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Error: keras merge LSTM layers in sum mode
2019 Community Moderator ElectionMerge two models - Keras“concat” mode can only merge layers with matching output shapes except for the concat axisRight Way to Input Text Data in Keras Auto EncoderUnevenly stretched sequences with LSTM/GRUWhy use two LSTM layers one after another?input_dim for Dense Layer after LSTM layers KerasMixing Textual Data and Numerical Data (Neural Network)How do I build a permutation invariance neural network in keras?Understanding output of LSTM for regressionError: building keras model using LSTM
$begingroup$
I want to merge two sequential models in sum mode into one model using keras as:
left = Sequential()
left.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
right = