TensorFlow/2_CNN/2_CNN_FeatureExtraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "000d7d23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hangyojeong/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.2\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-21 12:41:51.482690: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-12-21 12:41:51.483011: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58889256/58889256 [==============================] - 7s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bbcd251",
   "metadata": {},
   "source": [
    "매개변수\n",
    "\n",
    "- weights: 모델을 초기화할 가중치 체크포인트\n",
    "- include_top: 네트워크의 최상위 완전 연결 분류기 포함 여부\n",
    "- input_shape: 네트워크에 주입할 이미지 텐서의 크기, 이 파라미터는 선택 사항으로 값을 지정하지 않을시 네트워크가 어떤 크기의 입력도 처리할 수 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1adcdbf6",
   "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": "markdown",
   "id": "9edeb63a",
   "metadata": {},
   "source": [
    "최종 특성 맵의 크기는 (4, 4, 512)로 이 특성 위에 완전 연결 층을 얹을 예정\n",
    "\n",
    "두 가지 방법이 존재\n",
    "- 새로운 데이터셋에서 합성곱 기반 층을 실행하고 출력을 넘파이 배열로 디스크에 저장, 그 다음 이 데이터를 독립된 완전 연결 분류기에 입력으로 사용\n",
    "  - 모든 입력 이미지에 대해 합성곱 기반 층을 한 번만 실행하면 되기 때문에 빠르고 비용이 적게드는 방법 \n",
    "  - but, 데이터 증식을 사용할 수 없음\n",
    "- 준비된 모델 위에 Dense 층을 쌓아 확장. 그 다음 입력 데이터에서 end-to-end로 전체 모델을 실행\n",
    "  - 모델에 노출된 모든 입력 이미지가 매번 합성곱 기반층을 통과하기에 데이터 증식 사용 가능\n",
    "  - 첫번째 방식보단 비용이 많이 듬"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa483de",
   "metadata": {},
   "source": [
    "## 데이터 증식을 사용하지 않는 빠른 특성 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a1cc84f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-21 12:56:05.340190: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-12-21 12:56:05.409407: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 616ms/step\n",
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      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Found 1000 images belonging to 2 classes.\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
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      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "Found 1000 images belonging to 2 classes.\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
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      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 12ms/step\n",
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      "1/1 [==============================] - 0s 13ms/step\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "base_dir = './dogs-vs-cats/cats_and_dogs_small'\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "batch_size = 20\n",
    "\n",
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
    "    labels = np.zeros(shape=(sample_count))\n",
    "    generator = datagen.flow_from_directory(directory, \n",
    "                                            target_size=(150, 150), \n",
    "                                            batch_size=batch_size, \n",
    "                                            class_mode='binary')\n",
    "    \n",
    "    i = 0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        \n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            break  # 제너레이터는 루프 안에서 무한하게 데이터를 만들어 내므로 모든 이미지를 한 번씩 처리하고 나면 중지\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = extract_features(train_dir, 2000)\n",
    "validation_features, validation_labels = extract_features(validation_dir, 1000)\n",
    "test_features, test_labels = extract_features(test_dir, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c845a1f4",
   "metadata": {},
   "source": [
    "추출된 특성의 크기는 (samples, 4, 4, 512)\n",
    "\n",
    "완전 연결 분류기에 주입전 (samples, 8192)로 펼침"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a03d5430",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\n",
    "validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\n",
