final assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dcfd17b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119\n",
      "119\n",
      "59\n",
      "59\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "base_dir = '/Users/jw1010110/AI/car-dataset'\n",
    "\n",
    "train_dir = os.path.join(base_dir,'train') #train 폴더 경로\n",
    "validation_dir = os.path.join(base_dir,'validation')#validation 폴더 경로\n",
    "\n",
    "train_sedan_dir = os.path.join(train_dir,'sedan')\n",
    "train_suv_dir = os.path.join(train_dir,'suv')\n",
    "\n",
    "validation_sedan_dir = os.path.join(validation_dir,'sedan')\n",
    "validation_suv_dir = os.path.join(validation_dir,'suv')\n",
    "\n",
    "print(len(os.listdir(train_sedan_dir)))\n",
    "print(len(os.listdir(train_suv_dir)))\n",
    "print(len(os.listdir(validation_sedan_dir)))\n",
    "print(len(os.listdir(validation_suv_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85600465",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Pro\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-14 12:37:35.204342: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-06-14 12:37:35.204477: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "conv_base = VGG16(weights='imagenet',include_top=False,input_shape=(150,150,3)) #별도의 세단과 suv 클래스 2개를 구분하는 완전 연결층 추가할거임"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "febeda5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "641f4bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#합성곱 기반층 위에 완전 연결 분류기 추가\n",
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten()) #4*4*512=8192\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(256,activation='relu'))\n",
    "model.add(layers.Dense(1,activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a701ce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " vgg16 (Functional)          (None, 4, 4, 512)         14714688  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 8192)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 8192)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               2097408   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 257       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16,812,353\n",
      "Trainable params: 16,812,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47833034",
   "metadata": {},
   "outputs": [],
   "source": [
    "#사전에 학습된 표현이 훈련되는 동안 수정되는 것을 방지하기 위해 동결\n",
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a0bb3e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 238 images belonging to 2 classes.\n",
      "Found 118 images belonging to 2 classes.\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pn/m2bqkk014sv8108lk7tl4xy80000gn/T/ipykernel_34487/1115708154.py:35: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_generator,\n",
      "2022-06-14 12:50:41.981311: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - ETA: 0s - loss: 0.3001 - acc: 0.9083"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-14 12:50:42.955803: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 2s 171ms/step - loss: 0.3001 - acc: 0.9083 - val_loss: 0.2228 - val_acc: 0.8833\n",
      "Epoch 2/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0228 - acc: 1.0000 - val_loss: 0.5132 - val_acc: 0.8833\n",
      "Epoch 3/100\n",
      "6/6 [==============================] - 1s 149ms/step - loss: 0.0217 - acc: 1.0000 - val_loss: 0.4077 - val_acc: 0.8833\n",
      "Epoch 4/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.1537 - acc: 0.9333 - val_loss: 0.8506 - val_acc: 0.7833\n",
      "Epoch 5/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0682 - acc: 0.9750 - val_loss: 0.3446 - val_acc: 0.8500\n",
      "Epoch 6/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0276 - acc: 1.0000 - val_loss: 0.3324 - val_acc: 0.8833\n",
      "Epoch 7/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0569 - acc: 0.9833 - val_loss: 1.1448 - val_acc: 0.7333\n",
      "Epoch 8/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0453 - acc: 0.9917 - val_loss: 0.4233 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0560 - acc: 0.9750 - val_loss: 0.4130 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0357 - acc: 1.0000 - val_loss: 0.5188 - val_acc: 0.8167\n",
      "Epoch 11/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0927 - acc: 0.9576 - val_loss: 0.6514 - val_acc: 0.7833\n",
      "Epoch 12/100\n",
      "6/6 [==============================] - 1s 137ms/step - loss: 0.0321 - acc: 0.9915 - val_loss: 0.3917 - val_acc: 0.8500\n",
      "Epoch 13/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0740 - acc: 0.9661 - val_loss: 1.6594 - val_acc: 0.6833\n",
      "Epoch 14/100\n",
      "6/6 [==============================] - 1s 137ms/step - loss: 0.0858 - acc: 0.9661 - val_loss: 0.3343 - val_acc: 0.8667\n",
      "Epoch 15/100\n",
      "6/6 [==============================] - 1s 137ms/step - loss: 0.0221 - acc: 1.0000 - val_loss: 0.3951 - val_acc: 0.8500\n",
      "Epoch 16/100\n",
      "6/6 [==============================] - 1s 141ms/step - loss: 0.0281 - acc: 0.9917 - val_loss: 0.3039 - val_acc: 0.9167\n",
      "Epoch 17/100\n",
      "6/6 [==============================] - 1s 147ms/step - loss: 0.0112 - acc: 1.0000 - val_loss: 0.3468 - val_acc: 0.8167\n",
      "Epoch 18/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0185 - acc: 1.0000 - val_loss: 0.8215 - val_acc: 0.8333\n",
      "Epoch 19/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0162 - acc: 1.0000 - val_loss: 0.3477 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0222 - acc: 1.0000 - val_loss: 0.5247 - val_acc: 0.7667\n",
      "Epoch 21/100\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.0137 - acc: 1.0000 - val_loss: 0.4303 - val_acc: 0.8167\n",
      "Epoch 22/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.3480 - acc: 0.8814 - val_loss: 0.6082 - val_acc: 0.8000\n",
      "Epoch 23/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0492 - acc: 0.9917 - val_loss: 0.5043 - val_acc: 0.8500\n",
      "Epoch 24/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0060 - acc: 1.0000 - val_loss: 0.3920 - val_acc: 0.8333\n",
      "Epoch 25/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0177 - acc: 1.0000 - val_loss: 0.3622 - val_acc: 0.8667\n",
      "Epoch 26/100\n",
      "6/6 [==============================] - 1s 147ms/step - loss: 0.0290 - acc: 0.9831 - val_loss: 0.5850 - val_acc: 0.8667\n",
      "Epoch 27/100\n",
      "6/6 [==============================] - 1s 160ms/step - loss: 0.0166 - acc: 1.0000 - val_loss: 0.3865 - val_acc: 0.8667\n",
      "Epoch 28/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0196 - acc: 0.9915 - val_loss: 0.5408 - val_acc: 0.8333\n",
      "Epoch 29/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0075 - acc: 1.0000 - val_loss: 0.5599 - val_acc: 0.8000\n",
      "Epoch 30/100\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.0284 - acc: 1.0000 - val_loss: 0.2261 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0065 - acc: 1.0000 - val_loss: 0.4223 - val_acc: 0.9167\n",
      "Epoch 32/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.1719 - val_acc: 0.8833\n",
      "Epoch 33/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.1101 - acc: 0.9746 - val_loss: 0.8995 - val_acc: 0.7833\n",
      "Epoch 34/100\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.0660 - acc: 0.9833 - val_loss: 0.5856 - val_acc: 0.8667\n",
      "Epoch 35/100\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.0081 - acc: 1.0000 - val_loss: 0.3514 - val_acc: 0.8833\n",
      "Epoch 36/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0175 - acc: 0.9917 - val_loss: 0.4040 - val_acc: 0.8667\n",
      "Epoch 37/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0046 - acc: 1.0000 - val_loss: 0.4050 - val_acc: 0.8500\n",
      "Epoch 38/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0424 - acc: 0.9746 - val_loss: 0.8267 - val_acc: 0.7167\n",
      "Epoch 39/100\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.0768 - acc: 0.9833 - val_loss: 0.1987 - val_acc: 0.9000\n",
      "Epoch 40/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0099 - acc: 1.0000 - val_loss: 0.4196 - val_acc: 0.8667\n",
      "Epoch 41/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0161 - acc: 1.0000 - val_loss: 0.3891 - val_acc: 0.9000\n",
      "Epoch 42/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0071 - acc: 1.0000 - val_loss: 0.3347 - val_acc: 0.9000\n",
      "Epoch 43/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0050 - acc: 1.0000 - val_loss: 0.5623 - val_acc: 0.8833\n",
