def vgg16_model(img_row, img_col, channel, num_classes):
model = Sequential()
# model.add(ZeroPadding2D(1, 1), input_shape = (img_row, img_col, channel))
model.add(
Conv2D(64, (3, 3),
padding='same',
input_shape=(img_row, img_col, channel),
activation='relu')) #224
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2))) #112 注意步长是2才能下采样！
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2))) #56
model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2))) #28
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2))) #14
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2))) #7
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='softmax'))
## 加载预训练模型在ImageNet上训练好的权重
model.load_weights(
'C:\\Users\\chiyuan\\.keras\\models\\vgg16_weights_tf_dim_ordering_tf_kernels.h5'
)
## 去掉最后一层FC，用自己的代替
model.layers.pop() #去掉最后一层
model.outputs = [model.layers[-1].output] #加中括号将多个结果直接放进一个列表
model.layers[-1].outbound_nodes = [] ##???????????????
model.add(Dense(num_classes, activation='softmax'))
## 保持前十层权重不变（freeze）
for layer in model.layers[:10]:
layer.trainable = False
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