    "test_features = np.reshape(test_features, (1000, 4 * 4 * 512))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ae2a012",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hangyojeong/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/rmsprop.py:143: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n",
      "2022-12-21 13:01:50.273719: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 3s 10ms/step - loss: 0.5887 - acc: 0.6810 - val_loss: 0.4420 - val_acc: 0.8480\n",
      "Epoch 2/30\n",
      "  1/100 [..............................] - ETA: 0s - loss: 0.4979 - acc: 0.7000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-21 13:01:52.823815: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 1s 9ms/step - loss: 0.3957 - acc: 0.8390 - val_loss: 0.3531 - val_acc: 0.8680\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.3287 - acc: 0.8740 - val_loss: 0.3122 - val_acc: 0.8830\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.2879 - acc: 0.8865 - val_loss: 0.2949 - val_acc: 0.8790\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.2604 - acc: 0.9025 - val_loss: 0.2795 - val_acc: 0.8830\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.2416 - acc: 0.9105 - val_loss: 0.2699 - val_acc: 0.8880\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.2280 - acc: 0.9130 - val_loss: 0.2599 - val_acc: 0.8900\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.2071 - acc: 0.9280 - val_loss: 0.2661 - val_acc: 0.8900\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1987 - acc: 0.9290 - val_loss: 0.2494 - val_acc: 0.9020\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.1841 - acc: 0.9400 - val_loss: 0.2535 - val_acc: 0.8910\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1743 - acc: 0.9370 - val_loss: 0.2433 - val_acc: 0.9050\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1676 - acc: 0.9390 - val_loss: 0.2438 - val_acc: 0.8970\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1609 - acc: 0.9460 - val_loss: 0.2589 - val_acc: 0.8880\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.1490 - acc: 0.9520 - val_loss: 0.2431 - val_acc: 0.8950\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1437 - acc: 0.9530 - val_loss: 0.2395 - val_acc: 0.8990\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1386 - acc: 0.9545 - val_loss: 0.2389 - val_acc: 0.8980\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1290 - acc: 0.9590 - val_loss: 0.2556 - val_acc: 0.8920\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1246 - acc: 0.9570 - val_loss: 0.2364 - val_acc: 0.9020\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1172 - acc: 0.9620 - val_loss: 0.2457 - val_acc: 0.8950\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1183 - acc: 0.9595 - val_loss: 0.2609 - val_acc: 0.8910\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1114 - acc: 0.9635 - val_loss: 0.2449 - val_acc: 0.8980\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1033 - acc: 0.9680 - val_loss: 0.2404 - val_acc: 0.8970\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.1026 - acc: 0.9680 - val_loss: 0.2387 - val_acc: 0.9060\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0956 - acc: 0.9715 - val_loss: 0.2515 - val_acc: 0.8940\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0910 - acc: 0.9760 - val_loss: 0.2431 - val_acc: 0.8990\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0871 - acc: 0.9715 - val_loss: 0.2422 - val_acc: 0.8970\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0850 - acc: 0.9785 - val_loss: 0.2612 - val_acc: 0.8920\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0811 - acc: 0.9775 - val_loss: 0.2542 - val_acc: 0.8970\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0784 - acc: 0.9830 - val_loss: 0.2484 - val_acc: 0.8980\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 1s 8ms/step - loss: 0.0773 - acc: 0.9785 - val_loss: 0.2407 - val_acc: 0.9010\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(256, activation='relu', input_dim=4 * 4* 512))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=2e-5), loss='binary_crossentropy', metrics=['acc'])\n",
