无法建立模型，因为backend.squeeze没有层

Question

我正在尝试建立一个模型，其中必须压缩张量，然后将其输入LSTM。

由于压缩的张量不具有layer属性，因此无法编译模型。

Using TensorFlow backend.
Traceback (most recent call last):
  File "C:/workspace/keras_test/src/testing.py", line 10, in <module>
    model = Model(inputs=model_in, outputs=output)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\engine\network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\engine\network.py", line 237, in _init_graph_network
    self.inputs, self.outputs)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\engine\network.py", line 1353, in _map_graph_network
    tensor_index=tensor_index)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)
  File "E:\ProgramData\Miniconda3\envs\py37\lib\site-packages\keras\engine\network.py", line 1312, in build_map
    node = layer._inbound_nodes[node_index]
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

有关最少的示例，请参见：

from keras import Input, backend, Model
from keras.layers import LSTM, Dense

input_shape = (128, 1, 1)
model_in = Input(tensor=Input(input_shape), shape=input_shape)
squeezed = backend.squeeze(model_in, 2)
hidden1 = LSTM(10)(squeezed)
output = Dense(1, activation='sigmoid')(hidden1)

model = Model(inputs=model_in, outputs=output)
model.summary()

如何删除的一维而model_in又不丢失图层信息？

Answer 1

后端操作squeeze未包装在Lambda层内，因此生成的张量不是Keras张量。结果，它缺少诸如的某些属性_inbound_nodes。您可以将squeeze操作包装如下：

from keras import Input, backend, Model
from keras.layers import LSTM, Dense, Lambda

input_shape = (128, 1, 1)
model_in = Input(tensor=Input(input_shape), shape=input_shape)
squeezed = Lambda(lambda x: backend.squeeze(x, 2))(model_in)
hidden1 = LSTM(10)(squeezed)
output = Dense(1, activation='sigmoid')(hidden1)

model = Model(inputs=model_in, outputs=output)
model.summary()