NIN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-10 22:42:35.070867: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-10 22:42:35.087403: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-10 22:42:35.087451: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-08-10 22:42:35.096570: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-08-10 22:42:35.605275: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import cifar100\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow는 GPU를 사용할 수 있습니다.\n",
      "사용 가능한 GPU: PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-10 22:42:36.144835: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-08-10 22:42:36.166524: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2024-08-10 22:42:36.166569: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n"
     ]
    }
   ],
   "source": [
    "# GPU 장치 목록 확인\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    print(\"TensorFlow는 GPU를 사용할 수 있습니다.\")\n",
    "    for gpu in gpus:\n",
    "        print(f\"사용 가능한 GPU: {gpu}\")\n",
    "\n",
    "    # gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "    # print(gpus)\n",
    "    # if gpus:\n",
    "    try:\n",
    "        tf.config.set_visible_devices(gpus[0], 'GPU')\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    print(\"TensorFlow는 GPU를 사용할 수 없습니다.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST 데이터셋 로드\n",
    "(x_train, y_train), (x_test, y_test) = cifar100.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 전처리\n",
    "x_train = x_train.reshape(-1, 32, 32, 3).astype('float32') / 255.0\n",
    "x_test = x_test.reshape(-1, 32, 32, 3).astype('float32') / 255.0\n",
    "y_train = to_categorical(y_train, 100)\n",
    "y_test = to_categorical(y_test, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tf.keras.Input(shape=(32, 32, 3))\n",
    "\n",
    "# 첫 번째 블록\n",
    "x = tf.keras.layers.Conv2D(filters=32, kernel_size=(5, 5), padding='valid', activation='relu')(inputs)\n",
    "# x = tf.keras.layers.Conv2D(filters=32, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=16, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=32, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "x = tf.keras.layers.BatchNormalization()(x)\n",
    "x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)\n",
    "\n",
    "# 두 번째 블록 (MLPConv 레이어)\n",
    "x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')(x)\n",
    "x = tf.keras.layers.Conv2D(filters=64, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=16, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=128, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "x = tf.keras.layers.BatchNormalization()(x)\n",
    "x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)\n",
    "\n",
    "# 세 번째 블록 (MLPConv 레이어)\n",
    "x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu')(x)\n",
    "x = tf.keras.layers.Conv2D(filters=128, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=32, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=192, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.BatchNormalization()(x)\n",
    "x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)\n",
    "\n",
    "# 내 번째 블록 (MLPConv 레이어)\n",
    "# x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=64, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=64, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "# x = tf.keras.layers.Conv2D(filters=128, kernel_size=(1, 1), padding='same', activation='relu')(x)\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "\n",
    "x = tf.keras.layers.Dense(100, activation='softmax')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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: \"functional_31\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_31\"\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",
       "│ input_layer_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_307 (<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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,432</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_14          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_70 (<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_308 (<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\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_309 (<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\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_15          │ (<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\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_71 (<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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_310 (<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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_311 (<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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_72 (<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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d_36     │ (<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\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_33 (<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\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,900</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",
       "│ input_layer_36 (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_307 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m2,432\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_14          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_70 (\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_308 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_309 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m4,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_15          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_71 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_310 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_311 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m16,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_72 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d_36     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_33 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m12,900\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\">129,252</span> (504.89 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m129,252\u001b[0m (504.89 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\">128,804</span> (503.14 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m128,804\u001b[0m (503.14 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\">448</span> (1.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 모델 생성\n",
