03-minst_fcnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "yFcrWjzgWlKg"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mnist dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "aFGRZja-XK9h"
   },
   "outputs": [],
   "source": [
    "# MNIST 데이터셋을 불러와 학습셋과 테스트셋으로 저장\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "svwgdJfUXRbO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape : (60000, 28, 28)\n",
      "y_train shape : (60000,)\n",
      "X_test shape : (10000, 28, 28)\n",
      "y_test shape : (10000,)\n"
     ]
    }
   ],
   "source": [
    "# 학습셋과 테스트셋이 각각 몇 개의 이미지로 되어 있는지 확인\n",
    "print(\"X_train shape :\", X_train.shape)\n",
    "print(\"y_train shape :\", y_train.shape)\n",
    "print(\"X_test shape :\", X_test.shape)\n",
    "print(\"y_test shape :\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "dXhjLJn4XVb5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 첫 번째 이미지를 확인\n",
    "# 28 * 28 image\n",
    "plt.imshow(X=X_train[0], cmap='Greys')\n",
    "plt.title(y_train[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "NukQVs_QXocY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  24925324964 0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  46 1301832532532072  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  0  0  0  0  39 1482292532532532501820  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  0  0  24 11422125325325325320178 0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  23 66 21325325325325319881 2  0  0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  18 17121925325325325319580 9  0  0  0  0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  55 17222625325325325324413311 0  0  0  0  0  0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  13625325325321213513216 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  \n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  \n"
     ]
    }
   ],
   "source": [
    "# 이미지 인식\n",
    "for x in X_train[0]:\n",
    "    for i in x:\n",
    "        sys.stdout.write(\"%-3s\" % i)\n",
    "    sys.stdout.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4M6p3vuRZ5k1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 784), (10000, 784))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 차원 변환 과정 (2차원 -> 1차원)\n",
    "X_train = X_train.reshape(60000, 784)\n",
    "X_test = X_test.reshape(10000, 784)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 실수화\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# 정규화 (0 to 1)\n",
    "X_train = X_train / 255\n",
    "X_test = X_test/ 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "MvxToekvaZcL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class : 5 \n"
     ]
    }
   ],
   "source": [
    "# 클래스 값 확인\n",
    "print(\"class : %d \" % (y_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tAeL54mqan3L"
   },
   "outputs": [],
   "source": [
    "# 바이너리화 과정 (One-Hot Encoding)\n",
    "# get_dummies / to_categorical\n",
    "\n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "d9dfbT7TlSqf"
   },
   "outputs": [],
   "source": [
    "# 모델 구조를 설정\n",
    "model = Sequential()\n",
    "model.add(Input(shape=(784,))) # 입력층\n",
    "model.add(Dense(512, activation='relu')) # 은닉층\n",
    "model.add(Dense(10, activation='softmax')) # 출력층"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "UAebSL9tl2ZM"
   },
   "outputs": [],
   "source": [
    "# 모델 실행 환경을 설정\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">401,920</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,130</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m401,920\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m5,130\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">407,050</span> (1.55 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m407,050\u001b[0m (1.55 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">407,050</span> (1.55 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m407,050\u001b[0m (1.55 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Q0drK03_l5KB"
   },
   "outputs": [],
   "source": [
    "# 모델 최적화를 위한 설정 구간\n",
    "modelpath= './model_storage/MNIST_MLP_1.keras'\n",
    "checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss',\n",
    "               verbose=1, save_best_only=True)\n",
    "early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "Rd1vL4gtmGHg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m217/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8366 - loss: 0.5996\n",
      "Epoch 1: val_loss improved from inf to 0.19015, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8393 - loss: 0.5894 - val_accuracy: 0.9459 - val_loss: 0.1901\n",
      "Epoch 2/100\n",
      "\u001b[1m202/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9549 - loss: 0.1614\n",
      "Epoch 2: val_loss improved from 0.19015 to 0.13416, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9552 - loss: 0.1600 - val_accuracy: 0.9600 - val_loss: 0.1342\n",
      "Epoch 3/100\n",
      "\u001b[1m203/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9710 - loss: 0.1033\n",
      "Epoch 3: val_loss improved from 0.13416 to 0.10846, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9711 - loss: 0.1029 - val_accuracy: 0.9681 - val_loss: 0.1085\n",
      "Epoch 4/100\n",
