04-mnist_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7981cc33-ec57-4076-8522-f4ca464ccea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Input\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ff0bdea-d7d2-45a0-a3ee-3b0f9b6ee410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load_data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad8a90ed-e6fe-44ab-930c-3cda964249a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89a4b6dc-19c0-4931-a09a-acf16d72e8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2차원 -> 3차원 변환, 가로 * 세로 * rgb\n",
    "X_train = X_train.reshape(60000, 28, 28, 1)\n",
    "X_test = X_test.reshape(10000, 28, 28, 1)\n",
    "\n",
    "# 실수화\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# 정규화\n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38f32a5a-5d5b-4f37-bdcf-e764b8cad7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6bcabf42-d0af-48e3-a5a9-486b3c2a14b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Input(shape=(28, 28, 1)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ce88222-b20b-4852-946e-54f5f2b96d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7082d808",
   "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",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">800</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">102,528</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\">1,290</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",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m800\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m102,528\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;34m1,290\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\">113,386</span> (442.91 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m113,386\u001b[0m (442.91 KB)\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\">113,386</span> (442.91 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m113,386\u001b[0m (442.91 KB)\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": 9,
   "id": "3ca2b599-30ac-4131-9ecd-abac0c5580ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 최적화를 위한 설정 구간\n",
    "modelpath= './model_storage/MNIST_MLP_2.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": 10,
   "id": "0f22e01f-c829-483e-8dc8-ee6c6ed18eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m221/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7813 - loss: 0.7682\n",
      "Epoch 1: val_loss improved from inf to 0.11844, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.7839 - loss: 0.7591 - val_accuracy: 0.9652 - val_loss: 0.1184\n",
      "Epoch 2/30\n",
      "\u001b[1m219/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9707 - loss: 0.0966\n",
      "Epoch 2: val_loss improved from 0.11844 to 0.07730, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9708 - loss: 0.0964 - val_accuracy: 0.9759 - val_loss: 0.0773\n",
      "Epoch 3/30\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9785 - loss: 0.0674\n",
      "Epoch 3: val_loss improved from 0.07730 to 0.06928, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9785 - loss: 0.0674 - val_accuracy: 0.9782 - val_loss: 0.0693\n",
      "Epoch 4/30\n",
      "\u001b[1m221/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9844 - loss: 0.0482\n",
      "Epoch 4: val_loss improved from 0.06928 to 0.06446, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9844 - loss: 0.0482 - val_accuracy: 0.9816 - val_loss: 0.0645\n",
      "Epoch 5/30\n",
      "\u001b[1m219/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9877 - loss: 0.0413\n",
      "Epoch 5: val_loss improved from 0.06446 to 0.05044, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9877 - loss: 0.0414 - val_accuracy: 0.9851 - val_loss: 0.0504\n",
      "Epoch 6/30\n",
      "\u001b[1m223/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9900 - loss: 0.0338\n",
      "Epoch 6: val_loss did not improve from 0.05044\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9900 - loss: 0.0338 - val_accuracy: 0.9827 - val_loss: 0.0584\n",
      "Epoch 7/30\n",
      "\u001b[1m224/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9906 - loss: 0.0288\n",
      "Epoch 7: val_loss did not improve from 0.05044\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9906 - loss: 0.0288 - val_accuracy: 0.9827 - val_loss: 0.0555\n",
      "Epoch 8/30\n",
      "\u001b[1m222/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9924 - loss: 0.0242\n",
      "Epoch 8: val_loss improved from 0.05044 to 0.04967, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9924 - loss: 0.0242 - val_accuracy: 0.9857 - val_loss: 0.0497\n",
      "Epoch 9/30\n",
      "\u001b[1m224/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9934 - loss: 0.0200\n",
      "Epoch 9: val_loss did not improve from 0.04967\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9934 - loss: 0.0201 - val_accuracy: 0.9858 - val_loss: 0.0500\n",
      "Epoch 10/30\n",
      "\u001b[1m220/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9941 - loss: 0.0183\n",
      "Epoch 10: val_loss did not improve from 0.04967\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9941 - loss: 0.0183 - val_accuracy: 0.9849 - val_loss: 0.0498\n",
      "Epoch 11/30\n",
      "\u001b[1m221/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9957 - loss: 0.0133\n",
      "Epoch 11: val_loss improved from 0.04967 to 0.04583, saving model to ./model_storage/MNIST_MLP_2.keras\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9957 - loss: 0.0134 - val_accuracy: 0.9866 - val_loss: 0.0458\n",
      "Epoch 12/30\n",
      "\u001b[1m224/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9960 - loss: 0.0131\n",
      "Epoch 12: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9960 - loss: 0.0131 - val_accuracy: 0.9872 - val_loss: 0.0459\n",
      "Epoch 13/30\n",
      "\u001b[1m221/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9961 - loss: 0.0116\n",
      "Epoch 13: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.9961 - loss: 0.0116 - val_accuracy: 0.9875 - val_loss: 0.0466\n",
      "Epoch 14/30\n",
      "\u001b[1m224/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9975 - loss: 0.0089\n",
      "Epoch 14: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.9975 - loss: 0.0089 - val_accuracy: 0.9870 - val_loss: 0.0491\n",
      "Epoch 15/30\n",
      "\u001b[1m220/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9975 - loss: 0.0081\n",
      "Epoch 15: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9975 - loss: 0.0081 - val_accuracy: 0.9874 - val_loss: 0.0479\n",
      "Epoch 16/30\n",
      "\u001b[1m223/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9977 - loss: 0.0079\n",
      "Epoch 16: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9977 - loss: 0.0079 - val_accuracy: 0.9871 - val_loss: 0.0505\n",
      "Epoch 17/30\n",
      "\u001b[1m222/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9981 - loss: 0.0062\n",
      "Epoch 17: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9981 - loss: 0.0062 - val_accuracy: 0.9881 - val_loss: 0.0514\n",
      "Epoch 18/30\n",
      "\u001b[1m221/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9992 - loss: 0.0035\n",
      "Epoch 18: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.9992 - loss: 0.0035 - val_accuracy: 0.9885 - val_loss: 0.0514\n",
      "Epoch 19/30\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9985 - loss: 0.0049\n",
      "Epoch 19: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9985 - loss: 0.0049 - val_accuracy: 0.9876 - val_loss: 0.0546\n",
      "Epoch 20/30\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9988 - loss: 0.0038\n",
      "Epoch 20: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9988 - loss: 0.0038 - val_accuracy: 0.9850 - val_loss: 0.0693\n",
      "Epoch 21/30\n",
      "\u001b[1m222/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9981 - loss: 0.0073\n",
      "Epoch 21: val_loss did not improve from 0.04583\n",
      "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.9981 - loss: 0.0073 - val_accuracy: 0.9891 - val_loss: 0.0570\n"
     ]
    }
   ],
   "source": [
    "# 모델을 실행\n",
    "history = model.fit(X_train, y_train, validation_split=0.25, epochs=30, \n",
    "         batch_size=200, verbose=1, callbacks=[checkpointer, early_stopping_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93cbb574-067f-4627-aa5a-f7b41dc05dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 검증셋과 학습셋의 오차를 저장\n",
    "y_vloss = history.history['val_loss']\n",
    "y_loss = history.history['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f2eee5d-1f2f-4219-ba16-8c4a419584a0",
   "metadata": {},
   "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()"
   ]
  }
 ],
 "metadata": {
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}