在Keras中使用multi_gpu_model冻结图层

from keras.utils import multi_gpu_model
from keras.models import Model
from keras.layers import Input, Dense

input_shape = (input_dim,)
inputs = Input(shape=input_shape)
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
outputs = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

for layer in model.layers[:2]:
    layer.trainable = False

parallel_model = multi_gpu_model(model, gpus=2)

parallel_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

parallel_model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val))