将连接的输入传递给 keras 中的 LSTM

Question

我有几个神经网络。它们的输出被连接起来，然后传递给 LSTM。

这是一个简化的代码片段：

import keras.backend as K

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

# 1st NN
input_l1 = Input(shape=(1, ))
out_l1 = Dense(1)(input_l1)

# 2nd NN
input_l2 = Input(shape=(1, ))
out_l2 = Dense(1)(input_l2)

# concatenated layer
concat_vec = concatenate([out_l1, out_l2])

# expand dimensions to (None, 2, 1)
expanded_concat = K.expand_dims(concat_vec, axis=2)

lstm_out = LSTM(10)(expanded_concat)

model = keras.Model(inputs=[input_l1, input_l2], outputs=lstm_out)

不幸的是，我在最后一行收到错误消息：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-53-a16fe60c0fc3> in <module>
      2 lstm_out = LSTM(10)(expanded_concat)
      3 
----> 4 model = keras.Model(inputs=[input_l1, input_l2], outputs=lstm_out)

/usr/local/lib/python3.9/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.9/site-packages/keras/engine/network.py in __init__(self, *args, **kwargs)
     91                 'inputs' in kwargs and 'outputs' in kwargs):
     92             # Graph network
---> 93             self._init_graph_network(*args, **kwargs)
     94         else:
     95             # Subclassed network

/usr/local/lib/python3.9/site-packages/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name)
    228 
    229         # Keep track of the network's nodes and layers.
--> 230         nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network(
    231             self.inputs, self.outputs)
    232         self._network_nodes = nodes

/usr/local/lib/python3.9/site-packages/keras/engine/network.py in _map_graph_network(inputs, outputs)
   1361     for x in outputs:
   1362         layer, node_index, tensor_index = x._keras_history
-> 1363         build_map(x, finished_nodes, nodes_in_progress,
   1364                   layer=layer,
   1365                   node_index=node_index,

/usr/local/lib/python3.9/site-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
   1350             node_index = node.node_indices[i]
   1351             tensor_index = node.tensor_indices[i]
-> 1352             build_map(x, finished_nodes, nodes_in_progress, layer,
   1353                       node_index, tensor_index)
   1354 

/usr/local/lib/python3.9/site-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
   1323             ValueError: if a cycle is detected.
   1324         """
-> 1325         node = layer._inbound_nodes[node_index]
   1326 
   1327         # Prevent cycles.

AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

有办法解决吗？如果它很重要，我使用 PlaidML 后端作为支持独立 GPU 的 macOS 的唯一选择。

Answer 1

要实现此处的目标，您可以使用 Reshape 层，将输入转换为目标形状。

Keras 与 Tensorflow 集成。这是 Tensorflow 版本中的工作代码。

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LSTM, concatenate
from tensorflow.keras.models import Model

# 1st NN
input_l1 = Input(shape=(1, ))
out_l1 = Dense(1)(input_l1)

# 2nd NN
input_l2 = Input(shape=(1, ))
out_l2 = Dense(1)(input_l2)

# concatenated layer
concat_vec = concatenate([out_l1, out_l2])

# expand dimensions to (None, 2, 1)
expanded_concat = tf.keras.layers.Reshape((2, 1))(concat_vec)
#expanded_concat = K.expand_dims(concat_vec, axis=2)

lstm_out = LSTM(10)(expanded_concat)

model = Model(inputs=[input_l1, input_l2], outputs=lstm_out)
model.summary()

输出：

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 1)]          0                                            
__________________________________________________________________________________________________
input_2 (InputLayer)            [(None, 1)]          0                                            
__________________________________________________________________________________________________
dense (Dense)                   (None, 1)            2           input_1[0][0]                    
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            2           input_2[0][0]                    
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 2)            0           dense[0][0]                      
                                                                 dense_1[0][0]                    
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 2, 1)         0           concatenate[0][0]                
__________________________________________________________________________________________________
lstm (LSTM)                     (None, 10)           480         reshape_1[0][0]                  
==================================================================================================
Total params: 484
Trainable params: 484
Non-trainable params: 0
__________________________________________________________________________________________________

将连接的输入传递给 keras 中的 LSTM

Issue passing concatenated inputs to LSTM in keras

neural-network

lstm

keras

plaidml