python - 无法构建模型，因为 backend.squeeze 没有层

Question

我正在尝试构建一个模型，其中我有一个张量，必须对其进行压缩，然后将其输入 LSTM。

模型无法编译，因为压缩张量没有层属性。

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'

有关最小示例，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()

如何在不丢失层信息的情况下删除 model_in 的一维？

Answer 1

后端操作 squeeze 没有包含在 Lambda 中层，因此生成的张量不是 Keras 张量。因此，它缺少一些属性，例如 _inbound_nodes。您可以按如下方式包装 squeeze 操作:

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()