如何“合并"Keras 2.0 中的序列模型? [英] How to "Merge" Sequential models in Keras 2.0?

问题描述

我正在尝试使用以下行在 Keras 2.0 中合并两个 Sequential 模型:

I am trying to merge two Sequential models In Keras 2.0, using the following line:

merged_model.add(Merge([model1, model2], mode='concat'))

这仍然可以正常工作，但会发出警告:

This still works fine, but gives a warning:

"The `Merge` layer is deprecated and will be removed after 08/2017. Use
instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc." 

但是，研究 Keras 文档并尝试添加 Add() 并没有产生任何效果.我已经阅读了一些有同样问题的人的帖子，但在下面的案例中没有找到适合我的解决方案.有什么建议吗?

However, studying the Keras documentation and trying add, Add(), has not resulted in something that works. I have read several posts from people with the same problem, but found no solution that works in my case below. Any suggestions?

model = Sequential()
model1 = Sequential()
model1.add(Dense(300, input_dim=40, activation='relu', name='layer_1'))
model2 = Sequential()
model2.add(Dense(300, input_dim=40, activation='relu', name='layer_2'))
merged_model = Sequential()

merged_model.add(Merge([model1, model2], mode='concat'))

merged_model.add(Dense(1, activation='softmax', name='output_layer'))
merged_model.compile(loss='binary_crossentropy', optimizer='adam', 
metrics=['accuracy'])

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc',
save_best_only=True, verbose=2)
early_stopping = EarlyStopping(monitor="val_loss", patience=5)

merged_model.fit([x1, x2], y=y, batch_size=384, epochs=200,
             verbose=1, validation_split=0.1, shuffle=True, 
callbacks=[early_stopping, checkpoint])

当我尝试时(如下面 Kent Sommer 的建议):

When I tried (as suggested below by Kent Sommer):

from keras.layers.merge import concatenate
merged_model.add(concatenate([model1, model2]))

这是错误信息:

Traceback (most recent call last):
  File "/anaconda/lib/python3.6/site- packages/keras/engine/topology.py", line 425, 
in assert_input_compatibility
    K.is_keras_tensor(x)
  File "/anaconda/lib/python3.6/site-
packages/keras/backend/tensorflow_backend.py", line 403, in     is_keras_tensor
    raise ValueError('Unexpectedly found an instance of type `' +
 str(type(x)) + '`. '
ValueError: Unexpectedly found an instance of type 
`<class'keras.models.Sequential'>`. Expected a symbolic tensor instance.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "quoradeeptest_simple1.py", line 78, in <module>
    merged_model.add(concatenate([model1, model2]))
  File "/anaconda/lib/python3.6/site-packages/keras/layers/merge.py",
 line 600, in concatenate return Concatenate(axis=axis, **kwargs)(inputs)
  File "/anaconda/lib/python3.6/site-   packages/keras/engine/topology.py", 
line 558, in __call__self.assert_input_compatibility(inputs)
  File "/anaconda/lib/python3.6/site-packages/keras/engine/topology.py", line 431, 
 in assert_input_compatibility str(inputs) + '.All inputs to the layer '
ValueError: Layer concatenate_1 was called with an input that isn't a
symbolic tensor. Received type: <class 'keras.models.Sequential'>. 
Full input: [<keras.models.Sequential object at 0x140fa7ba8>,
<keras.models.Sequential object at 0x140fabdd8>]. All inputs to the
layer should be tensors.

推荐答案

该警告的意思是，不是使用具有特定模式的 Merge 图层，不同的模式现在已拆分为它们自己的单独图层.

What that warning is saying is that instead of using the Merge layer with a specific mode, the different modes have now been split into their own individual layers.

>

所以 Merge(mode='concat') 现在是 concatenate(axis=-1).

但是，由于您想合并模型而不是图层，因此这不适用于您的情况.您需要做的是使用功能模型，因为基本 Sequential 模型类型不再支持此行为.

However, since you want to merge models not layers, this will not work in your case. What you will need to do is use the functional model since this behavior is no longer supported with the basic Sequential model type.

在您的情况下，这意味着应将代码更改为以下内容:

In your case that means the code should be changed to the following:

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

model1_in = Input(shape=(27, 27, 1))
model1_out = Dense(300, input_dim=40, activation='relu', name='layer_1')(model1_in)
model1 = Model(model1_in, model1_out)

model2_in = Input(shape=(27, 27, 1))
model2_out = Dense(300, input_dim=40, activation='relu', name='layer_2')(model2_in)
model2 = Model(model2_in, model2_out)


concatenated = concatenate([model1_out, model2_out])
out = Dense(1, activation='softmax', name='output_layer')(concatenated)

merged_model = Model([model1_in, model2_in], out)
merged_model.compile(loss='binary_crossentropy', optimizer='adam', 
metrics=['accuracy'])

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc',
save_best_only=True, verbose=2)
early_stopping = EarlyStopping(monitor="val_loss", patience=5)

merged_model.fit([x1, x2], y=y, batch_size=384, epochs=200,
             verbose=1, validation_split=0.1, shuffle=True, 
callbacks=[early_stopping, checkpoint])

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