我正在加载VGG16预训练模型，添加几个密集层并对基础VGG16的最后5层进行微调 . 我在mutliple gpus上训练我的模型 . 我在训练前后保存了模型 . 尽管有layers.trainable = True，权重是相同的 .
请帮忙！
继承人的代码
from keras import applications
from keras import Model
<import other relevant Keras layers, etc.>
model = applications.VGG16(weights = "imagenet", include_top = False, input_shape = (224,224,3))
model.save('./before_training')
for layer in model.layers:
layer.trainable = False
for layer in model.layers[-5:]:
layer.trainable = True
x = model.output
x = Flatten()(x)
x = Dense(1024, activation = "relu")(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation = "relu")(x)
predictions = Dense(2, activation = "softmax")(x)
model_final = Model(input = model.input, output = predictions)
from keras.utils import multi_gpu_model
parallel_model = multi_gpu_model(model_final, gpus = 4)
parallel_model.compile(loss = "categorical_crossentropy" ..... )
datagen = ImageDataGenerator(....)
early = EarlyStopping(...)
train_generator = datagen.flow_from_directory(train_data_dir,...)
validation_generator = datagen.flow_from_directory(test_data_dir,...)
parallel_model.fit_generator(train_generator, validation_data = valiudation_generator,...)
model_final.save('./after_training)
before_training和after_training模型中的权重是相同的！这不是我的预期！