src/ML_w14_hw.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW\n",
    "\n",
    "1. Take 10 pictures of objects within the classifications of CIFAR-10 and change the image such that the format is the same (32x32 pixel resolution). Use your trained CNN model to classify the images. \n",
    "2. Train two more modified versions of the CNN model to improve the results of the first model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dachu\\AppData\\Roaming\\Python\\Python310\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device mapping:\n",
      "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: NVIDIA GeForce GTX 1650 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD, Adam\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, Conv3D, MaxPooling2D, MaxPooling3D, BatchNormalization\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.utils import np_utils\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import gridspec as gridspec\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_validate\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import tree, datasets\n",
    "from scipy.io import arff\n",
    "from os.path import join\n",
    "from timeit import default_timer as timer\n",
    "from numba import cuda\n",
    "from GPUtil import showUtilization as gpu_usage\n",
    "import keras.datasets.cifar10 as cifar10\n",
    "import tensorflow as tf\n",
    "import numba\n",
    "import torch\n",
    "import datetime\n",
    "import time\n",
    "import os\n",
    "import math\n",
    "import json\n",
    "import inspect\n",
    "import concurrent.futures as cf  # doesn't work with sklearn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "import statistics as stt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import sys\n",
    "sns.set_theme()\n",
    "%matplotlib inline\n",
    "tf.compat.v1.Session(\n",
    "    config=tf.compat.v1.ConfigProto(log_device_placement=True))\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
    "y_train = np_utils.to_categorical(y_train, 10)\n",
    "y_test = np_utils.to_categorical(y_test, 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# params\n",
    "model_root_path = os.path.join(os.getcwd().rstrip('src'), 'data', 'ml_w14_hw')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN():\n",
    "    def __init__(self, X_train, y_train, X_test, y_test):\n",
    "        # data variables\n",
    "        self.X_train = X_train\n",
    "        self.y_train = y_train\n",
    "        self.X_test = X_test\n",
    "        self.y_test = y_test\n",
    "        # stats variables\n",
    "        self.stats_dict = {\n",
    "            'model_name': None,\n",
    "            'model_file_path': None,\n",
    "            'epochs': None,\n",
    "            'batch_size': None,\n",
    "            'building_time': None,\n",
    "            'training_time': None,\n",
    "            'testing_time': None,\n",
    "            'loss': None,\n",
    "            'accuracy': None,\n",
    "            'evaluating_proba_time': None,\n",
    "            }\n",
    "        # labels\n",
    "        self.label = {0: \"airplane\", 1: \"automobile\", 2: \"bird\", 3: \"cat\",\n",
    "                 4: \"deer\", 5: \"dog\", 6: \"frog\", 7: \"horse\", 8: \"ship\", 9: \"truck\"}\n",
    "        current_time = str(time.strftime(\"%Y/%m/%d %H:%M:%S\", time.localtime()))\n",
    "        self.stats_dict['time'] = current_time\n",
    "        current_time = current_time.replace('/', '').replace(' ', '_').replace(':', '')\n",
    "        self.current_time = current_time\n",
    "\n",
    "    def build_model1(self):\n",
    "        start = timer()\n",
    "        activation1 = 'relu'\n",
    "        channel1 = 100\n",
    "        channel2 = 200\n",
    "        channel3 = 400\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(channel1, (9, 9),\n",
    "                         input_shape=X_train.shape[1:], activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(channel2, (5, 5), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(channel3, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(channel3, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(160, activation=activation1))\n",
    "        model.add(Dense(40, activation=activation1))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer='adam', metrics=['accuracy'])\n",
    "        self.model = model\n",
    "        self.stats_dict['building_time'] = timer() - start\n",
    "\n",
    "    def build_model2(self):\n",
    "        # testing conv layer depth\n",
    "        # https://pyimagesearch.com/2018/12/31/keras-conv2d-and-convolutional-layers/\n",
    "        # it doesn't looks like 4th or more conv layers are possible (error: ValueError: Negative dimension size caused by subtracting 3 from 1 for 'conv2d_4/convolution' (op: 'Conv2D') with input shapes: [?,1,1,256], [3,3,256,256].)\n",
    "        start = timer()\n",
    "        activation1 = 'relu'\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(32, (3, 3),\n",
    "                         input_shape=X_train.shape[1:], activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(64, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(128, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        # model.add(Conv2D(256, (3, 3), activation=activation1))\n",
    "        # model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(128, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(160, activation=activation1))\n",
    "        model.add(Dense(40, activation=activation1))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer='adam', metrics=['accuracy'])\n",
    "        self.model = model\n",
    "        self.stats_dict['building_time'] = timer() - start\n",
    "\n",
    "    def build_model3(self):\n",
    "        # testing dense layer depth\n",
    "        start = timer()\n",
    "        activation1 = 'relu'\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(32, (3, 3),\n",
    "                         input_shape=X_train.shape[1:], activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(64, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(128, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(128, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(256, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(128, activation=activation1))\n",
    "        model.add(Dense(64, activation=activation1))\n",
    "        model.add(Dense(32, activation=activation1))\n",
    "        model.add(Dense(16, activation=activation1))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer='adam', metrics=['accuracy'])\n",
    "        self.model = model\n",
    "        self.stats_dict['building_time'] = timer() - start\n",
    "\n",
    "    def build_model4(self):\n",
    "        # testing even deeper dense layer\n",
    "        start = timer()\n",
    "        activation1 = 'relu'\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(32, (3, 3),\n",
    "                        input_shape=X_train.shape[1:], activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(64, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(128, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(128, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(256, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(512, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(1024, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(512, activation=activation1))\n",
    "        model.add(Dense(256, activation=activation1))\n",
    "        model.add(Dense(128, activation=activation1))\n",
    "        model.add(Dense(64, activation=activation1))\n",
    "        model.add(Dense(32, activation=activation1))\n",
    "        model.add(Dense(16, activation=activation1))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                    optimizer='adam', metrics=['accuracy'])\n",
    "        self.model = model\n",
    "        self.stats_dict['building_time'] = timer() - start\n",
    "\n",
    "    def build_model5(self):\n",
    "        # testing conv layer features\n",
    "        # https://pyimagesearch.com/2018/12/31/keras-conv2d-and-convolutional-layers/\n",
    "        # it doesn't looks like 4th or more conv layers are possible (error: ValueError: Negative dimension size caused by subtracting 3 from 1 for 'conv2d_4/convolution' (op: 'Conv2D') with input shapes: [?,1,1,256], [3,3,256,256].)\n",
    "        start = timer()\n",
    "        activation1 = 'relu'\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(32, (5, 5),\n",
    "                         input_shape=X_train.shape[1:], activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(128, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Conv2D(512, (3, 3), activation=activation1))\n",
    "        model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(512, activation=activation1))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(160, activation=activation1))\n",
    "        model.add(Dense(40, activation=activation1))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer='adam', metrics=['accuracy'])\n",
    "        self.model = model\n",
    "        self.stats_dict['building_time'] = timer() - start\n",
    "\n",
    "    def build_model6(self):\n",
    "        # testing deep conv layer\n",
    "        start = timer()\n",
    "        img_width = X_train.shape[1:][0]\n",
    "        filter_size = 3\n",
    "        layer_depth = img_width / (filter_size - 1)\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(32, (3, 3),\n",
    "                             input_shape=X_train.shape[1:], activation='relu'))\n",
    "        for i in range(int(layer_depth)-2):\n",
    "            model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(512, activation='relu'))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(160, activation='relu'))\n",
    "        model.add(Dense(40, activation='relu'))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer='adam', metrics=['accuracy'])\n",
    "        self.model = model\n",
    "        self.stats_dict['building_time'] = timer() - start\n",
    "\n",
    "    def train_model(self, epochs=5, batch_size=500, verbose=1):\n",
    "        self.stats_dict['epochs'] = epochs\n",
    "        self.stats_dict['batch_size'] = batch_size\n",
    "        start = timer()\n",
    "        self.history = self.model.fit(self.X_train, self.y_train, epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "        self.stats_dict['training_time'] = timer() - start\n",
    "        self.model.summary()\n",
    "\n",
    "    def store_model(self, model_name, path=''):\n",
    "        path = os.path.join(path, f'{model_name}_{self.current_time}.h5')\n",
    "        print(path)\n",
    "        self.model.save(path)\n",
