如何冻结图层并微调自定义暹罗网络

Question

我有以下暹罗网络(基于Xception(，其组成如下：

from tensorflow.keras.models import Model, model_from_json, Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, SeparableConv2D, UpSampling2D, BatchNormalization, Input, GlobalAveragePooling2D
from tensorflow.keras.regularizers import l2
from tensorflow.keras.optimizers import SGD, RMSprop
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.utils import plot_model

def entry_flow(inputs) :
x = Conv2D(32, 3, strides = 2, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(64,3,padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
previous_block_activation = x
for size in [128, 256, 728] :
x = Activation('relu')(x)
x = SeparableConv2D(size, 3, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(size, 3, padding='same')(x)
x = BatchNormalization()(x)
x = MaxPooling2D(3, strides=2, padding='same')(x)
residual = Conv2D(size, 1, strides=2, padding='same')(previous_block_activation)
x = tensorflow.keras.layers.Add()([x, residual])
previous_block_activation = x
return x
def middle_flow(x, num_blocks=1) :
previous_block_activation = x
for _ in range(num_blocks) :
x = Activation('relu')(x)
x = SeparableConv2D(728, 3, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(728, 3, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(728, 3, padding='same')(x)
x = BatchNormalization()(x)
x = tensorflow.keras.layers.Add()([x, previous_block_activation])
previous_block_activation = x
return x
def exit_flow(x) :
previous_block_activation = x
x = Activation('relu')(x)
x = SeparableConv2D(728, 3, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(1024, 3, padding='same')(x) 
x = BatchNormalization()(x)
x = MaxPooling2D(3, strides=2, padding='same')(x)
residual = Conv2D(1024, 1, strides=2, padding='same')(previous_block_activation)
x = tensorflow.keras.layers.Add()([x, residual])
x = Activation('relu')(x)
x = SeparableConv2D(728, 3, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(1024, 3, padding='same')(x)
x = BatchNormalization()(x)
x = GlobalAveragePooling2D()(x)
x = Dense(48, activation='linear')(x)
return x

要建立暹罗网络，我只需要调用以下代码：

def build_siamese_model(inputShape):
# specify the inputs for the feature extractor network
inputs = Input(shape=(inputShape))
outputs = exit_flow(middle_flow(entry_flow(inputs)))
xception = Model(inputs, outputs)
# return the model to the calling function
return exception 

我已经在给定的数据集中训练了这个模型，现在我想做的就是再次构建模型，加载之前的权重，冻结层(除了具有48个神经元的密集层(，并用新的数据集训练密集(完全连接(层我如何使用暹罗网络做到这一点？我只能用Imagenet CNNs来做到这一点，但我不知道如何用自定义的暹罗网络来做同样的事情。

Answer 1

model.layers[number_of_layer].trainable = False