在冻结的Keras模型中，辍学层是否仍处于活动状态（即，trainable=False）？

我有两个训练有素的模型（model_A和model_B），并且两个模型都有辍学层。我已经冻结model_A并model_B合并了它们，并获得了新的密集层model_AB（但我尚未删除model_A的和model_B的辍学层）。model_AB的权重将是不可训练的，除了增加的致密层。

现在的问题是：在辍学层model_A和model_B活动状态（即滴神经元）时，我的训练model_AB？

Short answer: The dropout layers will continue dropping neurons during training, even if you set their trainable property to False.

Long answer: There are two distinct notions in Keras:

Updating the weights and states of a layer: this is controlled using trainable property of that layer, i.e. if you set layer.trainable = False then the weights and internal states of the layer would not be updated.

Behavior of a layer in training and testing phases: as you know a layer, like dropout, may have a different behavior during training and testing phases. Learning phase in Keras is set using keras.backend.set_learning_phase(). For example, when you call model.fit(...) the learning phase is automatically set to 1 (i.e. training), whereas when you use model.predict(...) it will be automatically set to 0 (i.e. test). Further, note that learning phase of 1 (i.e. training) does not necessarily imply updating the weighs/states of a layer. You can run your model with a learning phase of 1 (i.e. training phase), but no weights will be updated; just layers will switch to their training behavior (see this answer for more information). Further, there is another way to set learning phase for each individual layer by passing training=True在张量上调用图层时的实参（有关更多信息，请参见此答案）。

因此，根据以上几点，当您trainable=False在退出层上设置并在训练模式下使用该层时（例如，通过调用model.fit(...)，或手动将学习阶段设置为训练，如下例所示），神经元仍会被退出层丢弃。

这是一个可重现的示例，说明了这一点：

from keras import layers
from keras import models
from keras import backend as K
import numpy as np

inp = layers.Input(shape=(10,))
out = layers.Dropout(0.5)(inp)

model = models.Model(inp, out)
model.layers[-1].trainable = False  # set dropout layer as non-trainable
model.compile(optimizer='adam', loss='mse') # IMPORTANT: we must always compile model after changing `trainable` attribute

# create a custom backend function so that we can control the learning phase
func = K.function(model.inputs + [K.learning_phase()], model.outputs)

x = np.ones((1,10))
# learning phase = 1, i.e. training mode
print(func([x, 1]))
# the output will be:
[array([[2., 2., 2., 0., 0., 2., 2., 2., 0., 0.]], dtype=float32)]
# as you can see some of the neurons have been dropped

# now set learning phase = 0, i.e test mode
print(func([x, 0]))
# the output will be:
[array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]], dtype=float32)]
# unsurprisingly, no neurons have been dropped in test phase