    "        self.stats_dict['model_name'] = model_name\n",
    "        self.stats_dict['model_file_path'] = path\n",
    "\n",
    "    def store_meta_data(self, model_name, path=''):\n",
    "        # check empty values\n",
    "        values = list(self.stats_dict.values())\n",
    "        if None in values:\n",
    "            arr = []\n",
    "            for element in self.stats_dict:\n",
    "                if self.stats_dict[element] == None:\n",
    "                    arr.append(element)\n",
    "            ans = input(\n",
    "                f'Not all stats are recorded...\\nThese stats are not recorded: {arr}\\nDo you still want to export? (y/[n]): ')\n",
    "            if ans != 'y':\n",
    "                return\n",
    "        \n",
    "        # read meta data, append, and write\n",
    "        path = os.path.join(path, 'model_meta_data.csv')\n",
    "        if os.path.exists(path):\n",
    "            meta_data = pd.read_csv(path, encoding='utf-8', index_col=0)\n",
    "            meta_data = pd.concat([meta_data, pd.DataFrame(self.stats_dict, index=[0])])\n",
    "            meta_data.reset_index(drop=True, inplace=True)\n",
    "        else:\n",
    "            meta_data = pd.DataFrame(self.stats_dict, index=[0])\n",
    "        meta_data.to_csv(path, encoding='utf-8')\n",
    "        print('Meta data exported to', path)\n",
    "\n",
    "    def load_model(self, path):\n",
    "        self.model = load_model(path)\n",
    "        self.model_name = os.path.basename(path).split('.')[0]\n",
    "\n",
    "    def evaluate(self):\n",
    "        # record loss and accuracy\n",
    "        start = timer()\n",
    "        result = self.model.evaluate(X_test, y_test)\n",
    "        self.stats_dict['loss'] = result[0]\n",
    "        self.stats_dict['accuracy'] = result[1]\n",
    "        self.stats_dict['testing_time'] = timer() - start\n",
    "\n",
    "    def evaluate_proba(self, img_row_num=2, img_col_num=5, display_bar_num=3, random=False, start_img_num=0, save=False, path='', show=True):\n",
    "        # top 1 accuracy\n",
    "        '''\n",
    "        display images and their predicted probabilities\n",
    "        -----------------------------------------------------------------------\n",
    "        start_img_num: start image number (necessary if random=False)\n",
    "        save_img_path: path to save images\n",
    "        img_row_num: number of rows of images\n",
    "        img_col_num: number of columns of images\n",
    "        display_bar_num: number of bars to display (probability of each class)\n",
    "        random: if True, select images randomly\n",
    "        save: if True, save images\n",
    "        show: if True, show images\n",
    "        -----------------------------------------------------------------------\n",
    "        src: https://stackoverflow.com/questions/34933905/matplotlib-adding-subplots-to-a-subplot\n",
    "        '''\n",
    "        start = timer()\n",
    "        model = self.model\n",
    "        X_test = self.X_test\n",
    "        y_test = self.y_test\n",
    "        label = self.label\n",
    "        # load test data (images)\n",
    "        image_count = img_row_num * img_col_num\n",
    "        test_image_count = X_test.shape[0]\n",
    "        if random:\n",
    "            selected_image_indexes = np.random.choice(a=test_image_count, size=image_count, replace=False)\n",
    "            selected_image = X_test[selected_image_indexes]\n",
    "        else:\n",
    "            selected_image_indexes = np.arange(\n",
    "                start=start_img_num, stop=start_img_num+image_count)\n",
    "            selected_image = X_test[selected_image_indexes]\n",
    "        # predict\n",
    "        probabilities = model.predict(selected_image)\n",
    "        # calculate prediction accuracy\n",
    "        correct_count = 0\n",
    "        for i in range(image_count):\n",
    "            prediction = np.argmax(probabilities[i])\n",
    "            answer = np.argmax(y_test[selected_image_indexes[i]])\n",
    "            if prediction == answer:\n",
    "                correct_count += 1\n",
    "        accuracy = correct_count / image_count * 100\n",
    "        # plot\n",
    "        fig = plt.figure(figsize=(img_col_num*2, img_row_num*2), dpi=300)\n",
    "        plt.title(\n",
    "            f'Top {display_bar_num} Accuracy of {image_count} Images ({img_col_num} x {img_row_num})\\nAccuracy of this batch: {accuracy:.2f}%', pad=30)\n",
    "        plt.axis('off')\n",
    "        outer = gridspec.GridSpec(img_row_num, img_col_num, wspace=0.1, hspace=0.1)\n",
    "        for i in range(image_count):\n",
    "            prediction = label[np.argmax(probabilities[i])]\n",
    "            answer = label[np.argmax(y_test[selected_image_indexes[i]])]\n",
    "            x = i//img_col_num\n",
    "            y = i%img_col_num\n",
    "            inner = gridspec.GridSpecFromSubplotSpec(\n",
    "                2, 1, subplot_spec=outer[i], wspace=0.1, hspace=0.1)\n",
    "            # plot image\n",
    "            ax = plt.Subplot(fig, inner[0])\n",
    "            ax.set_title(f'{prediction} ({answer})', fontsize=8)\n",
    "            ax.imshow(selected_image[i])\n",
    "            ax.axis('off')\n",
    "            fig.add_subplot(ax)\n",
    "            # get top x probabilities\n",
    "            temp_probabilities = (copy.copy(probabilities[i])).tolist()\n",
    "            temp_probabilities.sort(reverse=True)\n",
    "            temp_probabilities = temp_probabilities[:display_bar_num]\n",
    "            temp_label = []\n",
    "            for j in temp_probabilities:\n",
    "                prob_list = probabilities[i].tolist()\n",
    "                index = prob_list.index(j)\n",
    "                temp_label.append(label[index])\n",
    "            # plot bar\n",
    "            ax = plt.Subplot(fig, inner[1])\n",
    "            ax.barh(np.arange(display_bar_num),\n",
    "                    temp_probabilities, align='center', alpha=0.5)\n",
    "            for element in temp_label:\n",
    "                text = f'{element} ({temp_probabilities[temp_label.index(element)]*100:.2f}%)'\n",
    "                ax.text(0.01, temp_label.index(element), text, fontsize=6, va='center')\n",
    "            ax.invert_yaxis()\n",
    "            ax.axis('off')\n",
    "            fig.add_subplot(ax)\n",
    "        if save:\n",
    "            path = os.path.join(\n",
    "                path, '{}_eval_top{}_{}x{}_{}-{}.png'.format(self.model_name, display_bar_num, img_col_num, img_row_num, start_img_num, start_img_num+image_count))\n",
    "            plt.savefig(path)\n",
    "        if show:\n",
    "            plt.show()\n",
    "        plt.close()\n",
    "        print('Image exported to', path)\n",
    "        self.stats_dict['evaluating_proba_time'] = timer() - start\n",
    "\n",
    "    def clear_mem(self):\n",
    "        # https://www.kaggle.com/getting-started/140636\n",
    "        torch.cuda.empty_cache()\n",
    "        cuda.select_device(0)\n",
    "        cuda.close()\n",
    "        cuda.select_device(0)\n",
    "        print('Cleared GPU memory')\n",
    "\n",
    "    def plot_history(self, model_name, path):\n",
    "        history = self.history\n",
    "        plt.figure(figsize=(20, 20), dpi=300)\n",
    "        # plot loss\n",
    "        plt.subplot(211)\n",
    "        plt.title(f'{model_name}_{self.current_time} - Train Loss')\n",
    "        plt.plot(history.history['loss'], label='train_loss')\n",
    "        plt.xlabel('Epoch')\n",
    "        # plt.legend(loc='upper right')\n",
    "        # plot accuracy\n",
    "        plt.subplot(212)\n",
    "        plt.title(f'{model_name}_{self.current_time} - Train Accuracy')\n",
    "        plt.plot(history.history['accuracy'], label='train_accuracy')\n",
    "        plt.xlabel('Epoch')\n",
    "        # plt.legend(loc='lower right')\n",
    "        # save\n",
    "        path = os.path.join(\n",
    "            path, f'{model_name}_{self.current_time}_train_hist.png')\n",
    "        plt.savefig(path)\n",
    "        plt.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 6s 53ms/step - loss: 2.9141 - accuracy: 0.1194\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 5s 52ms/step - loss: 2.1488 - accuracy: 0.1936\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.8653 - accuracy: 0.3103\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.7281 - accuracy: 0.3634\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 1.6394 - accuracy: 0.4044\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.5518 - accuracy: 0.4418\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 6s 56ms/step - loss: 1.4821 - accuracy: 0.4682\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 6s 56ms/step - loss: 1.4300 - accuracy: 0.4906\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 1.3871 - accuracy: 0.5020\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.3240 - accuracy: 0.5282\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.2873 - accuracy: 0.5432\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 1.2526 - accuracy: 0.5527\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.2001 - accuracy: 0.5743\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 1.1513 - accuracy: 0.5901\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.1122 - accuracy: 0.6038\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.0793 - accuracy: 0.6157\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 1.0405 - accuracy: 0.6307\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 1.0055 - accuracy: 0.6425\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.9704 - accuracy: 0.6560\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.9279 - accuracy: 0.6721\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.8969 - accuracy: 0.6795\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.8531 - accuracy: 0.6959\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.8308 - accuracy: 0.7019\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.7951 - accuracy: 0.7135\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.7476 - accuracy: 0.7324\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.7532 - accuracy: 0.7312\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.6891 - accuracy: 0.7523\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.6676 - accuracy: 0.7613\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.6540 - accuracy: 0.7658\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.6237 - accuracy: 0.7760\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.5840 - accuracy: 0.7898\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.5867 - accuracy: 