      "Epoch 44/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0378 - acc: 0.9831 - val_loss: 1.4907 - val_acc: 0.7167\n",
      "Epoch 45/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0492 - acc: 0.9917 - val_loss: 0.2348 - val_acc: 0.9167\n",
      "Epoch 46/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0105 - acc: 1.0000 - val_loss: 0.5394 - val_acc: 0.8500\n",
      "Epoch 47/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0334 - acc: 0.9915 - val_loss: 0.2925 - val_acc: 0.9167\n",
      "Epoch 48/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0425 - acc: 0.9917 - val_loss: 0.5998 - val_acc: 0.8500\n",
      "Epoch 49/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0581 - acc: 0.9833 - val_loss: 0.7181 - val_acc: 0.8333\n",
      "Epoch 50/100\n",
      "6/6 [==============================] - 1s 153ms/step - loss: 0.0096 - acc: 1.0000 - val_loss: 0.5659 - val_acc: 0.8167\n",
      "Epoch 51/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0184 - acc: 0.9917 - val_loss: 0.2092 - val_acc: 0.9000\n",
      "Epoch 52/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.3277 - val_acc: 0.8500\n",
      "Epoch 53/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0056 - acc: 1.0000 - val_loss: 0.2583 - val_acc: 0.8667\n",
      "Epoch 54/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0088 - acc: 1.0000 - val_loss: 0.3159 - val_acc: 0.8500\n",
      "Epoch 55/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.3891 - val_acc: 0.8333\n",
      "Epoch 56/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0767 - acc: 0.9583 - val_loss: 0.3829 - val_acc: 0.8667\n",
      "Epoch 57/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.3018 - val_acc: 0.8333\n",
      "Epoch 58/100\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.0113 - acc: 1.0000 - val_loss: 0.5350 - val_acc: 0.8667\n",
      "Epoch 59/100\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.3611 - val_acc: 0.8667\n",
      "Epoch 60/100\n",
      "6/6 [==============================] - 1s 154ms/step - loss: 0.0184 - acc: 1.0000 - val_loss: 0.3595 - val_acc: 0.8667\n",
      "Epoch 61/100\n",
      "6/6 [==============================] - 1s 150ms/step - loss: 0.0065 - acc: 1.0000 - val_loss: 0.3906 - val_acc: 0.8000\n",
      "Epoch 62/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0057 - acc: 1.0000 - val_loss: 0.3084 - val_acc: 0.8500\n",
      "Epoch 63/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 1s 150ms/step - loss: 0.0179 - acc: 1.0000 - val_loss: 1.3046 - val_acc: 0.7833\n",
      "Epoch 64/100\n",
      "6/6 [==============================] - 1s 154ms/step - loss: 0.0306 - acc: 0.9750 - val_loss: 0.6149 - val_acc: 0.8667\n",
      "Epoch 65/100\n",
      "6/6 [==============================] - 1s 150ms/step - loss: 0.0034 - acc: 1.0000 - val_loss: 0.4308 - val_acc: 0.9000\n",
      "Epoch 66/100\n",
      "6/6 [==============================] - 1s 151ms/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.4228 - val_acc: 0.8833\n",
      "Epoch 67/100\n",
      "6/6 [==============================] - 1s 155ms/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.3371 - val_acc: 0.9000\n",
      "Epoch 68/100\n",
      "6/6 [==============================] - 1s 149ms/step - loss: 7.7355e-04 - acc: 1.0000 - val_loss: 0.4536 - val_acc: 0.9000\n",
      "Epoch 69/100\n",
      "6/6 [==============================] - 1s 151ms/step - loss: 0.1077 - acc: 0.9583 - val_loss: 0.4941 - val_acc: 0.8333\n",
      "Epoch 70/100\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.0232 - acc: 0.9917 - val_loss: 0.5228 - val_acc: 0.8833\n",
      "Epoch 71/100\n",
      "6/6 [==============================] - 1s 141ms/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.4677 - val_acc: 0.8167\n",
      "Epoch 72/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.3608 - val_acc: 0.8833\n",
      "Epoch 73/100\n",
      "6/6 [==============================] - 1s 137ms/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.2733 - val_acc: 0.8833\n",
      "Epoch 74/100\n",
      "6/6 [==============================] - 1s 147ms/step - loss: 0.0109 - acc: 0.9917 - val_loss: 1.4685 - val_acc: 0.7500\n",