    "model = tf.keras.Model(inputs=inputs, outputs=x)\n",
    "\n",
    "# 모델 컴파일\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# 모델 요약\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.0902 - loss: 4.1656 - val_accuracy: 0.1108 - val_loss: 4.1019\n",
      "Epoch 2/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2357 - loss: 3.1481 - val_accuracy: 0.2581 - val_loss: 3.0507\n",
      "Epoch 3/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.3068 - loss: 2.7401 - val_accuracy: 0.2541 - val_loss: 3.1003\n",
      "Epoch 4/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.3610 - loss: 2.4841 - val_accuracy: 0.2214 - val_loss: 3.3821\n",
      "Epoch 5/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4033 - loss: 2.2868 - val_accuracy: 0.3144 - val_loss: 2.7600\n",
      "Epoch 6/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4399 - loss: 2.1312 - val_accuracy: 0.3063 - val_loss: 2.9781\n",
      "Epoch 7/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4742 - loss: 1.9982 - val_accuracy: 0.3437 - val_loss: 2.6344\n",
      "Epoch 8/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4935 - loss: 1.8809 - val_accuracy: 0.3020 - val_loss: 3.0904\n",
      "Epoch 9/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.5231 - loss: 1.7679 - val_accuracy: 0.3665 - val_loss: 2.5518\n",
      "Epoch 10/10\n",
      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.5362 - loss: 1.7002 - val_accuracy: 0.3671 - val_loss: 2.5569\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7f2bb4eed990>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "model.fit(x_train, y_train, epochs=10, batch_size=64, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.3786 - loss: 2.5546\n",
      "Test Loss: 2.5462\n",
      "Test Accuracy: 0.3751\n"
     ]
    }
   ],
   "source": [
    "# 모델 평가\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print(f\"Test Loss: {test_loss:.4f}\")\n",
    "print(f\"Test Accuracy: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 665ms/step\n",
      "television\n",
      "Predicted Class: 83\n",
      "Predictions: [[1.35430023e-01 8.39571101e-07 1.00743819e-06 1.11924701e-08\n",
      "  4.20343547e-08 6.18094873e-06 2.32851480e-08 2.40499885e-05\n",
      "  4.87393859e-09 2.40078208e-07 8.95340985e-04 9.47573335e-06\n",
      "  3.06416559e-10 6.27219805e-08 1.17378067e-05 9.16298404e-07\n",
      "  5.83336107e-04 4.59413826e-11 4.39280348e-06 1.61452263e-07\n",
      "  1.01702881e-06 4.41120562e-09 2.17689259e-04 3.73397873e-13\n",
      "  1.85442957e-06 4.84917218e-05 2.34069172e-04 1.02449285e-07\n",
      "  3.11174904e-06 2.43909597e-07 9.54591339e-09 1.79392404e-10\n",
      "  7.54317007e-05 7.50800117e-11 5.19197352e-09 3.81828329e-07\n",
      "  3.15769788e-09 5.00859043e-09 1.39667664e-08 4.73071050e-05\n",
      "  3.84378336e-06 1.91914501e-06 2.28772690e-09 1.36669648e-10\n",
      "  1.80172719e-06 2.15014457e-04 2.37623610e-07 5.80604826e-08\n",
      "  2.45405658e-07 6.77127077e-10 5.06217248e-07 7.56879004e-08\n",
      "  9.76147829e-10 1.97453392e-05 2.47645276e-05 2.73304277e-08\n",
      "  4.49754622e-10 1.22972578e-03 1.13108490e-06 1.12120215e-08\n",
      "  5.26821253e-10 1.74973884e-05 2.68184920e-07 1.19013710e-09\n",
      "  2.69930384e-10 4.54706367e-07 3.08991019e-08 2.18687433e-06\n",
      "  1.08307230e-09 2.18874252e-09 6.23709857e-05 1.42023990e-10\n",
      "  1.03434206e-06 1.01484638e-07 1.12476684e-07 1.36467406e-08\n",
      "  1.79482082e-10 2.03101149e-06 1.47723231e-05 6.65939437e-09\n",
      "  5.42291678e-09 3.73863052e-10 3.07630827e-08 8.60055864e-01\n",
      "  6.67462609e-06 1.59241631e-06 6.07347247e-05 1.35931100e-06\n",
      "  4.15059676e-09 7.95756883e-09 5.15860961e-07 8.61256103e-06\n",
      "  6.47010864e-04 2.15646821e-07 4.48860398e-08 2.26727749e-07\n",
      "  4.06912726e-10 4.30483871e-09 2.19220624e-07 1.93582964e-05]]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "# 이미지 로드 및 전처리\n",
    "def prepare_image(img_path, target_size=(32, 32)):\n",
    "    # 이미지를 열고 리사이즈\n",
    "    img = Image.open(img_path)\n",
    "    img = img.resize(target_size)\n",
    "    \n",
    "    # 이미지를 numpy 배열로 변환하고 차원 추가\n",
    "    img_array = np.array(img)\n",
    "    \n",
    "    # 색상 채널이 3개인지 확인\n",
    "    if img_array.shape[-1] != 3:\n",
    "        img_array = np.stack([img_array] * 3, axis=-1)\n",
    "    \n",
    "    img_array = np.expand_dims(img_array, axis=0)  # 배치 차원 추가\n",
    "    img_array = img_array.astype('float32') / 255.0  # 정규화\n",
    "    return img_array\n",
    "\n",
    "# 예측 함수\n",
    "def predict_image(model, img_path):\n",
    "    img_array = prepare_image(img_path)\n",
    "    predictions = model.predict(img_array)\n",
    "    predicted_class = np.argmax(predictions, axis=1)\n",
    "    return predicted_class[0], predictions\n",
    "\n",
    "# 예측할 이미지 경로\n",
    "img_path = 'apple.png'\n",
    "\n",
    "# 예측 수행\n",
    "predicted_class, predictions = predict_image(model, img_path)\n",
    "\n",
    "cifar100_class_names = [\n",
    "    'apple', 'aquarium_fish', 'baby', 'bear', 'beetle', 'bicycle', 'bottle', 'bowl',\n",
    "    'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar',\n",
    "    'chair', 'clock', 'cloud', 'cocktail', 'computer', 'couch', 'crab', 'cup',\n",
    "    'dinosaur', 'dog', 'drum', 'elephant', 'flatfish', 'forest', 'fox', 'girl',\n",
    "    'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',\n",
    "    'lizard', 'mailbox', 'man', 'map', 'motorcycle', 'mountain', 'mouse', 'mushroom',\n",
    "    'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree',\n",
    "    'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray',\n",
    "    'road', 'rocket', 'rose', 'seal', 'shark', 'shoes', 'skateboard', 'skis',\n",
    "    'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper',\n",
    "    'table', 'tank', 'telephone', 'television', 'tiger', 'toilet', 'train', 'truck',\n",
    "    'umbrella', 'waterfall', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'\n",
    "]\n",
    "print(cifar100_class_names[predicted_class])\n",
    "print(f\"Predicted Class: {predicted_class}\")\n",
    "print(f\"Predictions: {predictions}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}