0.7910\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.5515 - accuracy: 0.8034\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.5398 - accuracy: 0.8082\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.4987 - accuracy: 0.8213\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.4786 - accuracy: 0.8279\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.4536 - accuracy: 0.8366\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.4529 - accuracy: 0.8395\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.4417 - accuracy: 0.8428\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.4418 - accuracy: 0.8426\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.4224 - accuracy: 0.8499\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.4034 - accuracy: 0.8560\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.4043 - accuracy: 0.8553\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.3841 - accuracy: 0.8631\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.3418 - accuracy: 0.8762\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.3425 - accuracy: 0.8800\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.3396 - accuracy: 0.8820\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.3370 - accuracy: 0.8799\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.3179 - accuracy: 0.8892\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.2992 - accuracy: 0.8939\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.2818 - accuracy: 0.8994\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 5s 53ms/step - loss: 0.2760 - accuracy: 0.9031\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2771 - accuracy: 0.9028\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2931 - accuracy: 0.8981\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2685 - accuracy: 0.9047\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2671 - accuracy: 0.9085\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2556 - accuracy: 0.9116\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2372 - accuracy: 0.9177\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.2430 - accuracy: 0.9155\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 6s 56ms/step - loss: 0.2540 - accuracy: 0.9120\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.2205 - accuracy: 0.9233\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.2301 - accuracy: 0.9187\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 6s 57ms/step - loss: 0.2022 - accuracy: 0.9301\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.2111 - accuracy: 0.9269\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.2021 - accuracy: 0.9305\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1873 - accuracy: 0.9363\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.1980 - accuracy: 0.9327\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.1959 - accuracy: 0.9338\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.2082 - accuracy: 0.9289\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1713 - accuracy: 0.9414\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 6s 56ms/step - loss: 0.1658 - accuracy: 0.9431\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 6s 58ms/step - loss: 0.1931 - accuracy: 0.9354\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1814 - accuracy: 0.9385\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1696 - accuracy: 0.9413\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.1945 - accuracy: 0.9345\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1652 - accuracy: 0.9443\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1568 - accuracy: 0.9466\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1802 - accuracy: 0.9397\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 6s 58ms/step - loss: 0.1603 - accuracy: 0.9463\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.1507 - accuracy: 0.9492\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 6s 58ms/step - loss: 0.1575 - accuracy: 0.9478\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1471 - accuracy: 0.9517\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1559 - accuracy: 0.9494\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1418 - accuracy: 0.9540\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1323 - accuracy: 0.9575\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1365 - accuracy: 0.9542\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1344 - accuracy: 0.9561\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1501 - accuracy: 0.9513\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1358 - accuracy: 0.9558\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1328 - accuracy: 0.9563\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1244 - accuracy: 0.9595\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1379 - accuracy: 0.9535\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1255 - accuracy: 0.9593\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.1231 - accuracy: 0.9596\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 6s 57ms/step - loss: 0.1243 - accuracy: 0.9602\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1266 - accuracy: 0.9589\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 6s 55ms/step - loss: 0.1161 - accuracy: 0.9612\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 5s 55ms/step - loss: 0.1144 - accuracy: 0.9632\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1149 - accuracy: 0.9628\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 5s 54ms/step - loss: 0.1238 - accuracy: 0.9598\n",
      "Model: \"sequential_30\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_97 (Conv2D)          (None, 24, 24, 100)       24400     \n",
      "                                                                 \n",
      " max_pooling2d_84 (MaxPoolin  (None, 12, 12, 100)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_98 (Conv2D)          (None, 8, 8, 200)         500200    \n",
      "                                                                 \n",
      " max_pooling2d_85 (MaxPoolin  (None, 4, 4, 200)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_99 (Conv2D)          (None, 2, 2, 400)         720400    \n",
      "                                                                 \n",
      " max_pooling2d_86 (MaxPoolin  (None, 1, 1, 400)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_15 (Flatten)        (None, 400)               0         \n",
      "                                                                 \n",
      " dense_70 (Dense)            (None, 400)               160400    \n",
      "                                                                 \n",
      " dropout_19 (Dropout)        (None, 400)               0         \n",
      "                                                                 \n",
      " dense_71 (Dense)            (None, 160)               64160     \n",
      "                                                                 \n",
      " dense_72 (Dense)            (None, 40)                6440      \n",
      "                                                                 \n",
      " dense_73 (Dense)            (None, 10)                410       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,476,410\n",
      "Trainable params: 1,476,410\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model1_20221217_022509.h5\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 3.4917 - accuracy: 0.5500\n",
      "Meta data exported to d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model_meta_data.csv\n"
     ]
    }
   ],
   "source": [
    "# model1\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.build_model1()\n",
    "cnn.train_model(epochs=100, batch_size=500, verbose=1)\n",
    "cnn.plot_history(model_name='model1', path=model_root_path)\n",
    "cnn.store_model(model_name='model1', path=model_root_path)\n",
    "cnn.evaluate()\n",
    "cnn.store_meta_data(model_name='model1', path=model_root_path)\n",
    "# cnn.clear_mem()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 3s 19ms/step - loss: 2.8197 - accuracy: 0.1740\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.8401 - accuracy: 0.3060\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 1.6284 - accuracy: 0.3971\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 1.4881 - accuracy: 0.4595\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.3945 - accuracy: 0.4942\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.3023 - accuracy: 0.5331\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.2480 - accuracy: 0.5576\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 1.1916 - accuracy: 0.5782\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 1.1256 - accuracy: 0.6025\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.0827 - accuracy: 0.6194\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 1.0442 - accuracy: 0.6305\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.9936 - accuracy: 0.6515\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.9552 - accuracy: 0.6671\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.9176 - accuracy: 0.6794\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.8836 - accuracy: 0.6897\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 2s 24ms/step - loss: 0.8439 - accuracy: 0.7027\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.8051 - accuracy: 0.7174\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.7833 - accuracy: 0.7254\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.7597 - accuracy: 0.7319\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7286 - accuracy: 0.7441\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.7007 - accuracy: 0.7515\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.6749 - accuracy: 0.7632\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.6508 - accuracy: 0.7695\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6293 - accuracy: 0.7767\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6160 - accuracy: 0.7835\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.5922 - accuracy: 0.7923\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.5661 - accuracy: 0.8002\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.5581 - accuracy: 0.8030\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.5505 - accuracy: 0.8048\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.5266 - accuracy: 0.8148\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.5015 - accuracy: 0.8228\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.4830 - accuracy: 0.8278\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.4701 - accuracy: 0.8330\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4714 - accuracy: 0.8331\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.4340 - accuracy: 0.8459\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.4280 - accuracy: 0.8479\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.4258 - accuracy: 0.8489\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.4047 - accuracy: 0.8553\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3809 - accuracy: 0.8636\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3834 - accuracy: 0.8633\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3845 - accuracy: 0.8634\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.3597 - accuracy: 0.8723\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.3614 - accuracy: 0.8706\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.3593 - accuracy: 0.8721\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3419 - accuracy: 0.8792\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3320 - accuracy: 0.8825\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3085 - accuracy: 0.8908\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3248 - accuracy: 0.8860\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2973 - accuracy: 0.8964\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2991 - accuracy: 0.8938\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 0.2908 - accuracy: 0.8968\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.2795 - accuracy: 0.9013\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2765 - accuracy: 0.9010\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2612 - accuracy: 0.9076\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2750 - accuracy: 0.9028\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2590 - accuracy: 0.9091\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2590 - accuracy: 0.9082\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.2525 - accuracy: 0.9121\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2359 - accuracy: 0.9166\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.2400 - accuracy: 0.9165\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.2307 - accuracy: 0.9187\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2141 - accuracy: 0.9249\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2206 - accuracy: 0.9229\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2241 - accuracy: 0.9215\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2192 - accuracy: 0.9235\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2190 - accuracy: 0.9224\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.2067 - accuracy: 0.9278\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1924 - accuracy: 0.9321\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2030 - accuracy: 0.9289\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1963 - accuracy: 0.9309\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1985 - accuracy: 0.9301\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1903 - accuracy: 0.9331\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2190 - accuracy: 0.9247\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1805 - accuracy: 0.9371\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.1799 - accuracy: 0.9360\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1708 - accuracy: 0.9407\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.1731 - accuracy: 0.9386\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1666 - accuracy: 0.9426\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1851 - accuracy: 0.9359\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1741 - accuracy: 0.9406\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.1637 - accuracy: 0.9435\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1582 - accuracy: 0.9453\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1645 - accuracy: 0.9440\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1483 - accuracy: 0.9494\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1604 - accuracy: 0.9451\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1604 - accuracy: 0.9457\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1453 - accuracy: 0.9507\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1449 - accuracy: 0.9513\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1408 - accuracy: 0.9515\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1534 - accuracy: 0.9488\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1482 - accuracy: 0.9489\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1402 - accuracy: 0.9523\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1400 - accuracy: 0.9528\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.1496 - accuracy: 0.9494\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1495 - accuracy: 0.9493\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.1207 - accuracy: 0.9577\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1423 - accuracy: 0.9508\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1348 - accuracy: 0.9540\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1260 - accuracy: 0.9567\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1276 - accuracy: 0.9567\n",
      "Model: \"sequential_24\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_78 (Conv2D)          (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_66 (MaxPoolin  (None, 15, 15, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_79 (Conv2D)          (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_67 (MaxPoolin  (None, 6, 6, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_80 (Conv2D)          (None, 4, 4, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_68 (MaxPoolin  (None, 2, 2, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_10 (Flatten)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_40 (Dense)            (None, 128)               65664     \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_41 (Dense)            (None, 160)               20640     \n",
      "                                                                 \n",
      " dense_42 (Dense)            (None, 40)                6440      \n",
      "                                                                 \n",
      " dense_43 (Dense)            (None, 10)                410       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 186,402\n",
      "Trainable params: 186,402\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model2_20221217_014700.h5\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 1.6152 - accuracy: 0.6667\n",
      "Meta data exported to d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model_meta_data.csv\n"
     ]
    }
   ],
   "source": [
    "# model2\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.build_model2()\n",
    "cnn.train_model(epochs=100, batch_size=500, verbose=1)\n",
    "cnn.plot_history(model_name='model2', path=model_root_path)\n",
    "cnn.store_model(model_name='model2', path=model_root_path)\n",
    "cnn.evaluate()\n",
    "cnn.store_meta_data(model_name='model2', path=model_root_path)\n",
    "# cnn.clear_mem()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 3s 21ms/step - loss: 2.5136 - accuracy: 0.1065\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 2.1587 - accuracy: 0.1809\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.9291 - accuracy: 0.2534\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 1.7573 - accuracy: 0.3211\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 1.6501 - accuracy: 0.3738\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 1.5417 - accuracy: 0.4200\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 1.4476 - accuracy: 0.4555\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.3568 - accuracy: 0.4971\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 1.2911 - accuracy: 0.5256\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.2092 - accuracy: 0.5635\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 1.1614 - accuracy: 0.5846\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 1.1200 - accuracy: 0.6024\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 1.0607 - accuracy: 0.6258\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.0168 - accuracy: 0.6430\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.9677 - accuracy: 0.6620\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 0.9365 - accuracy: 0.6738\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.8789 - accuracy: 0.6955\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.8490 - accuracy: 0.7075\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.8299 - accuracy: 0.7136\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.7868 - accuracy: 0.7253\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.7604 - accuracy: 0.7376\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.7335 - accuracy: 0.7459\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6998 - accuracy: 0.7577\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6832 - accuracy: 0.7640\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6583 - accuracy: 0.7720\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6427 - accuracy: 0.7796\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6163 - accuracy: 0.7867\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.6075 - accuracy: 0.7925\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.5870 - accuracy: 0.7972\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.5727 - accuracy: 0.8020\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.5427 - accuracy: 0.8124\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.5390 - accuracy: 0.8138\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.5159 - accuracy: 0.8236\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4941 - accuracy: 0.8309\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4869 - accuracy: 0.8324\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4774 - accuracy: 0.8353\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4672 - accuracy: 0.8406\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.4493 - accuracy: 0.8446\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4393 - accuracy: 0.8479\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.4134 - accuracy: 0.8570\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4219 - accuracy: 0.8536\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.4009 - accuracy: 0.8621\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3957 - accuracy: 0.8636\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3818 - accuracy: 0.8682\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3681 - accuracy: 0.8734\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3672 - accuracy: 0.8760\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3464 - accuracy: 0.8812\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3327 - accuracy: 0.8855\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3370 - accuracy: 0.8854\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3215 - accuracy: 0.8909\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3452 - accuracy: 