1108VGG.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# 实验一\n",
    "加载预定义VGG16网络参数，并在Kaggle猫/狗数据集上进行微调和测试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.加载keras模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Convolution2D, MaxPooling2D,ZeroPadding2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense, Input\n",
    "from keras.utils import to_categorical\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.applications.vgg16 import preprocess_input, VGG16\n",
    "from keras import backend as K\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载预训练权值\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义新加入的layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_width, img_height = 150, 150\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    input_shape = (3, img_width, img_height)\n",
    "else:\n",
    "    input_shape = (img_width, img_height, 3)\n",
    "    \n",
    "# LOAD VGG16\n",
    "input_tensor = Input(shape=input_shape)\n",
    "model = VGG16(weights='imagenet', \n",
    "                           include_top=False,\n",
    "                           input_tensor=input_tensor)\n",
    "\n",
    "top_model = Sequential()\n",
    "top_model.add(Flatten(input_shape=model.output_shape[1:]))\n",
    "top_model.add(Dense(256, activation='relu'))\n",
    "top_model.add(Dropout(0.5))\n",
    "top_model.add(Dense(2, activation='softmax'))\n",
    "#top_model.load_weights(top_model_weights_path)\n",
    "\n",
    "\n",
    "# CREATE AN \"REAL\" MODEL FROM VGG16\n",
    "# BY COPYING ALL THE LAYERS OF VGG16\n",
    "new_model = Sequential()\n",
    "for l in model.layers:\n",
    "    new_model.add(l)\n",
    "new_model.add(top_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置不需微调的layers的trainable属性\n",
    "并调用compile函数重新编译网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import SGD\n",
    "for layer in new_model.layers[:4]:\n",
    "    layer.trainable = False\n",
    "#compile the model with a SGD/momentum optimizer\n",
    "# and a very slow learning rate.\n",
    "new_model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=SGD(lr=1e-4, momentum=0.9),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义ImageDataGenerator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10835 images belonging to 2 classes.\n",
      "Found 4000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_dir = r'C:\\Users\\coffe\\Desktop\\dogs-vs-cats\\train'\n",
    "validation_data_dir = r'C:\\Users\\coffe\\Desktop\\dogs-vs-cats\\validation'\n",
    "nb_train_samples = 10835\n",
    "nb_validation_samples = 4000\n",
    "epochs = 1\n",
    "batch_size = 20\n",
    "\n",
    "\n",
    "# this is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1. / 255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "# this is the augmentation configuration we will use for testing:\n",
    "# only rescaling\n",
    "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    validation_data_dir,\n",
    "    target_size=(img_width, img_height),\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "541/541 [==============================] - 253s 467ms/step - loss: 0.3200 - accuracy: 0.8505 - val_loss: 0.1355 - val_accuracy: 0.9337\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1e0d921ff28>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=nb_train_samples // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=nb_validation_samples // batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用训练后模型预测图像\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.resize(cv2.imread(r'C:\\Users\\coffe\\Desktop\\dogs-vs-cats\\test\\1.jpg'), (img_width, img_height)).astype(np.float32)\n",
    "# img[:,:,0] -= 103.939\n",
    "# img[:,:,1] -= 116.779\n",
    "# img[:,:,2] -= 123.68\n",
    "#img = img.transpose((2,0,1))\n",
    "x = img_to_array(img)\n",
    "\n",
    "x = np.expand_dims(x, axis=0)\n",
    "\n",
    "#x = preprocess_input(x)\n",
    "\n",
    "score = new_model.predict(x)\n",
    "\n",
    "\n",
    "print(score)"
   ]
  }
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