keras使用中间层的输出

Question

我训练了模型A，并尝试将名称为“ layer_x”的中间层的输出用于模型B中的其他输入。

我试图像在keras doc https://keras.io/getting-started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer上一样使用中间层的输出

模型A：

inputs = Input(shape=(100,))
dnn = Dense(1024, activation='relu')(inputs)
dnn = Dense(128, activation='relu', name="layer_x")(dnn)
dnn = Dense(1024, activation='relu')(dnn)
output = Dense(10, activation='softmax')(dnn)

型号B：

input_1 = Input(shape=(200,))
input_2 = Input(shape=(100,)) # input for model A

# loading model A
model_a = keras.models.load_model(path_to_saved_model_a)

intermediate_layer_model = Model(inputs=model_a.input, 
                                 outputs=model_a.get_layer("layer_x").output)

intermediate_output = intermediate_layer_model.predict(data)

merge_layer = concatenate([input_1, intermediate_output])
dnn_layer = Dense(512, activation="relu")(merge_layer)
output = Dense(5, activation="sigmoid")(dnn_layer)
model = keras.models.Model(inputs=[input_1, input_2], outputs=output)

当我调试时，在此行出现错误：

intermediate_layer_model = Model(inputs=model_a.input, 
                                 outputs=model_a.get_layer("layer_x").output)

File "..", line 89, in set_model
  outputs=self.neural_net_asc.model.get_layer("layer_x").output)
File "C:\WinPython\python-3.5.3.amd64\lib\site-packages\keras\legacy\interfaces.py", line 87, in wrapper
  return func(*args, **kwargs)
File "C:\WinPython\python-3.5.3.amd64\lib\site-packages\keras\engine\topology.py", line 1592, in __init__
  mask = node.output_masks[tensor_index]
AttributeError: 'Node' object has no attribute 'output_masks'

我可以使用get_layer（“ layer_x”）。output访问Tensor，并且output_mask为None。我是否必须手动设置输出掩码，如果需要，如何设置该输出掩码？

Answer 1

您似乎做错了两件事：

intermediate_output = intermediate_layer_model.predict(data)

当您这样做时.predict()，您实际上是通过图表传递数据并询问结果是什么。当您这样做时，intermediate_output它将是一个numpy数组，而不是您想要的图层。

其次，您无需重新创建新的中间模型。您可以直接使用您model_a感兴趣的部分。

这是为我“编译”的代码：

from keras.layers import Input, Dense, concatenate
from keras.models import Model

inputs = Input(shape=(100,))
dnn = Dense(1024, activation='relu')(inputs)
dnn = Dense(128, activation='relu', name="layer_x")(dnn)
dnn = Dense(1024, activation='relu')(dnn)
output = Dense(10, activation='softmax')(dnn)

model_a = Model(inputs=inputs, outputs=output)

# You don't need to recreate an input for the model_a, 
# it already has one and you can reuse it
input_b = Input(shape=(200,))

# Here you get the layer that interests you from model_a, 
# it is still linked to its input layer, you just need to remember it for later
intermediate_from_a = model_a.get_layer("layer_x").output

# Since intermediate_from_a is a layer, you can concatenate it with the other input
merge_layer = concatenate([input_b, intermediate_from_a])
dnn_layer = Dense(512, activation="relu")(merge_layer)
output_b = Dense(5, activation="sigmoid")(dnn_layer)
# Here you remember that one input is input_b and the other one is from model_a
model_b = Model(inputs=[input_b, model_a.input], outputs=output_b)

我希望这是您想要做的。

请告诉我是否不清楚:-)