      "Epoch 75/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0267 - acc: 0.9750 - val_loss: 0.3792 - val_acc: 0.8667\n",
      "Epoch 76/100\n",
      "6/6 [==============================] - 1s 152ms/step - loss: 0.0222 - acc: 0.9833 - val_loss: 1.0085 - val_acc: 0.8500\n",
      "Epoch 77/100\n",
      "6/6 [==============================] - 1s 148ms/step - loss: 0.0390 - acc: 0.9750 - val_loss: 0.6020 - val_acc: 0.8500\n",
      "Epoch 78/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0198 - acc: 0.9915 - val_loss: 0.2863 - val_acc: 0.8667\n",
      "Epoch 79/100\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.3620 - val_acc: 0.8833\n",
      "Epoch 80/100\n",
      "6/6 [==============================] - 1s 141ms/step - loss: 6.6417e-04 - acc: 1.0000 - val_loss: 0.2374 - val_acc: 0.9167\n",
      "Epoch 81/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0044 - acc: 1.0000 - val_loss: 0.3967 - val_acc: 0.8500\n",
      "Epoch 82/100\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.0118 - acc: 1.0000 - val_loss: 0.3807 - val_acc: 0.8500\n",
      "Epoch 83/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0366 - acc: 0.9915 - val_loss: 0.3098 - val_acc: 0.8333\n",
      "Epoch 84/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.4955 - val_acc: 0.8667\n",
      "Epoch 85/100\n",
      "6/6 [==============================] - 1s 140ms/step - loss: 0.0317 - acc: 0.9833 - val_loss: 1.1321 - val_acc: 0.8000\n",
      "Epoch 86/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0637 - acc: 0.9746 - val_loss: 0.5312 - val_acc: 0.8000\n",
      "Epoch 87/100\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.0145 - acc: 0.9915 - val_loss: 0.2856 - val_acc: 0.8500\n",
      "Epoch 88/100\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.5164 - val_acc: 0.8500\n",
      "Epoch 89/100\n",
      "6/6 [==============================] - 1s 150ms/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.2983 - val_acc: 0.8833\n",
      "Epoch 90/100\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.3174 - val_acc: 0.8833\n",
      "Epoch 91/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.3111 - val_acc: 0.9167\n",
      "Epoch 92/100\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.3900 - val_acc: 0.8500\n",
      "Epoch 93/100\n",
      "6/6 [==============================] - 1s 142ms/step - loss: 0.0010 - acc: 1.0000 - val_loss: 0.4641 - val_acc: 0.8500\n",
      "Epoch 94/100\n",
      "6/6 [==============================] - 1s 150ms/step - loss: 8.2577e-04 - acc: 1.0000 - val_loss: 0.3393 - val_acc: 0.9167\n",
      "Epoch 95/100\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 0.0929 - acc: 0.9417 - val_loss: 0.6648 - val_acc: 0.7833\n",
      "Epoch 96/100\n",
      "6/6 [==============================] - 1s 149ms/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.3108 - val_acc: 0.9000\n",
      "Epoch 97/100\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.2041 - val_acc: 0.9333\n",
      "Epoch 98/100\n",
      "6/6 [==============================] - 1s 148ms/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.2795 - val_acc: 0.9167\n",
      "Epoch 99/100\n",
      "6/6 [==============================] - 1s 155ms/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.5136 - val_acc: 0.8667\n",
      "Epoch 100/100\n",
      "6/6 [==============================] - 1s 147ms/step - loss: 9.3439e-04 - acc: 1.0000 - val_loss: 0.2744 - val_acc: 0.8833\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.1,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest'\n",
    ")\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(150,150),\n",
    "    batch_size=20,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    validation_dir,\n",
    "    target_size=(150,150),\n",
    "    batch_size=20,\n",
    "    class_mode='binary'\n",
    ")\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "             optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "             metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(train_generator,\n",
    "                              steps_per_epoch=6,\n",
    "                              epochs=100,\n",
    "                              validation_data=validation_generator,\n",
