I have several neural networks. Their outputs are concatenated and then passed to LSTM.
Here is a simplified code snippet:
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)
Unfortunately I get an error on the last line:
---------------------------------------------------------------------------
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'
Is there a way to fix it? If it is important I use PlaidML backend as the only option for macOS with discrete GPU support.
To achieve the goal here you can use Reshape layer, that convert input into the target shape.
Keras is integrated with Tensorflow. Here is the working code in Tensorflow version.
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()
Output:
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
__________________________________________________________________________________________________