0.8841\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3198 - accuracy: 0.8927\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3045 - accuracy: 0.8966\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.3137 - accuracy: 0.8937\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2905 - accuracy: 0.9020\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2875 - accuracy: 0.9023\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2885 - accuracy: 0.9043\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2854 - accuracy: 0.9055\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2779 - accuracy: 0.9054\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2781 - accuracy: 0.9065\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2682 - accuracy: 0.9108\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2650 - accuracy: 0.9120\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2579 - accuracy: 0.9151\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2663 - accuracy: 0.9116\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2467 - accuracy: 0.9179\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2675 - accuracy: 0.9129\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2429 - accuracy: 0.9196\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2424 - accuracy: 0.9223\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2333 - accuracy: 0.9234\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2294 - accuracy: 0.9244\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2337 - accuracy: 0.9236\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2172 - accuracy: 0.9300\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2314 - accuracy: 0.9249\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2196 - accuracy: 0.9284\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2328 - accuracy: 0.9252\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1977 - accuracy: 0.9361\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2080 - accuracy: 0.9319\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.2054 - accuracy: 0.9339\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1992 - accuracy: 0.9358\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1896 - accuracy: 0.9393\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1950 - accuracy: 0.9381\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1932 - accuracy: 0.9384\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.2136 - accuracy: 0.9307\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1870 - accuracy: 0.9399\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1718 - accuracy: 0.9456\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1855 - accuracy: 0.9411\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1845 - accuracy: 0.9399\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1711 - accuracy: 0.9450\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1779 - accuracy: 0.9433\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1793 - accuracy: 0.9437\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1808 - accuracy: 0.9428\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1800 - accuracy: 0.9414\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1763 - accuracy: 0.9439\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1686 - accuracy: 0.9469\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1733 - accuracy: 0.9450\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1616 - accuracy: 0.9479\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1595 - accuracy: 0.9501\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1563 - accuracy: 0.9507\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 2s 19ms/step - loss: 0.1632 - accuracy: 0.9483\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1480 - accuracy: 0.9537\n",
      "Model: \"sequential_25\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_81 (Conv2D)          (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_69 (MaxPoolin  (None, 15, 15, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_82 (Conv2D)          (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_70 (MaxPoolin  (None, 6, 6, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_83 (Conv2D)          (None, 4, 4, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_71 (MaxPoolin  (None, 2, 2, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_11 (Flatten)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_44 (Dense)            (None, 128)               65664     \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_45 (Dense)            (None, 256)               33024     \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_46 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_47 (Dense)            (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_48 (Dense)            (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_49 (Dense)            (None, 16)                528       \n",
      "                                                                 \n",
      " dense_50 (Dense)            (None, 10)                170       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 235,866\n",
      "Trainable params: 235,866\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model3_20221217_015132.h5\n",
      "313/313 [==============================] - 2s 4ms/step - loss: 1.5691 - accuracy: 0.6873\n",
      "Meta data exported to d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model_meta_data.csv\n"
     ]
    }
   ],
   "source": [
    "# model3\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.build_model3()\n",
    "cnn.train_model(epochs=100, batch_size=500, verbose=1)\n",
    "cnn.plot_history(model_name='model3', path=model_root_path)\n",
    "cnn.store_model(model_name='model3', path=model_root_path)\n",
    "cnn.evaluate()\n",
    "cnn.store_meta_data(model_name='model3', path=model_root_path)\n",
    "# cnn.clear_mem()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 4s 22ms/step - loss: 2.3510 - accuracy: 0.1238\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 2.1008 - accuracy: 0.1953\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.9142 - accuracy: 0.2382\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.7801 - accuracy: 0.2768\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.6730 - accuracy: 0.3394\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.5944 - accuracy: 0.3773\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 1.5264 - accuracy: 0.4056\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 1.4520 - accuracy: 0.4349\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 1.4054 - accuracy: 0.4605\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.3762 - accuracy: 0.4795\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.3112 - accuracy: 0.5083\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.2682 - accuracy: 0.5357\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 1.2214 - accuracy: 0.5615\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 1.1974 - accuracy: 0.5709\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.1693 - accuracy: 0.5874\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 1.1094 - accuracy: 0.6100\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 3s 26ms/step - loss: 1.0827 - accuracy: 0.6212\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 2s 25ms/step - loss: 1.0545 - accuracy: 0.6338\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 2s 24ms/step - loss: 1.0243 - accuracy: 0.6432\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.9881 - accuracy: 0.6563\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.9616 - accuracy: 0.6662\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.9542 - accuracy: 0.6700\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.9486 - accuracy: 0.6723\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 3s 25ms/step - loss: 0.9116 - accuracy: 0.6844\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 3s 33ms/step - loss: 0.8854 - accuracy: 0.6937\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 0.8835 - accuracy: 0.6945\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.8570 - accuracy: 0.7054\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.8539 - accuracy: 0.7074\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.8584 - accuracy: 0.7058\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.8302 - accuracy: 0.7145\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.8092 - accuracy: 0.7256\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7819 - accuracy: 0.7303\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7636 - accuracy: 0.7375\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7472 - accuracy: 0.7469\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7461 - accuracy: 0.7456\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7514 - accuracy: 0.7439\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7070 - accuracy: 0.7606\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6997 - accuracy: 0.7626\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7090 - accuracy: 0.7609\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 2s 24ms/step - loss: 0.6787 - accuracy: 0.7701\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.7024 - accuracy: 0.7625\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 0.7445 - accuracy: 0.7465\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6669 - accuracy: 0.7722\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6701 - accuracy: 0.7734\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6674 - accuracy: 0.7747\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6698 - accuracy: 0.7730\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6326 - accuracy: 0.7870\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6355 - accuracy: 0.7852\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6404 - accuracy: 0.7831\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 0.7473 - accuracy: 0.7485\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6432 - accuracy: 0.7829\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6687 - accuracy: 0.7745\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6231 - accuracy: 0.7907\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5951 - accuracy: 0.7995\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6321 - accuracy: 0.7880\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6034 - accuracy: 0.7979\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5768 - accuracy: 0.8058\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5285 - accuracy: 0.8221\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6367 - accuracy: 0.7866\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6660 - accuracy: 0.7811\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5670 - accuracy: 0.8105\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5406 - accuracy: 0.8191\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5941 - accuracy: 0.8034\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5189 - accuracy: 0.8283\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5092 - accuracy: 0.8310\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4901 - accuracy: 0.8389\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5855 - accuracy: 0.8095\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5080 - accuracy: 0.8335\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5879 - accuracy: 0.8094\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5694 - accuracy: 0.8132\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5520 - accuracy: 0.8198\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.5410 - accuracy: 0.8238\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 2s 24ms/step - loss: 0.5305 - accuracy: 0.8268\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 2s 23ms/step - loss: 0.5482 - accuracy: 0.8231\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 0.5098 - accuracy: 0.8348\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.4966 - accuracy: 0.8390\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.5442 - accuracy: 0.8227\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5733 - accuracy: 0.8113\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6090 - accuracy: 0.8011\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5635 - accuracy: 0.8191\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5199 - accuracy: 0.8311\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.4575 - accuracy: 0.8515\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6081 - accuracy: 0.8054\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 2s 22ms/step - loss: 0.6215 - accuracy: 0.7980\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.5707 - accuracy: 0.8134\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6313 - accuracy: 0.7946\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4764 - accuracy: 0.8453\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4973 - accuracy: 0.8387\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4783 - accuracy: 0.8452\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4769 - accuracy: 0.8469\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4670 - accuracy: 0.8494\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.4202 - accuracy: 0.8658\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 2s 21ms/step - loss: 0.6452 - accuracy: 0.7961\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4868 - accuracy: 0.8422\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6802 - accuracy: 0.7821\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.7993 - accuracy: 0.7460\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.8152 - accuracy: 0.7336\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.5873 - accuracy: 0.8108\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.4920 - accuracy: 0.8414\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 2s 20ms/step - loss: 0.6223 - accuracy: 0.8045\n",
      "Model: \"sequential_26\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_84 (Conv2D)          (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_72 (MaxPoolin  (None, 15, 15, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_85 (Conv2D)          (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_73 (MaxPoolin  (None, 6, 6, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_86 (Conv2D)          (None, 4, 4, 128)         73856     \n",
      "                                                                 \n",
      " max_pooling2d_74 (MaxPoolin  (None, 2, 2, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_12 (Flatten)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_51 (Dense)            (None, 128)               65664     \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_52 (Dense)            (None, 256)               33024     \n",
      "                                                                 \n",
      " dropout_14 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_53 (Dense)            (None, 512)               131584    \n",
      "                                                                 \n",
      " dropout_15 (Dropout)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_54 (Dense)            (None, 1024)              525312    \n",
      "                                                                 \n",
      " dropout_16 (Dropout)        (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_55 (Dense)            (None, 512)               524800    \n",
      "                                                                 \n",
      " dense_56 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " dense_57 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_58 (Dense)            (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_59 (Dense)            (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_60 (Dense)            (None, 16)                528       \n",
      "                                                                 \n",
      " dense_61 (Dense)            (None, 10)                170       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,548,890\n",
      "Trainable params: 1,548,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model4_20221217_015801.h5\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 1.5442 - accuracy: 0.4971\n",
      "Meta data exported to d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model_meta_data.csv\n"
     ]
    }
   ],
   "source": [
    "# model4\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.build_model4()\n",
    "cnn.train_model(epochs=100, batch_size=500, verbose=1)\n",
    "cnn.plot_history(model_name='model4', path=model_root_path)\n",
    "cnn.store_model(model_name='model4', path=model_root_path)\n",
    "cnn.evaluate()\n",
    "cnn.store_meta_data(model_name='model4', path=model_root_path)\n",
    "# cnn.clear_mem()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "50/50 [==============================] - 6s 65ms/step - loss: 4.0249 - accuracy: 0.1685\n",
      "Epoch 2/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 1.7981 - accuracy: 0.3380\n",
      "Epoch 3/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 1.5483 - accuracy: 0.4413\n",
      "Epoch 4/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 1.4116 - accuracy: 0.4970\n",
      "Epoch 5/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 1.3053 - accuracy: 0.5382\n",
      "Epoch 6/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 1.2180 - accuracy: 0.5698\n",
      "Epoch 7/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 1.1161 - accuracy: 0.6090\n",
      "Epoch 8/100\n",
      "50/50 [==============================] - 3s 65ms/step - loss: 1.0536 - accuracy: 0.6263\n",
      "Epoch 9/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.9910 - accuracy: 0.6502\n",
      "Epoch 10/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.9146 - accuracy: 0.6771\n",
      "Epoch 11/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.8511 - accuracy: 0.6996\n",
      "Epoch 12/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.8145 - accuracy: 0.7138\n",
      "Epoch 13/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.7518 - accuracy: 0.7373\n",
      "Epoch 14/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.6991 - accuracy: 0.7570\n",
      "Epoch 15/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.6522 - accuracy: 0.7721\n",
      "Epoch 16/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.6022 - accuracy: 0.7870\n",
      "Epoch 17/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.5522 - accuracy: 0.8054\n",
      "Epoch 18/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.5353 - accuracy: 0.8108\n",
      "Epoch 19/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.4758 - accuracy: 0.8320\n",
      "Epoch 20/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.4537 - accuracy: 0.8383\n",
      "Epoch 21/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.4318 - accuracy: 0.8471\n",
      "Epoch 22/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.3892 - accuracy: 0.8620\n",
      "Epoch 23/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.3521 - accuracy: 0.8738\n",
      "Epoch 24/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.3336 - accuracy: 0.8816\n",
      "Epoch 25/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.3140 - accuracy: 0.8885\n",
      "Epoch 26/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.2963 - accuracy: 0.8957\n",
      "Epoch 27/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.2631 - accuracy: 0.9066\n",
      "Epoch 28/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.2778 - accuracy: 0.9018\n",
      "Epoch 29/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.2502 - accuracy: 0.9123\n",
      "Epoch 30/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.2246 - accuracy: 0.9202\n",
      "Epoch 31/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.2168 - accuracy: 0.9240\n",
      "Epoch 32/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1918 - accuracy: 0.9319\n",
      "Epoch 33/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.2011 - accuracy: 0.9297\n",
      "Epoch 34/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1868 - accuracy: 0.9341\n",
      "Epoch 35/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.1866 - accuracy: 0.9350\n",
      "Epoch 36/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.1725 - accuracy: 0.9401\n",
      "Epoch 37/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.1707 - accuracy: 0.9406\n",
      "Epoch 38/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.1505 - accuracy: 0.9477\n",
      "Epoch 39/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.1472 - accuracy: 0.9485\n",
      "Epoch 40/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.1268 - accuracy: 0.9564\n",
      "Epoch 41/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1233 - accuracy: 0.9578\n",
      "Epoch 42/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.1266 - accuracy: 0.9569\n",
      "Epoch 43/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.1415 - accuracy: 0.9513\n",
      "Epoch 44/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.1100 - accuracy: 0.9625\n",
      "Epoch 45/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1124 - accuracy: 0.9625\n",
      "Epoch 46/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0942 - accuracy: 0.9679\n",
      "Epoch 47/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1289 - accuracy: 0.9568\n",
      "Epoch 48/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1177 - accuracy: 0.9596\n",
      "Epoch 49/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0955 - accuracy: 0.9680\n",
