keras例子之迁移学习

1、抽取中间层特征

from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np

base_model = VGG19(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)

img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

block4_pool_features = model.predict(x)

2、迁移学习

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K

# 创建预训练模型
base_model = InceptionV3(weights='imagenet', include_top=False)

# 增加全局平均池化层
x = base_model.output
x = GlobalAveragePooling2D()(x)
# 增加全连接层
x = Dense(1024, activation='relu')(x)
# softmax激活函数用户分类
predictions = Dense(200, activation='softmax')(x)

# 预训练模型与新加层的组合
model = Model(inputs=base_model.input, outputs=predictions)

# 只训练新加的Top层，冻结InceptionV3所有层
for layer in base_model.layers:
    layer.trainable = False

# 编译模型
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# 训练模型
model.fit_generator(...)


# 第二种设置：冻结前249层，训练后249层
for layer in model.layers[:249]:
   layer.trainable = False
for layer in model.layers[249:]:
   layer.trainable = True

# 编译模型
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')

# 训练模型
model.fit_generator(...)

参考文献：
1、http://keras-cn.readthedocs.io/en/latest/