准备工作：

一.安装所需的python库

1️⃣ tensorflow库
打开anaconda promp，输入pip install tensorflow，安装tensorflow库（推荐使用科学上网下载）

2️⃣keras库
同在是在anaconda promp里，输入pip install keras，安装keras库

3️⃣测试
最后在jupyter中输入以下代码看看是否安装成功

import keras
keras.__version__

出现版本号说明安装成功了

二.所需文件

1️⃣ 猫狗数据集

实验内容：

一.猫狗数据集的两阶段分类实验

1.创建三个子集的新数据集

1️⃣ 根据猫狗数据集的内容，我们得到了三个新数据集，一个包含每个类1000个样本的训练集，一个包含每个类500个样本的验证集和一个包含每个类500个样本的测试集，可以通过以下代码实现：

import os, shutil
# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = 'E:\\dogs-vs-cats\\train\\train'
# The directory where we will
# store our smaller dataset
base_dir = 'E:\\dogs-vs-cats1'
os.mkdir(base_dir)
# Directories for our training,
# validation and test splits
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)
# Directory with our training cat pictures
train_cats_dir = os.path.join(train_dir, 'cats')
os.mkdir(train_cats_dir)
# Directory with our training dog pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')
os.mkdir(train_dogs_dir)
# Directory with our validation cat pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')
os.mkdir(validation_cats_dir)
# Directory with our validation dog pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
os.mkdir(validation_dogs_dir)
# Directory with our validation cat pictures
test_cats_dir = os.path.join(test_dir, 'cats')
os.mkdir(test_cats_dir)
# Directory with our validation dog pictures
test_dogs_dir = os.path.join(test_dir, 'dogs')
os.mkdir(test_dogs_dir)
# Copy first 1000 cat images to train_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(train_cats_dir, fname)shutil.copyfile(src, dst)
# Copy next 500 cat images to validation_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(validation_cats_dir, fname)shutil.copyfile(src, dst)
# Copy next 500 cat images to test_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(test_cats_dir, fname)shutil.copyfile(src, dst)    
# Copy first 1000 dog images to train_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(train_dogs_dir, fname)shutil.copyfile(src, dst)    
# Copy next 500 dog images to validation_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(validation_dogs_dir, fname)shutil.copyfile(src, dst)    
# Copy next 500 dog images to test_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(test_dogs_dir, fname)shutil.copyfile(src, dst)

2️⃣打印数据

print('total training cat images:', len(os.listdir(train_cats_dir)))
print('total training dog images:', len(os.listdir(train_dogs_dir)))
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
print('total test cat images:', len(os.listdir(test_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))

2.构建网络

1️⃣ 构建小型卷积网络

from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

2️⃣ 了解征图的尺寸是如何随着每一层变化的

model.summary()

3️⃣ 使用RMSprop优化器

from keras import optimizersmodel.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])

3、数据预处理

1️⃣ 数据预处理

from keras.preprocessing.image import ImageDataGenerator# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(# This is the target directorytrain_dir,# All images will be resized to 150x150target_size=(150, 150),batch_size=20,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=20,class_mode='binary')

2️⃣ 查看生成器输出

for data_batch, labels_batch in train_generator:print('data batch shape:', data_batch.shape)print('labels batch shape:', labels_batch.shape)break

3️⃣ 使用生成器使我们的模型适合于数据

history = model.fit_generator(train_generator,steps_per_epoch=100,epochs=30,validation_data=validation_generator,validation_steps=50)

4️⃣ 保存我们的模型

model.save('cats_and_dogs_small_1.h5')

5️⃣ 在训练和验证数据上绘制模型的损失和准确性

import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

4、使用数据增强

1️⃣ 图像数据生成器增强数据

datagen = ImageDataGenerator(rotation_range=40,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True,fill_mode='nearest')

2️⃣ 查看增强后的图像

# This is module with image preprocessing utilities
from keras.preprocessing import image
fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]
# We pick one image to "augment"
img_path = fnames[3]
# Read the image and resize it
img = image.load_img(img_path, target_size=(150, 150))
# Convert it to a Numpy array with shape (150, 150, 3)
x = image.img_to_array(img)
# Reshape it to (1, 150, 150, 3)
x = x.reshape((1,) + x.shape)
# The .flow() command below generates batches of randomly transformed images.
# It will loop indefinitely, so we need to `break` the loop at some point!
i = 0
for batch in datagen.flow(x, batch_size=1):plt.figure(i)imgplot = plt.imshow(image.array_to_img(batch[0]))i += 1if i % 4 == 0:break
plt.show()

3️⃣ 增加一个Dropout层，进一步对抗过拟合

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])