    "                             validation_steps=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e8799d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1,len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs,acc,'bo',label='Training acc')\n",
    "plt.plot(epochs,val_acc,'b',label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,loss,'bo',label='training loss')\n",
    "plt.plot(epochs, val_loss,'b',label='validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb12c9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#지수평균이동으로 부드러운 그래프\n",
    "def smooth_curve(points, factor=0.8): \n",
    "    smoothed_points = []\n",
    "    for point in points: \n",
    "        if smoothed_points: # not empty\n",
    "            previous = smoothed_points[-1]\n",
    "            smoothed_points.append(previous * factor + point * (1 - factor)) \n",
    "        else:\n",
    "            smoothed_points.append(point) \n",
    "    return smoothed_points\n",
    "\n",
    "plt.plot(epochs,smooth_curve(acc),'bo',label='smoothed training acc')\n",
    "plt.plot(epochs,smooth_curve(val_acc),'b',label='smoothed validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,smooth_curve(loss),'bo',label='smoothed training loss')\n",
    "plt.plot(epochs,smooth_curve(val_loss),'b',label='smoothed validation loss')\n",
    "plt.title('Trainging and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64fd96bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#특정 층까지 모든 층 동결하기\n",
    "conv_base.trainable = True\n",
    "\n",
    "set_trainable = False\n",
    "for layer in conv_base.layers:\n",
    "    if layer.name == 'block5_conv1':\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f5c46ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pn/m2bqkk014sv8108lk7tl4xy80000gn/T/ipykernel_34487/2071223682.py:5: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_generator,\n",
      "2022-06-14 12:53:56.760754: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - ETA: 0s - loss: 0.0343 - acc: 0.9915 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-14 12:53:57.953479: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 2s 191ms/step - loss: 0.0343 - acc: 0.9915 - val_loss: 0.4788 - val_acc: 0.8667\n",
      "Epoch 2/20\n",
      "6/6 [==============================] - 1s 149ms/step - loss: 0.0010 - acc: 1.0000 - val_loss: 0.4434 - val_acc: 0.8833\n",
      "Epoch 3/20\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 6.6575e-05 - acc: 1.0000 - val_loss: 0.7424 - val_acc: 0.8167\n",
      "Epoch 4/20\n",
      "6/6 [==============================] - 1s 158ms/step - loss: 1.6422e-04 - acc: 1.0000 - val_loss: 0.5922 - val_acc: 0.8667\n",
      "Epoch 5/20\n",
      "6/6 [==============================] - 1s 144ms/step - loss: 0.0084 - acc: 1.0000 - val_loss: 0.3861 - val_acc: 0.9000\n",
      "Epoch 6/20\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.5174 - val_acc: 0.8667\n",
      "Epoch 7/20\n",
      "6/6 [==============================] - 1s 143ms/step - loss: 4.4295e-04 - acc: 1.0000 - val_loss: 0.5470 - val_acc: 0.8667\n",
      "Epoch 8/20\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 5.5606e-04 - acc: 1.0000 - val_loss: 0.1991 - val_acc: 0.9167\n",
      "Epoch 9/20\n",
      "6/6 [==============================] - 1s 139ms/step - loss: 3.4972e-05 - acc: 1.0000 - val_loss: 0.5134 - val_acc: 0.9167\n",
      "Epoch 10/20\n",
      "6/6 [==============================] - 1s 146ms/step - loss: 8.4653e-05 - acc: 1.0000 - val_loss: 0.4964 - val_acc: 0.9167\n",
      "Epoch 11/20\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0038 - acc: 1.0000 - val_loss: 1.1260 - val_acc: 0.7667\n",
      "Epoch 12/20\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.7326 - val_acc: 0.8667\n",
      "Epoch 13/20\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 2.9422e-05 - acc: 1.0000 - val_loss: 0.1887 - val_acc: 0.9167\n",
      "Epoch 14/20\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 4.3705e-04 - acc: 1.0000 - val_loss: 0.8982 - val_acc: 0.8833\n",
      "Epoch 15/20\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.6254 - val_acc: 0.9333\n",
      "Epoch 16/20\n",
      "6/6 [==============================] - 1s 138ms/step - loss: 2.0207e-04 - acc: 1.0000 - val_loss: 0.5498 - val_acc: 0.9500\n",
      "Epoch 17/20\n",