    "history = model.fit(train_features, train_labels, \n",
    "                    epochs=30, \n",
    "                    batch_size=20, \n",
    "                    validation_data=(validation_features, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "993e9015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa75db4",
   "metadata": {},
   "source": [
    "## 데이터 증식을 사용한 특성 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35292f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e21897c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\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",
      " dense_2 (Dense)             (None, 256)               2097408   \n",
      "                                                                 \n",
      " dense_3 (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": "markdown",
   "id": "8c846318",
   "metadata": {},
   "source": [
    "**모델을 컴파일하고 훈련하기 전 합성곱 기반 층을 동결해야만 함**\n",
    "\n",
    "하나 이상의 층을 동결한다는 것은 훈련하는 동안 가중치가 업데이트되지 않도록 막는다는 뜻\n",
    "\n",
    "케라스에서는 trainable 속성을 False로 설정하여 네트워크 동결이 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c1195a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv_base를 동결하기 전 훈련되는 가중치의 수:  30\n",
      "conv_base를 동결한 후 훈련되는 가중치의 수:  4\n"
     ]
    }
   ],
   "source": [
    "print('conv_base를 동결하기 전 훈련되는 가중치의 수: ', len(model.trainable_weights))\n",
    "conv_base.trainable = False\n",
    "print('conv_base를 동결한 후 훈련되는 가중치의 수: ', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e1066b",
   "metadata": {},
   "source": [
    "추가한 2개의 Dense 층 가중치만 훈련됨. 층마다 2개씩(weight, biase) 총 4개의 텐서가 훈련됨\n",
    "\n",
    "변경 사항을 적용하려면 모델을 컴파일해야하는데 컴파일 단계 후 trainable 속성을 변경하면 반드시 모델을 다시 컴파일 해야만 함 그렇지 않으면 변경 사항이 적용되지 않음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95f3a81c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hangyojeong/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/rmsprop.py:143: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n",
      "/var/folders/rz/96n_vjq17kg53f0rgc21grc40000gn/T/ipykernel_4374/2045135678.py:27: 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",
      "2022-12-21 13:20:58.096030: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-12-21 13:21:14.550462: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 - 24s - loss: 0.5427 - acc: 0.7390 - val_loss: 0.4919 - val_acc: 0.7460 - 24s/epoch - 244ms/step\n",
      "Epoch 2/30\n",
      "100/100 - 24s - loss: 0.4114 - acc: 0.8305 - val_loss: 0.3819 - val_acc: 0.8480 - 24s/epoch - 236ms/step\n",
      "Epoch 3/30\n",
      "100/100 - 24s - loss: 0.3561 - acc: 0.8535 - val_loss: 0.3426 - val_acc: 0.8500 - 24s/epoch - 235ms/step\n",
      "Epoch 4/30\n",
      "100/100 - 23s - loss: 0.3272 - acc: 0.8550 - val_loss: 0.3286 - val_acc: 0.8640 - 23s/epoch - 235ms/step\n",
      "Epoch 5/30\n",
      "100/100 - 23s - loss: 0.2984 - acc: 0.8795 - val_loss: 0.3216 - val_acc: 0.8580 - 23s/epoch - 235ms/step\n",
      "Epoch 6/30\n",
      "100/100 - 24s - loss: 0.2861 - acc: 0.8830 - val_loss: 0.2986 - val_acc: 0.8730 - 24s/epoch - 235ms/step\n",
      "Epoch 7/30\n",
      "100/100 - 24s - loss: 0.2802 - acc: 0.8845 - val_loss: 0.2927 - val_acc: 0.8660 - 24s/epoch - 236ms/step\n",
      "Epoch 8/30\n",
      "100/100 - 24s - loss: 0.2613 - acc: 0.8980 - val_loss: 0.2887 - val_acc: 0.8750 - 24s/epoch - 236ms/step\n",
      "Epoch 9/30\n",
      "100/100 - 24s - loss: 0.2603 - acc: 0.8995 - val_loss: 0.2947 - val_acc: 0.8790 - 24s/epoch - 236ms/step\n",
      "Epoch 10/30\n",
      "100/100 - 23s - loss: 0.2567 - acc: 0.8950 - val_loss: 0.2873 - val_acc: 0.8820 - 23s/epoch - 235ms/step\n",
      "Epoch 11/30\n",
      "100/100 - 23s - loss: 0.2497 - acc: 0.9000 - val_loss: 0.2890 - val_acc: 0.8780 - 23s/epoch - 234ms/step\n",
      "Epoch 12/30\n",
      "100/100 - 24s - loss: 0.2407 - acc: 0.8985 - val_loss: 0.2674 - val_acc: 0.8810 - 24s/epoch - 236ms/step\n",