      "Epoch 50/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1066 - accuracy: 0.9638\n",
      "Epoch 51/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.0965 - accuracy: 0.9676\n",
      "Epoch 52/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0956 - accuracy: 0.9676\n",
      "Epoch 53/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.1078 - accuracy: 0.9636\n",
      "Epoch 54/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0881 - accuracy: 0.9714\n",
      "Epoch 55/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0875 - accuracy: 0.9695\n",
      "Epoch 56/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.0906 - accuracy: 0.9698\n",
      "Epoch 57/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.0839 - accuracy: 0.9724\n",
      "Epoch 58/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0903 - accuracy: 0.9702\n",
      "Epoch 59/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0859 - accuracy: 0.9713\n",
      "Epoch 60/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.1076 - accuracy: 0.9640\n",
      "Epoch 61/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0818 - accuracy: 0.9730\n",
      "Epoch 62/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0832 - accuracy: 0.9723\n",
      "Epoch 63/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0833 - accuracy: 0.9721\n",
      "Epoch 64/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.0734 - accuracy: 0.9757\n",
      "Epoch 65/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0692 - accuracy: 0.9769\n",
      "Epoch 66/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0918 - accuracy: 0.9699\n",
      "Epoch 67/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0737 - accuracy: 0.9766\n",
      "Epoch 68/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.0821 - accuracy: 0.9730\n",
      "Epoch 69/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.0926 - accuracy: 0.9688\n",
      "Epoch 70/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0809 - accuracy: 0.9735\n",
      "Epoch 71/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0759 - accuracy: 0.9748\n",
      "Epoch 72/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0654 - accuracy: 0.9788\n",
      "Epoch 73/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.0883 - accuracy: 0.9708\n",
      "Epoch 74/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0571 - accuracy: 0.9802\n",
      "Epoch 75/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0594 - accuracy: 0.9805\n",
      "Epoch 76/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0651 - accuracy: 0.9782\n",
      "Epoch 77/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0629 - accuracy: 0.9797\n",
      "Epoch 78/100\n",
      "50/50 [==============================] - 3s 66ms/step - loss: 0.0805 - accuracy: 0.9735\n",
      "Epoch 79/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.0748 - accuracy: 0.9746\n",
      "Epoch 80/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0714 - accuracy: 0.9768\n",
      "Epoch 81/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0990 - accuracy: 0.9679\n",
      "Epoch 82/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.0744 - accuracy: 0.9759\n",
      "Epoch 83/100\n",
      "50/50 [==============================] - 3s 68ms/step - loss: 0.0737 - accuracy: 0.9753\n",
      "Epoch 84/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0620 - accuracy: 0.9792\n",
      "Epoch 85/100\n",
      "50/50 [==============================] - 4s 71ms/step - loss: 0.0614 - accuracy: 0.9803\n",
      "Epoch 86/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0537 - accuracy: 0.9825\n",
      "Epoch 87/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0550 - accuracy: 0.9824\n",
      "Epoch 88/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0672 - accuracy: 0.9777\n",
      "Epoch 89/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0581 - accuracy: 0.9806\n",
      "Epoch 90/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0668 - accuracy: 0.9784\n",
      "Epoch 91/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0720 - accuracy: 0.9770\n",
      "Epoch 92/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0763 - accuracy: 0.9761\n",
      "Epoch 93/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0617 - accuracy: 0.9798\n",
      "Epoch 94/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0655 - accuracy: 0.9795\n",
      "Epoch 95/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0545 - accuracy: 0.9825\n",
      "Epoch 96/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0518 - accuracy: 0.9826\n",
      "Epoch 97/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0583 - accuracy: 0.9802\n",
      "Epoch 98/100\n",
      "50/50 [==============================] - 3s 70ms/step - loss: 0.0579 - accuracy: 0.9813\n",
      "Epoch 99/100\n",
      "50/50 [==============================] - 3s 69ms/step - loss: 0.0548 - accuracy: 0.9821\n",
      "Epoch 100/100\n",
      "50/50 [==============================] - 3s 67ms/step - loss: 0.0771 - accuracy: 0.9759\n",
      "Model: \"sequential_29\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_94 (Conv2D)          (None, 28, 28, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d_81 (MaxPoolin  (None, 14, 14, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_95 (Conv2D)          (None, 12, 12, 128)       36992     \n",
      "                                                                 \n",
      " max_pooling2d_82 (MaxPoolin  (None, 6, 6, 128)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_96 (Conv2D)          (None, 4, 4, 512)         590336    \n",
      "                                                                 \n",
      " max_pooling2d_83 (MaxPoolin  (None, 2, 2, 512)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_14 (Flatten)        (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_66 (Dense)            (None, 512)               1049088   \n",
      "                                                                 \n",
      " dropout_18 (Dropout)        (None, 512)               0         \n",
      "                                                                 \n",
      " dense_67 (Dense)            (None, 160)               82080     \n",
      "                                                                 \n",
      " dense_68 (Dense)            (None, 40)                6440      \n",
      "                                                                 \n",
      " dense_69 (Dense)            (None, 10)                410       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,767,778\n",
      "Trainable params: 1,767,778\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model5_20221217_021525.h5\n",
      "313/313 [==============================] - 2s 5ms/step - loss: 2.7050 - accuracy: 0.6637\n",
      "Meta data exported to d:\\Note_Database\\Subject\\BD_ML Big Data and Machine Learning\\BD_ML_Code\\data\\ml_w14_hw\\model_meta_data.csv\n"
     ]
    }
   ],
   "source": [
    "# model5\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.build_model5()\n",
    "cnn.train_model(epochs=100, batch_size=1000, verbose=1)\n",
    "cnn.plot_history(model_name='model5', path=model_root_path)\n",
    "cnn.store_model(model_name='model5', path=model_root_path)\n",
    "cnn.evaluate()\n",
    "cnn.store_meta_data(model_name='model5', path=model_root_path)\n",
    "# cnn.clear_mem()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "50/50 [==============================] - 16s 152ms/step - loss: 2.3019 - accuracy: 0.1041\n",
      "Epoch 2/100\n",
      "50/50 [==============================] - 8s 152ms/step - loss: 2.3054 - accuracy: 0.1055\n",
      "Epoch 3/100\n",
      "50/50 [==============================] - 8s 153ms/step - loss: 2.1334 - accuracy: 0.1613\n",
      "Epoch 4/100\n",
      "50/50 [==============================] - 8s 153ms/step - loss: 1.9549 - accuracy: 0.2290\n",
      "Epoch 5/100\n",
      "50/50 [==============================] - 8s 154ms/step - loss: 1.8254 - accuracy: 0.2965\n",
      "Epoch 6/100\n",
      "50/50 [==============================] - 8s 154ms/step - loss: 1.7374 - accuracy: 0.3387\n",
      "Epoch 7/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.6678 - accuracy: 0.3742\n",
      "Epoch 8/100\n",
      "50/50 [==============================] - 8s 154ms/step - loss: 1.5800 - accuracy: 0.4097\n",
      "Epoch 9/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.5045 - accuracy: 0.4397\n",
      "Epoch 10/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.4257 - accuracy: 0.4734\n",
      "Epoch 11/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.3794 - accuracy: 0.4935\n",
      "Epoch 12/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.3093 - accuracy: 0.5217\n",
      "Epoch 13/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.2780 - accuracy: 0.5337\n",
      "Epoch 14/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.2393 - accuracy: 0.5462\n",
      "Epoch 15/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.1929 - accuracy: 0.5659\n",
      "Epoch 16/100\n",
      "50/50 [==============================] - 8s 155ms/step - loss: 1.1797 - accuracy: 0.5720\n",
      "Epoch 17/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.1353 - accuracy: 0.5886\n",
      "Epoch 18/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.1168 - accuracy: 0.5970\n",
      "Epoch 19/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.0913 - accuracy: 0.6058\n",
      "Epoch 20/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.0634 - accuracy: 0.6174\n",
      "Epoch 21/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.0633 - accuracy: 0.6168\n",
      "Epoch 22/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.0416 - accuracy: 0.6257\n",
      "Epoch 23/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 1.0160 - accuracy: 0.6355\n",
      "Epoch 24/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 1.0072 - accuracy: 0.6371\n",
      "Epoch 25/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.9671 - accuracy: 0.6519\n",
      "Epoch 26/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.9598 - accuracy: 0.6579\n",
      "Epoch 27/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.9246 - accuracy: 0.6675\n",
      "Epoch 28/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.9067 - accuracy: 0.6730\n",
      "Epoch 29/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.9142 - accuracy: 0.6728\n",
      "Epoch 30/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.9036 - accuracy: 0.6739\n",
      "Epoch 31/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.8532 - accuracy: 0.6929\n",
      "Epoch 32/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.8429 - accuracy: 0.6973\n",
