TensorFlow/2_CNN/3_CNN_FineTuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "caed9140",
   "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 19:44:27.375003: 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 19:44:27.375269: 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"
     ]
    }
   ],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2c21ae8",
   "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": "d15580eb",
   "metadata": {},
   "source": [
    "마지막 3개의 합성곱 층을 미세 조정\n",
    "\n",
    "block5_conv1, block5_conv2, block5_conv3 층 학습 대상 나머지는 모든 층은 동결\n",
    "\n",
    "더 많은 층을 미세 조정 하지 않는 이유?\n",
    "- 합성곱 기반 층에 있는 하위 층들은 좀 더 일반적이고 재사용 가능한 특성들을 인코딩, 반면 상위 층은 좀 더 특화된 특성을 인코딩.\n",
    "    - 새로운 문제에 재활용하도록 수정이 필요한 것은 구체적 특성이므로 이들을 미세 조정하는 것이 유리\n",
    "    - 하위 층으로 갈수록 미세 조정에 대한 효과가 감소\n",
    "- 훈련해야 할 파라미터가 많을 수록 과대적합의 위험이 커짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ca166fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 5,
   "id": "55cdc932",
   "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": 7,
   "id": "939430c7",
   "metadata": {},
   "outputs": [],
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a74838f6",
   "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_973/2639404750.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 19:56:20.700556: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-12-21 19:56:20.969862: 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 [==============================] - ETA: 0s - loss: 0.4596 - acc: 0.7950"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-21 19:56:41.737471: 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 [==============================] - 29s 277ms/step - loss: 0.4596 - acc: 0.7950 - val_loss: 0.3266 - val_acc: 0.8760\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.2829 - acc: 0.8855 - val_loss: 0.2555 - val_acc: 0.8880\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.2288 - acc: 0.9070 - val_loss: 0.2491 - val_acc: 0.8880\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 27s 272ms/step - loss: 0.1970 - acc: 0.9220 - val_loss: 0.2764 - val_acc: 0.8770\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 27s 272ms/step - loss: 0.1781 - acc: 0.9350 - val_loss: 0.2077 - val_acc: 0.9140\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.1655 - acc: 0.9280 - val_loss: 0.2056 - val_acc: 0.9190\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.1578 - acc: 0.9425 - val_loss: 0.1926 - val_acc: 0.9200\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.1261 - acc: 0.9535 - val_loss: 0.3770 - val_acc: 0.8520\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.1136 - acc: 0.9535 - val_loss: 0.2078 - val_acc: 0.9180\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.1163 - acc: 0.9555 - val_loss: 0.2136 - val_acc: 0.9200\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.0938 - acc: 0.9650 - val_loss: 0.2200 - val_acc: 0.9060\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.0862 - acc: 0.9710 - val_loss: 0.2212 - val_acc: 0.9190\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0846 - acc: 0.9700 - val_loss: 0.2046 - val_acc: 0.9140\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.0791 - acc: 0.9730 - val_loss: 0.1965 - val_acc: 0.9250\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 27s 275ms/step - loss: 0.0632 - acc: 0.9775 - val_loss: 0.2056 - val_acc: 0.9250\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 28s 278ms/step - loss: 0.0649 - acc: 0.9740 - val_loss: 0.2094 - val_acc: 0.9150\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 27s 275ms/step - loss: 0.0540 - acc: 0.9825 - val_loss: 0.2053 - val_acc: 0.9270\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.0539 - acc: 0.9800 - val_loss: 0.2409 - val_acc: 0.9100\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 28s 277ms/step - loss: 0.0515 - acc: 0.9840 - val_loss: 0.1887 - val_acc: 0.9280\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 28s 276ms/step - loss: 0.0463 - acc: 0.9865 - val_loss: 0.2869 - val_acc: 0.9120\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 27s 273ms/step - loss: 0.0432 - acc: 0.9860 - val_loss: 0.2833 - val_acc: 0.9020\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0386 - acc: 0.9880 - val_loss: 0.2534 - val_acc: 0.9010\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0364 - acc: 0.9850 - val_loss: 0.2430 - val_acc: 0.9150\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0355 - acc: 0.9880 - val_loss: 0.2210 - val_acc: 0.9250\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0363 - acc: 0.9900 - val_loss: 0.4663 - val_acc: 0.8670\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0217 - acc: 0.9935 - val_loss: 0.2287 - val_acc: 0.9260\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0248 - acc: 0.9930 - val_loss: 0.2502 - val_acc: 0.9180\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0238 - acc: 0.9935 - val_loss: 0.2329 - val_acc: 0.9210\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0249 - acc: 0.9890 - val_loss: 0.2032 - val_acc: 0.9270\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 27s 274ms/step - loss: 0.0213 - acc: 0.9910 - val_loss: 0.2490 - val_acc: 0.9300\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=1e-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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a20ac2d7",
   "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": 10,
   "id": "371c86e5",
   "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": [
    "def smooth_curve(points, factor=0.8):\n",
    "    smoothed_points = []\n",
    "    for point in points:\n",
    "        if smoothed_points:\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.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35dc030f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rz/96n_vjq17kg53f0rgc21grc40000gn/T/ipykernel_973/2269266159.py:6: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acc: 0.9420000314712524\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(test_dir, \n",
    "                                                  target_size=(150,150), \n",
    "                                                  batch_size=20, \n",
    "                                                  class_mode='binary')\n",
    "\n",
    "test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\n",
    "print('test acc:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36db45ad",
   "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
}