接着我上一篇博客，https://blog.csdn.net/fanzonghao/article/details/81149153。

在上一篇基础上对数据集进行数据增强。函数如下：

"""
查看图像增强是否发生作用
"""
def see_pic_aug():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,fill_mode='nearest')# 从训练集例返回图片的地址train_dir, validation_dir, cat_img_files, dog_img_files = data_read.read_data()# 返回随机一张图片的地址img_path = random.choice(cat_img_files + dog_img_files)img = load_img(img_path, target_size=(150, 150))x = img_to_array(img)# 变成（1,150,150,3）x = x.reshape((1,) + x.shape)i = 0for batch in train_datagen.flow(x, batch_size=1):plt.figure(i)plt.imshow(array_to_img(batch[0]))i += 1if i % 5 == 0:breakplt.show()

打印5张查看：

确实发生了一些改变。

下面就用数据增强的样本训练模型，代码如下：

import numpy as np
import matplotlib.pyplot as plt
import random
import data_read
import tensorflow as tf
from keras.models import Model
from keras import  layers,optimizers
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator,img_to_array,load_img,array_to_img
"""
获得所需求的图片--进行了图像增强
"""
def data_deal_overfit():# 获取数据的路径train_dir, validation_dir, next_cat_pix, next_dog_pix = data_read.read_data()#图像增强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,fill_mode='nearest')test_datagen=ImageDataGenerator(rescale=1./255)#从文件夹获取所需要求的图片train_generator=train_datagen.flow_from_directory(train_dir,target_size=(150,150),batch_size=20,class_mode='binary')test_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=20,class_mode='binary')return train_generator,test_generator
"""
定义模型并加入了dropout
"""
def define_model():
#定义TF backend session# tf_config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))# K.set_session(tf.Session(config=tf_config))#卷积过程 三层卷积img_input=layers.Input(shape=(150,150,3))x=layers.Conv2D(filters=16,kernel_size=(3,3),activation='relu')(img_input)print('第一次卷积尺寸={}'.format(x.shape))x=layers.MaxPooling2D(strides=(2,2))(x)print('第一次池化尺寸={}'.format(x.shape))x=layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu')(x)print('第二次卷积尺寸={}'.format(x.shape))x=layers.MaxPooling2D(strides=(2,2))(x)print('第二次池化尺寸={}'.format(x.shape))x=layers.Conv2D(filters=64,kernel_size=(3,3),activation='relu')(x)print('第三次卷积尺寸={}'.format(x.shape))x=layers.MaxPooling2D(strides=(2,2))(x)print('第三次池化尺寸={}'.format(x.shape))#全连接层x=layers.Flatten()(x)x=layers.Dense(512,activation='relu')(x)output=layers.Dense(1,activation='sigmoid')(x)model=Model(inputs=img_input,outputs=output,name='CAT_DOG_Model')return img_input,model
"""
训练模型
"""
def train_model():#构建网络模型img_input,model=define_model()#编译模型model.compile(optimizer=optimizers.RMSprop(lr=0.001),loss='binary_crossentropy',metrics=['accuracy'])train_generator,test_generator=data_deal_overfit()#verbose：日志显示，0为不在标准输出流输出日志信息，1为输出进度条记录，2为每个epoch输出一行记录#训练模型 返回history包含各种精度和损失history=model.fit_generator(train_generator,steps_per_epoch=100,#2000 images=batch_szie*stepsepochs=1,validation_data=test_generator,validation_steps=50,#1000=20*50verbose=2)# 模型参数个数model.summary()#精度acc=history.history['acc']val_acc=history.history['val_acc']#损失loss=history.history['loss']val_loss=history.history['val_loss']#epochs的数量epochs=range(len(acc))plt.plot(epochs,acc)plt.plot(epochs, val_acc)plt.title('training and validation accuracy')plt.figure()plt.plot(epochs, loss)plt.plot(epochs, val_loss)plt.title('training and validation loss')plt.show()#测试图片# 从训练集例返回图片的地址train_dir, validation_dir, cat_img_files, dog_img_files = data_read.read_data()# 返回随机一张图片的地址img_path = random.choice(cat_img_files + dog_img_files)img = load_img(img_path, target_size=(150, 150))plt.imshow(img)plt.show()x = img_to_array(img)# 变成（1,150,150,3）x = x.reshape((1,) + x.shape)y_pred=model.predict(x)print('预测值y={}'.format(y_pred))#图形化形式查看卷积层生成的图
def visualize_model():img_input,model=define_model()# print(model.layers)#存储每一层的tensor的shape 类型等successive_outputs=[layer.output for layer in model.layers]# print(successive_outputs)visualization_model=Model(img_input,successive_outputs)#从训练集例返回图片的地址train_dir, validation_dir, cat_img_files,dog_img_files = data_read.read_data()#返回随机一张图片的地址img_path=random.choice(cat_img_files+dog_img_files)img=load_img(img_path,target_size=(150,150))x=img_to_array(img)#print(x.shape)#变成（1,150,150,3）x=x.reshape((1,)+x.shape)x/=255#(samples,150,150,3) 存储10层的信息successive_feature_maps=visualization_model.predict(x)# print(len(successive_feature_maps))# for i in range(len(successive_feature_maps)):#     print(successive_feature_maps[i].shape)layer_names=[layer.name for layer in model.layers]#zip 打包成一个个元组以列表形式返回[（）,()]#并且遍历元组里的内容for layer_name,feature_map in zip(layer_names,successive_feature_maps):if len(feature_map.shape)==4:#只查看卷积层n_features=feature_map.shape[-1]#(1,150,150,3)取3 取出深度size=feature_map.shape[1]##(1,150,150,3)取150  尺寸大小display_grid=np.zeros((size,size*n_features))for i in range(n_features):x=feature_map[0,:,:,i]x-=x.mean()x/=x.std()x*=64x+=128#限定x的值大小 小于0 则为0  大于255则为255x=np.clip(x,0,255).astype('uint8')display_grid[:,i*size:(i+1)*size]=x#显示scale=64./n_featuresplt.figure(figsize=(scale*n_features,scale))plt.title(layer_name)plt.grid(False)plt.imshow(display_grid,aspect='auto',cmap='Oranges')plt.show()
"""
查看图像增强是否发生作用
"""
def see_pic_aug():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,fill_mode='nearest')# 从训练集例返回图片的地址train_dir, validation_dir, cat_img_files, dog_img_files = data_read.read_data()# 返回随机一张图片的地址img_path = random.choice(cat_img_files + dog_img_files)img = load_img(img_path, target_size=(150, 150))x = img_to_array(img)# 变成（1,150,150,3）x = x.reshape((1,) + x.shape)i = 0#32个训练样本for batch in train_datagen.flow(x, batch_size=32):plt.figure(i)plt.imshow(array_to_img(batch[0]))i += 1if i % 5 == 0:breakplt.show()
if __name__ == '__main__':# see_pic_aug()train_model()#visualize_model()# 像素缩小到0～1

迭代100次结果：可看出相比上一篇文章，精度是稳定的，损失值也几乎是稳定的，数据增强还是起了防止过拟合的作用。

 

同样可视化卷积层：