      "Epoch 33/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.8383 - accuracy: 0.6995\n",
      "Epoch 34/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.8453 - accuracy: 0.6970\n",
      "Epoch 35/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.8321 - accuracy: 0.7001\n",
      "Epoch 36/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.8204 - accuracy: 0.7036\n",
      "Epoch 37/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.8142 - accuracy: 0.7088\n",
      "Epoch 38/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7626 - accuracy: 0.7273\n",
      "Epoch 39/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7879 - accuracy: 0.7151\n",
      "Epoch 40/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7447 - accuracy: 0.7308\n",
      "Epoch 41/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7242 - accuracy: 0.7404\n",
      "Epoch 42/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7354 - accuracy: 0.7354\n",
      "Epoch 43/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7219 - accuracy: 0.7394\n",
      "Epoch 44/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6876 - accuracy: 0.7521\n",
      "Epoch 45/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.7008 - accuracy: 0.7490\n",
      "Epoch 46/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.6666 - accuracy: 0.7605\n",
      "Epoch 47/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6938 - accuracy: 0.7508\n",
      "Epoch 48/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6735 - accuracy: 0.7582\n",
      "Epoch 49/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6497 - accuracy: 0.7661\n",
      "Epoch 50/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6338 - accuracy: 0.7737\n",
      "Epoch 51/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6355 - accuracy: 0.7716\n",
      "Epoch 52/100\n",
      "50/50 [==============================] - 8s 156ms/step - loss: 0.6579 - accuracy: 0.7639\n",
      "Epoch 53/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6377 - accuracy: 0.7694\n",
      "Epoch 54/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6378 - accuracy: 0.7716\n",
      "Epoch 55/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5966 - accuracy: 0.7843\n",
      "Epoch 56/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5992 - accuracy: 0.7839\n",
      "Epoch 57/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6171 - accuracy: 0.7773\n",
      "Epoch 58/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.6038 - accuracy: 0.7825\n",
      "Epoch 59/100\n",
      "50/50 [==============================] - 8s 158ms/step - loss: 0.6008 - accuracy: 0.7821\n",
      "Epoch 60/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5666 - accuracy: 0.7971\n",
      "Epoch 61/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5835 - accuracy: 0.7888\n",
      "Epoch 62/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5528 - accuracy: 0.8015\n",
      "Epoch 63/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5543 - accuracy: 0.8014\n",
      "Epoch 64/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5758 - accuracy: 0.7927\n",
      "Epoch 65/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5291 - accuracy: 0.8092\n",
      "Epoch 66/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5121 - accuracy: 0.8163\n",
      "Epoch 67/100\n",
      "50/50 [==============================] - 8s 158ms/step - loss: 0.5137 - accuracy: 0.8137\n",
      "Epoch 68/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5424 - accuracy: 0.8034\n",
      "Epoch 69/100\n",
      "50/50 [==============================] - 8s 158ms/step - loss: 0.5413 - accuracy: 0.8069\n",
      "Epoch 70/100\n",
      "50/50 [==============================] - 8s 158ms/step - loss: 0.4879 - accuracy: 0.8240\n",
      "Epoch 71/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5001 - accuracy: 0.8175\n",
      "Epoch 72/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4838 - accuracy: 0.8235\n",
      "Epoch 73/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4776 - accuracy: 0.8267\n",
      "Epoch 74/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.5078 - accuracy: 0.8158\n",
      "Epoch 75/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4622 - accuracy: 0.8324\n",
      "Epoch 76/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4481 - accuracy: 0.8395\n",
      "Epoch 77/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4703 - accuracy: 0.8277\n",
      "Epoch 78/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4449 - accuracy: 0.8406\n",
      "Epoch 79/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4588 - accuracy: 0.8349\n",
      "Epoch 80/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4551 - accuracy: 0.8356\n",
      "Epoch 81/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4857 - accuracy: 0.8246\n",
      "Epoch 82/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4577 - accuracy: 0.8330\n",
      "Epoch 83/100\n",
      "50/50 [==============================] - 8s 158ms/step - loss: 0.4233 - accuracy: 0.8483\n",
      "Epoch 84/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4043 - accuracy: 0.8538\n",
      "Epoch 85/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4329 - accuracy: 0.8434\n",
      "Epoch 86/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4125 - accuracy: 0.8516\n",
      "Epoch 87/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4329 - accuracy: 0.8432\n",
      "Epoch 88/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4291 - accuracy: 0.8439\n",
      "Epoch 89/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4434 - accuracy: 0.8391\n",
      "Epoch 90/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4138 - accuracy: 0.8514\n",
      "Epoch 91/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.3989 - accuracy: 0.8546\n",
      "Epoch 92/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.3906 - accuracy: 0.8589\n",
      "Epoch 93/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4002 - accuracy: 0.8528\n",
      "Epoch 94/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4216 - accuracy: 0.8468\n",
      "Epoch 95/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.3966 - accuracy: 0.8573\n",
      "Epoch 96/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4235 - accuracy: 0.8451\n",
      "Epoch 97/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.3788 - accuracy: 0.8636\n",
      "Epoch 98/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.4032 - accuracy: 0.8545\n",
      "Epoch 99/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.3918 - accuracy: 0.8576\n",
      "Epoch 100/100\n",
      "50/50 [==============================] - 8s 157ms/step - loss: 0.3825 - accuracy: 0.8621\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_16 (Conv2D)          (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " conv2d_17 (Conv2D)          (None, 28, 28, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_18 (Conv2D)          (None, 26, 26, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_19 (Conv2D)          (None, 24, 24, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_20 (Conv2D)          (None, 22, 22, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_21 (Conv2D)          (None, 20, 20, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_22 (Conv2D)          (None, 18, 18, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_23 (Conv2D)          (None, 16, 16, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_24 (Conv2D)          (None, 14, 14, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_25 (Conv2D)          (None, 12, 12, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_26 (Conv2D)          (None, 10, 10, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_27 (Conv2D)          (None, 8, 8, 32)          9248      \n",
      "                                                                 \n",
      " conv2d_28 (Conv2D)          (None, 6, 6, 32)          9248      \n",
      "                                                                 \n",
      " conv2d_29 (Conv2D)          (None, 4, 4, 32)          9248      \n",
      "                                                                 \n",
      " conv2d_30 (Conv2D)          (None, 2, 2, 32)          9248      \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               66048     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 512)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 160)               82080     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 40)                6440      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                410       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 285,346\n",
      "Trainable params: 285,346\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model_root_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m cnn\u001b[39m.\u001b[39mbuild_model6()\n\u001b[0;32m      4\u001b[0m cnn\u001b[39m.\u001b[39mtrain_model(epochs\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m, batch_size\u001b[39m=\u001b[39m\u001b[39m1000\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m cnn\u001b[39m.\u001b[39mplot_history(model_name\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmodel5\u001b[39m\u001b[39m'\u001b[39m, path\u001b[39m=\u001b[39mmodel_root_path)\n\u001b[0;32m      6\u001b[0m cnn\u001b[39m.\u001b[39mstore_model(model_name\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmodel5\u001b[39m\u001b[39m'\u001b[39m, path\u001b[39m=\u001b[39mmodel_root_path)\n\u001b[0;32m      7\u001b[0m cnn\u001b[39m.\u001b[39mevaluate()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'model_root_path' is not defined"
     ]
    }
   ],
   "source": [
    "# model6\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.build_model6()\n",
    "cnn.train_model(epochs=100, batch_size=1000, verbose=1)\n",
    "cnn.plot_history(model_name='model5', path=model_root_path)\n",
    "cnn.store_model(model_name='model5', path=model_root_path)\n",
    "cnn.evaluate()\n",
    "cnn.store_meta_data(model_name='model5', path=model_root_path)\n",
    "# cnn.clear_mem()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 739ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3600x2400 with 49 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate_proba\n",
    "cnn = CNN(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)\n",
    "cnn.load_model('D:/Note_Database/Subject/BD_ML Big Data and Machine Learning/BD_ML_Code/data/ml_w14_hw/model1_20221217_031354.h5')\n",
    "cnn.evaluate_proba(img_row_num=4, img_col_num=6,\n",
    "                   display_bar_num=3, random=False, start_img_num=10,\n",
    "                   save=True, path=model_root_path,show=True)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}