.9925 - val_loss: 2.3079 - val_accuracy: 0.5015\n",
            "Epoch 28/40\n",
            "94/94 [==============================] - 3s 32ms/step - loss: 0.0338 - accuracy: 0.9950 - val_loss: 2.5968 - val_accuracy: 0.4461\n",
            "Epoch 29/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0288 - accuracy: 0.9957 - val_loss: 2.4157 - val_accuracy: 0.5030\n",
            "Epoch 30/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0351 - accuracy: 0.9907 - val_loss: 2.4527 - val_accuracy: 0.4775\n",
            "Epoch 31/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0741 - accuracy: 0.9779 - val_loss: 2.5004 - val_accuracy: 0.4371\n",
            "Epoch 32/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.1752 - accuracy: 0.9351 - val_loss: 3.7250 - val_accuracy: 0.2710\n",
            "Epoch 33/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.1644 - accuracy: 0.9481 - val_loss: 2.6917 - val_accuracy: 0.4566\n",
            "Epoch 34/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.1075 - accuracy: 0.9662 - val_loss: 2.6152 - val_accuracy: 0.4611\n",
            "Epoch 35/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0789 - accuracy: 0.9749 - val_loss: 2.3927 - val_accuracy: 0.4850\n",
            "Epoch 36/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0539 - accuracy: 0.9857 - val_loss: 2.2835 - val_accuracy: 0.4940\n",
            "Epoch 37/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0312 - accuracy: 0.9920 - val_loss: 2.3212 - val_accuracy: 0.4476\n",
            "Epoch 38/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0189 - accuracy: 0.9967 - val_loss: 2.7941 - val_accuracy: 0.4850\n",
            "Epoch 39/40\n",
            "94/94 [==============================] - 3s 32ms/step - loss: 0.0168 - accuracy: 0.9973 - val_loss: 2.8429 - val_accuracy: 0.4835\n",
            "Epoch 40/40\n",
            "94/94 [==============================] - 3s 31ms/step - loss: 0.0164 - accuracy: 0.9973 - val_loss: 2.8941 - val_accuracy: 0.5060\n",
            "model fit is complete.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "188/188 [==============================] - 2s 8ms/step - loss: 0.0092 - accuracy: 0.9980\n",
            "Train loss & accuracy: [0.00921862106770277, 0.9980033040046692]\n",
            "\n",
            "\n",
            "21/21 [==============================] - 0s 7ms/step - loss: 2.8942 - accuracy: 0.5075\n",
            "Test loss & accuracy: [2.8941595554351807, 0.507485032081604]\n",
            "\n",
            "\n",
            "21/21 [==============================] - 0s 6ms/step\n",
            "Modified accuracy: 0.6781437125748503\n",
            "confusion_matrix:\n",
            "\n",
            "[[ 29   1  11   9  11   8  13]\n",
            " [  1   1   0   1   1   0   2]\n",
            " [ 16   2  24   9  22  13  12]\n",
            " [  3   0   6 147   9   2   8]\n",
            " [ 12   0  21  16  44   5  20]\n",
            " [  8   1   5  10   0  46   5]\n",
            " [  7   1  18   7  27   6  48]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "part 1 is complete!\n"
          ]
        }
      ]
    }
  ]
}