实战 迁移学习 VGG19、ResNet50、InceptionV3 实践 猫狗大战 问题

  参考博客:::https://blog.csdn.net/pengdali/article/details/79050662     2018年01月13日 12:52:14 pengdali 阅读数 10417  

一、实践流程

1、数据预处理

主要是对训练数据进行随机偏移、转动等变换图像处理，这样可以尽可能让训练数据多样化

另外处理数据方式采用分批无序读取的形式，避免了数据按目录排序训练

 

1.   #数据准备
2.   def DataGen(self, dir_path, img_row, img_col, batch_size, is_train):
3.   if is_train:
4.   datagen = ImageDataGenerator(rescale=1./255,
5.   zoom_range=0.25, rotation_range=15.,
6.   channel_shift_range=25., width_shift_range=0.02, height_shift_range=0.02,
7.   horizontal_flip=True, fill_mode='constant')
8.   else:
9.   datagen = ImageDataGenerator(rescale=1./255)
10.    
11.   generator = datagen.flow_from_directory(
12.   dir_path, target_size=(img_row, img_col),
13.   batch_size=batch_size,
14.   shuffle=is_train)
15.    
16.   return generator

2、载入现有模型

 

这个部分是核心工作，目的是使用ImageNet训练出的权重来做我们的特征提取器，注意这里后面的分类层去掉

 

1.   base_model = InceptionV3(weights='imagenet', include_top=False, pooling=None,
2.   input_shape=(img_rows, img_cols, color),
3.   classes=nb_classes)

然后是冻结这些层，因为是训练好的

 

1.   for layer in base_model.layers:
2.   layer.trainable = False

而分类部分，需要我们根据现有需求来新定义的，这里可以根据实际情况自己进行调整，比如这样

1.   x = base_model.output
2.   # 添加自己的全链接分类层
3.   x = GlobalAveragePooling2D()(x)
4.   x = Dense(1024, activation='relu')(x)
5.   predictions = Dense(nb_classes, activation='softmax')(x)

或者

 

1.   x = base_model.output
2.   #添加自己的全链接分类层
3.   x = Flatten()(x)
4.   predictions = Dense(nb_classes, activation='softmax')(x)

3、训练模型

这里我们用fit_generator函数，它可以避免了一次性加载大量的数据，并且生成器与模型将并行执行以提高效率。比如可以在CPU上进行实时的数据提升，同时在GPU上进行模型训练

 

1.   history_ft = model.fit_generator(
2.   train_generator,
3.   steps_per_epoch=steps_per_epoch,
4.   epochs=epochs,
5.   validation_data=validation_generator,
6.   validation_steps=validation_steps)

二、猫狗大战数据集

 

训练数据540M，测试数据270M，大家可以去官网下载

https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data

下载后把数据分成dog和cat两个目录来存放

三、训练

训练的时候会自动去下权值，比如vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5，但是如果我们已经下载好了的话，可以改源代码，让他直接读取我们的下载好的权值，比如在resnet50.py中

1、VGG19

vgg19的深度有26层，参数达到了549M，原模型最后有3个全连接层做分类器所以我还是加了一个1024的全连接层，训练10轮的情况达到了89%

2、ResNet50

ResNet50的深度达到了168层，但是参数只有99M，分类模型我就简单点，一层直接分类，训练10轮的达到了96%的准确率

3、inception_v3

InceptionV3的深度159层，参数92M，训练10轮的结果

这是一层直接分类的结果

这是加了一个512全连接的，大家可以随意调整测试

 

四、完整的代码

 