      "\u001b[1m217/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9813 - loss: 0.0695\n",
      "Epoch 4: val_loss improved from 0.10846 to 0.09774, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9812 - loss: 0.0695 - val_accuracy: 0.9703 - val_loss: 0.0977\n",
      "Epoch 5/100\n",
      "\u001b[1m205/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9858 - loss: 0.0520\n",
      "Epoch 5: val_loss improved from 0.09774 to 0.09106, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9857 - loss: 0.0522 - val_accuracy: 0.9729 - val_loss: 0.0911\n",
      "Epoch 6/100\n",
      "\u001b[1m216/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9890 - loss: 0.0423\n",
      "Epoch 6: val_loss improved from 0.09106 to 0.08587, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0423 - val_accuracy: 0.9751 - val_loss: 0.0859\n",
      "Epoch 7/100\n",
      "\u001b[1m201/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9924 - loss: 0.0314\n",
      "Epoch 7: val_loss improved from 0.08587 to 0.08142, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9923 - loss: 0.0315 - val_accuracy: 0.9756 - val_loss: 0.0814\n",
      "Epoch 8/100\n",
      "\u001b[1m224/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9955 - loss: 0.0218\n",
      "Epoch 8: val_loss did not improve from 0.08142\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9955 - loss: 0.0218 - val_accuracy: 0.9729 - val_loss: 0.0905\n",
      "Epoch 9/100\n",
      "\u001b[1m201/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9963 - loss: 0.0194\n",
      "Epoch 9: val_loss improved from 0.08142 to 0.07906, saving model to ./model_storage/MNIST_MLP_1.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9964 - loss: 0.0194 - val_accuracy: 0.9761 - val_loss: 0.0791\n",
      "Epoch 10/100\n",
      "\u001b[1m221/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0137\n",
      "Epoch 10: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0137 - val_accuracy: 0.9748 - val_loss: 0.0863\n",
      "Epoch 11/100\n",
      "\u001b[1m210/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0106\n",
      "Epoch 11: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9988 - loss: 0.0106 - val_accuracy: 0.9757 - val_loss: 0.0853\n",
      "Epoch 12/100\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0095\n",
      "Epoch 12: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0095 - val_accuracy: 0.9781 - val_loss: 0.0794\n",
      "Epoch 13/100\n",
      "\u001b[1m208/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0070\n",
      "Epoch 13: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9992 - loss: 0.0070 - val_accuracy: 0.9775 - val_loss: 0.0811\n",
      "Epoch 14/100\n",
      "\u001b[1m210/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0051\n",
      "Epoch 14: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9996 - loss: 0.0051 - val_accuracy: 0.9777 - val_loss: 0.0838\n",
      "Epoch 15/100\n",
      "\u001b[1m213/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0036\n",
      "Epoch 15: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9998 - loss: 0.0036 - val_accuracy: 0.9775 - val_loss: 0.0828\n",
      "Epoch 16/100\n",
      "\u001b[1m216/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0030\n",
      "Epoch 16: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9999 - loss: 0.0030 - val_accuracy: 0.9783 - val_loss: 0.0836\n",
      "Epoch 17/100\n",
      "\u001b[1m217/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0022\n",
      "Epoch 17: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0022 - val_accuracy: 0.9778 - val_loss: 0.0857\n",
      "Epoch 18/100\n",
      "\u001b[1m211/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 18: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0017 - val_accuracy: 0.9781 - val_loss: 0.0848\n",
      "Epoch 19/100\n",
      "\u001b[1m218/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 19: val_loss did not improve from 0.07906\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9786 - val_loss: 0.0863\n"
     ]
    }
   ],
   "source": [
    "# 모델을 실행\n",
    "history = model.fit(X_train, y_train, validation_split=0.25, epochs=100, \n",
    "                    batch_size=200, verbose=1, callbacks=[checkpointer, early_stopping_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "R7St2TUKmfCZ"
   },
   "outputs": [],
   "source": [
    "# 검증셋과 학습셋의 오차를 저장\n",
    "y_vloss = history.history['val_loss']\n",
    "y_loss = history.history['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "PcXsAiNymjh7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 표현\n",
    "x_len = np.arange(len(y_loss))\n",
    "plt.plot(x_len, y_vloss, marker='.', c=\"red\", label='Testset_loss')\n",
    "plt.plot(x_len, y_loss, marker='.', c=\"blue\", label='Trainset_loss')\n",
    "\n",
    "# 그래프에 그리드를 주고 레이블을 표시\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 과적합 현상"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721us/step - accuracy: 0.9808 - loss: 0.0752\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.06490882486104965, 0.982699990272522]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "owen",
   "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": "3.12.4"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}