      "6/6 [==============================] - 1s 141ms/step - loss: 2.9935e-04 - acc: 1.0000 - val_loss: 0.8144 - val_acc: 0.9000\n",
      "Epoch 18/20\n",
      "6/6 [==============================] - 1s 149ms/step - loss: 8.3584e-04 - acc: 1.0000 - val_loss: 0.4422 - val_acc: 0.9167\n",
      "Epoch 19/20\n",
      "6/6 [==============================] - 1s 145ms/step - loss: 4.4162e-05 - acc: 1.0000 - val_loss: 0.4828 - val_acc: 0.9167\n",
      "Epoch 20/20\n",
      "6/6 [==============================] - 1s 150ms/step - loss: 6.4812e-04 - acc: 1.0000 - val_loss: 1.4757 - val_acc: 0.8167\n"
     ]
    }
   ],
   "source": [
    "#모델 미세조정\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer = optimizers.RMSprop(lr=1e-5),metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(train_generator,\n",
    "                              steps_per_epoch=6,\n",
    "                              epochs=20,\n",
    "                              validation_data=validation_generator,\n",
    "                             validation_steps=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e0357675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1,len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs,acc,'bo',label='Training acc')\n",
    "plt.plot(epochs,val_acc,'b',label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,loss,'bo',label='training loss')\n",
    "plt.plot(epochs, val_loss,'b',label='validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e851358d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#지수평균이동으로 부드러운 그래프\n",
    "def smooth_curve(points, factor=0.8): \n",
    "    smoothed_points = []\n",
    "    for point in points: \n",
    "        if smoothed_points: # not empty\n",
    "            previous = smoothed_points[-1]\n",
    "            smoothed_points.append(previous * factor + point * (1 - factor)) \n",
    "        else:\n",
    "            smoothed_points.append(point) \n",
    "    return smoothed_points\n",
    "\n",
    "plt.plot(epochs,smooth_curve(acc),'bo',label='smoothed training acc')\n",
    "plt.plot(epochs,smooth_curve(val_acc),'b',label='smoothed validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,smooth_curve(loss),'bo',label='smoothed training loss')\n",
    "plt.plot(epochs,smooth_curve(val_loss),'b',label='smoothed validation loss')\n",
    "plt.title('Trainging and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "87c40e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20 images belonging to 2 classes.\n",
      "2/2 [==============================] - 0s 40ms/step - loss: 1.0972 - acc: 0.8000\n",
      "0.800000011920929\n",
      "Found 2 images belonging to 2 classes.\n",
      "1/1 [==============================] - 0s 68ms/step - loss: 3.3512 - acc: 0.0000e+00\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input, decode_predictions\n",
    "from tensorflow.keras.applications.resnet50 import decode_predictions\n",
    "import numpy as np\n",
    "base_dir = '/Users/jw1010110/AI/car-dataset/eval1'\n",
    "base_dir1 = '/Users/jw1010110/AI/car-dataset/evaluate'\n",
    "\n",
    "test_datagen =ImageDataGenerator(rescale=1./255)\n",
    "test_generator =test_datagen.flow_from_directory(base_dir1,target_size=(150,150),batch_size=10,class_mode='binary')\n",
    "test_loss, test_acc =model.evaluate(test_generator,steps=2)\n",
    "print(test_acc)\n",
    "\n",
    "\n",
    "pic1_datagen = ImageDataGenerator(rescale=1./255)\n",
    "pic1_generator = pic1_datagen.flow_from_directory(base_dir,target_size=(150,150),batch_size=1,class_mode='binary')\n",
    "pic1_loss, pic1_acc =model.evaluate(pic1_generator,steps=1)\n",
    "print(pic1_acc)\n",
    "\n",
    "# img = image.load_img(base_dir, target_size=(150,150))\n",
    "# x = image.img_to_array(img)\n",
    "# x = np.expand_dims(x,axis=0)\n",
    "\n",
    "# x = preprocess_input(x)\n",
    "\n",
    "# results = model.predict(x)\n",
    "# #print(results)\n",
    "# print(decode_predictions(results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5152c90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dc54dca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8530968f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32130048",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}