1.   # -*- coding: utf-8 -*-
2.   import os
3.   from keras.utils import plot_model
4.   from keras.applications.resnet50 import ResNet50
5.   from keras.applications.vgg19 import VGG19
6.   from keras.applications.inception_v3 import InceptionV3
7.   from keras.layers import Dense,Flatten,GlobalAveragePooling2D
8.   from keras.models import Model,load_model
9.   from keras.optimizers import SGD
10.   from keras.preprocessing.image import ImageDataGenerator
11.   import matplotlib.pyplot as plt
12.    
13.   class PowerTransferMode:
14.   #数据准备
15.   def DataGen(self, dir_path, img_row, img_col, batch_size, is_train):
16.   if is_train:
17.   datagen = ImageDataGenerator(rescale=1./255,
18.   zoom_range=0.25, rotation_range=15.,
19.   channel_shift_range=25., width_shift_range=0.02, height_shift_range=0.02,
20.   horizontal_flip=True, fill_mode='constant')
21.   else:
22.   datagen = ImageDataGenerator(rescale=1./255)
23.    
24.   generator = datagen.flow_from_directory(
25.   dir_path, target_size=(img_row, img_col),
26.   batch_size=batch_size,
27.   #class_mode='binary',
28.   shuffle=is_train)
29.    
30.   return generator
31.    
32.   #ResNet模型
33.   def ResNet50_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=2, img_rows=197, img_cols=197, RGB=True, is_plot_model=False):
34.   color = 3 if RGB else 1
35.   base_model = ResNet50(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color),
36.   classes=nb_classes)
37.    
38.   #冻结base_model所有层，这样就可以正确获得bottleneck特征
39.   for layer in base_model.layers:
40.   layer.trainable = False
41.    
42.   x = base_model.output
43.   #添加自己的全链接分类层
44.   x = Flatten()(x)
45.   #x = GlobalAveragePooling2D()(x)
46.   #x = Dense(1024, activation='relu')(x)
47.   predictions = Dense(nb_classes, activation='softmax')(x)
48.    
49.   #训练模型
50.   model = Model(inputs=base_model.input, outputs=predictions)
51.   sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
52.   model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
53.    
54.   #绘制模型
55.   if is_plot_model:
56.   plot_model(model, to_file='resnet50_model.png',show_shapes=True)
57.    
58.   return model
59.    
60.    
61.   #VGG模型
62.   def VGG19_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=2, img_rows=197, img_cols=197, RGB=True, is_plot_model=False):
63.   color = 3 if RGB else 1
64.   base_model = VGG19(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color),
65.   classes=nb_classes)
66.    
67.   #冻结base_model所有层，这样就可以正确获得bottleneck特征
68.   for layer in base_model.layers:
69.   layer.trainable = False
70.    
71.   x = base_model.output
72.   #添加自己的全链接分类层
73.   x = GlobalAveragePooling2D()(x)
74.   x = Dense(1024, activation='relu')(x)
75.   predictions = Dense(nb_classes, activation='softmax')(x)
76.    
77.   #训练模型
78.   model = Model(inputs=base_model.input, outputs=predictions)
79.   sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
80.   model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
81.    
82.   # 绘图
83.   if is_plot_model:
84.   plot_model(model, to_file='vgg19_model.png',show_shapes=True)
85.    
86.   return model
87.    
88.   # InceptionV3模型
89.   def InceptionV3_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=2, img_rows=197, img_cols=197, RGB=True,
90.   is_plot_model=False):
91.   color = 3 if RGB else 1
92.   base_model = InceptionV3(weights='imagenet', include_top=False, pooling=None,
93.   input_shape=(img_rows, img_cols, color),
94.   classes=nb_classes)
95.    
96.   # 冻结base_model所有层，这样就可以正确获得bottleneck特征
97.   for layer in base_model.layers:
98.   layer.trainable = False
99.    
100.   x = base_model.output
101.   # 添加自己的全链接分类层
102.   x = GlobalAveragePooling2D()(x)
103.   x = Dense(1024, activation='relu')(x)
104.   predictions = Dense(nb_classes, activation='softmax')(x)
105.    
106.   # 训练模型
107.   model = Model(inputs=base_model.input, outputs=predictions)
108.   sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
109.   model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
110.    
111.   # 绘图
112.   if is_plot_model:
113.   plot_model(model, to_file='inception_v3_model.png', show_shapes=True)
114.    
115.   return model
116.    
117.   #训练模型
118.   def train_model(self, model, epochs, train_generator, steps_per_epoch, validation_generator, validation_steps, model_url, is_load_model=False):
119.   # 载入模型
120.   if is_load_model and os.path.exists(model_url):
121.   model = load_model(model_url)
122.    
123.   history_ft = model.fit_generator(
124.   train_generator,
125.   steps_per_epoch=steps_per_epoch,
126.   epochs=epochs,
127.   validation_data=validation_generator,
128.   validation_steps=validation_steps)
129.   # 模型保存
130.   model.save(model_url,overwrite=True)
131.   return history_ft
132.    
133.   # 画图
134.   def plot_training(self, history):
135.   acc = history.history['acc']
136.   val_acc = history.history['val_acc']
137.   loss = history.history['loss']
138.   val_loss = history.history['val_loss']
139.   epochs = range(len(acc))
140.   plt.plot(epochs, acc, 'b-')
141.   plt.plot(epochs, val_acc, 'r')
142.   plt.title('Training and validation accuracy')
143.   plt.figure()
144.   plt.plot(epochs, loss, 'b-')
145.   plt.plot(epochs, val_loss, 'r-')
146.   plt.title('Training and validation loss')
147.   plt.show()
148.    
149.    
150.   if __name__ == '__main__':
151.   image_size = 197
152.   batch_size = 32
153.    
154.   transfer = PowerTransferMode()
155.    
156.   #得到数据
157.   train_generator = transfer.DataGen('data/cat_dog_Dataset/train', image_size, image_size, batch_size, True)
158.   validation_generator = transfer.DataGen('data/cat_dog_Dataset/test', image_size, image_size, batch_size, False)
159.    
160.   #VGG19
161.   #model = transfer.VGG19_model(nb_classes=2, img_rows=image_size, img_cols=image_size, is_plot_model=False)
162.   #history_ft = transfer.train_model(model, 10, train_generator, 600, validation_generator, 60, 'vgg19_model_weights.h5', is_load_model=False)
163.    
164.   #ResNet50
165.   model = transfer.ResNet50_model(nb_classes=2, img_rows=image_size, img_cols=image_size, is_plot_model=False)
166.   history_ft = transfer.train_model(model, 10, train_generator, 600, validation_generator, 60, 'resnet50_model_weights.h5', is_load_model=False)
167.    
168.   #InceptionV3
169.   #model = transfer.InceptionV3_model(nb_classes=2, img_rows=image_size, img_cols=image_size, is_plot_model=True)
170.   #history_ft = transfer.train_model(model, 10, train_generator, 600, validation_generator, 60, 'inception_v3_model_weights.h5', is_load_model=False)
171.    
172.   # 训练的acc_loss图
173.   transfer.plot_training(history_ft)

标签：plot,ResNet50,generator,img,VGG19,base,InceptionV3,model,size
来源： https://www.cnblogs.com/shuimuqingyang/p/11231748.html