      "Epoch 13/30\n",
      "100/100 - 24s - loss: 0.2351 - acc: 0.9115 - val_loss: 0.2874 - val_acc: 0.8840 - 24s/epoch - 236ms/step\n",
      "Epoch 14/30\n",
      "100/100 - 24s - loss: 0.2223 - acc: 0.9115 - val_loss: 0.2768 - val_acc: 0.8880 - 24s/epoch - 235ms/step\n",
      "Epoch 15/30\n",
      "100/100 - 24s - loss: 0.2349 - acc: 0.9025 - val_loss: 0.2634 - val_acc: 0.8930 - 24s/epoch - 236ms/step\n",
      "Epoch 16/30\n",
      "100/100 - 24s - loss: 0.2157 - acc: 0.9205 - val_loss: 0.2769 - val_acc: 0.8870 - 24s/epoch - 238ms/step\n",
      "Epoch 17/30\n",
      "100/100 - 24s - loss: 0.2167 - acc: 0.9215 - val_loss: 0.2721 - val_acc: 0.8870 - 24s/epoch - 237ms/step\n",
      "Epoch 18/30\n",
      "100/100 - 24s - loss: 0.2234 - acc: 0.9160 - val_loss: 0.2689 - val_acc: 0.8890 - 24s/epoch - 237ms/step\n",
      "Epoch 19/30\n",
      "100/100 - 23s - loss: 0.2183 - acc: 0.9100 - val_loss: 0.2670 - val_acc: 0.8770 - 23s/epoch - 235ms/step\n",
      "Epoch 20/30\n",
      "100/100 - 24s - loss: 0.2092 - acc: 0.9175 - val_loss: 0.2559 - val_acc: 0.8910 - 24s/epoch - 235ms/step\n",
      "Epoch 21/30\n",
      "100/100 - 24s - loss: 0.2060 - acc: 0.9160 - val_loss: 0.2726 - val_acc: 0.8920 - 24s/epoch - 235ms/step\n",
      "Epoch 22/30\n",
      "100/100 - 23s - loss: 0.2060 - acc: 0.9205 - val_loss: 0.2719 - val_acc: 0.8870 - 23s/epoch - 235ms/step\n",
      "Epoch 23/30\n",
      "100/100 - 24s - loss: 0.2038 - acc: 0.9165 - val_loss: 0.2732 - val_acc: 0.8760 - 24s/epoch - 235ms/step\n",
      "Epoch 24/30\n",
      "100/100 - 24s - loss: 0.2023 - acc: 0.9185 - val_loss: 0.2767 - val_acc: 0.8830 - 24s/epoch - 235ms/step\n",
      "Epoch 25/30\n",
      "100/100 - 24s - loss: 0.1912 - acc: 0.9255 - val_loss: 0.2597 - val_acc: 0.8890 - 24s/epoch - 236ms/step\n",
      "Epoch 26/30\n",
      "100/100 - 24s - loss: 0.1953 - acc: 0.9180 - val_loss: 0.2632 - val_acc: 0.8840 - 24s/epoch - 236ms/step\n",
      "Epoch 27/30\n",
      "100/100 - 24s - loss: 0.1989 - acc: 0.9230 - val_loss: 0.2743 - val_acc: 0.8860 - 24s/epoch - 235ms/step\n",
      "Epoch 28/30\n",
      "100/100 - 24s - loss: 0.1928 - acc: 0.9250 - val_loss: 0.2644 - val_acc: 0.8880 - 24s/epoch - 235ms/step\n",
      "Epoch 29/30\n",
      "100/100 - 23s - loss: 0.1933 - acc: 0.9255 - val_loss: 0.2753 - val_acc: 0.8910 - 23s/epoch - 235ms/step\n",
      "Epoch 30/30\n",
      "100/100 - 24s - loss: 0.1847 - acc: 0.9270 - val_loss: 0.2587 - val_acc: 0.8900 - 24s/epoch - 235ms/step\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import optimizers\n",
    "\n",
    "train_datagen = ImageDataGenerator(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",
    "# 검증 데이터는 증식되어선 안됨\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, \n",
    "                                                    target_size=(150,150), \n",
    "                                                    batch_size=20, \n",
    "                                                    class_mode='binary')\n",
    "validation_generator = train_datagen.flow_from_directory(validation_dir, \n",
    "                                                    target_size=(150,150), \n",
    "                                                    batch_size=20, \n",
    "                                                    class_mode='binary')\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=2e-5), metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(train_generator, \n",
    "                              steps_per_epoch=100, \n",
    "                              epochs=30, \n",
    "                              validation_data=validation_generator, \n",
    "                              validation_steps=50, \n",
    "                              verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ccabdf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84960767",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}