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(...)