4️⃣ 用数据增强和退出来训练我们的网络

train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=40,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True,)
# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(# This is the target directorytrain_dir,# All images will be resized to 150x150target_size=(150, 150),batch_size=32,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')
validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=32,class_mode='binary')
history = model.fit_generator(train_generator,steps_per_epoch=100,epochs=100,validation_data=validation_generator,validation_steps=50)

5️⃣ 保存模型，将在convnet可视化部分使用它

model.save('cats_and_dogs_small_2.h5')

6️⃣ 画出结果

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

二、提高猫狗图像分类模型精度

1、构建卷积网络

1️⃣ 网络结构

from keras import layers
from keras import models
from keras import optimizers
model = models.Sequential()
#输入图片大小是150*150 3表示图片像素用(R,G,B)表示
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(150 , 150, 3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(128, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(128, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])
model.summary()

2.构建VGG16网络

（建议科学上网）

from keras.applications import VGG16
conv_base = VGG16(weights = 'imagenet', include_top = False, input_shape=(150, 150, 3))
conv_base.summary()

3. 将猫狗数据集传递给神经网络

1️⃣ 将之前新建的猫狗数据集传递给神经网络并抽取出隐藏信息

import os 
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
base_dir = 'E:\\dogs-vs-cats1'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
datagen = ImageDataGenerator(rescale = 1. / 255)
batch_size = 20
def extract_features(directory, sample_count):features = np.zeros(shape = (sample_count, 4, 4, 512))labels = np.zeros(shape = (sample_count))generator = datagen.flow_from_directory(directory, target_size = (150, 150), batch_size = batch_size,class_mode = 'binary')i = 0for inputs_batch, labels_batch in generator:#把图片输入VGG16卷积层，让它把图片信息抽取出来features_batch = conv_base.predict(inputs_batch)#feature_batch 是 4*4*512结构features[i * batch_size : (i + 1)*batch_size] = features_batchlabels[i * batch_size : (i+1)*batch_size] = labels_batchi += 1if i * batch_size >= sample_count :#for in 在generator上的循环是无止境的，因此我们必须主动break掉breakreturn features , labels
#extract_features 返回数据格式为(samples, 4, 4, 512)
train_features, train_labels = extract_features(train_dir, 2000)
validation_features, validation_labels = extract_features(validation_dir, 1000)
test_features, test_labels = extract_features(test_dir, 1000)

2️⃣ 把抽取的特征输入到我们自己的神经层中进行分类

train_features = np.reshape(train_features, (2000, 4 * 4 * 512))
validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))
test_features = np.reshape(test_features, (1000, 4 * 4* 512))
from keras import models
from keras import layers
from keras import optimizers
#构造我们自己的网络层对输出数据进行分类
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim = 4 * 4 * 512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation = 'sigmoid'))
model.compile(optimizer=optimizers.RMSprop(lr = 2e-5), loss = 'binary_crossentropy', metrics = ['acc'])
history = model.fit(train_features, train_labels, epochs = 30, batch_size = 20, validation_data = (validation_features, validation_labels))

3️⃣ 画出训练结果和校验结果

import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label = 'Train_acc')
plt.plot(epochs, val_acc, 'b', label = 'Validation acc')
plt.title('Trainning and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label = 'Training loss')
plt.plot(epochs, val_loss, 'b', label = 'Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

4、参数调优

model = models.Sequential()
#将VGG16的卷积层直接添加到我们的网络
model.add(conv_base)
#添加我们自己的网络层
model.add(layers.Flatten())
model.add(layers.Dense(256, activation = 'relu'))
model.add(layers.Dense(1, activation = 'sigmoid'))
model.summary()

把它最高三层与我们自己的网络层结合在一起训练，同时冻结最低四层

conv_base.trainable = True
set_trainable = False
#一旦读取到'block5_conv1'时，意味着来到卷积网络的最高三层
#可以使用conv_base.summary()来查看卷积层的信息
for layer in conv_base.layers:if layer.name == 'block5_conv1':set_trainable = Trueif set_trainable:#当trainable == True 意味着该网络层可以更改，要不然该网络层会被冻结，不能修改layer.trainable = Trueelse:layer.trainable = False

数据传入网络，训练给定的卷积层和我们自己的网络层

#把图片数据读取进来
test_datagen = ImageDataGenerator(rescale = 1. / 255)
train_generator = test_datagen.flow_from_directory(train_dir, target_size = (150, 150), batch_size = 20,class_mode = 'binary')
validation_generator = test_datagen.flow_from_directory(validation_dir, target_size = (150,150),batch_size = 20,class_mode = 'binary')
model.compile(loss = 'binary_crossentropy', optimizer = optimizers.RMSprop(2e-5),metrics = ['acc'])history = model.fit_generator(train_generator, steps_per_epoch = 100, epochs = 30, validation_data = validation_generator,validation_steps = 50)

最后保存

model.save('cats_and_dogs_small_3.h5')

本次实验结束！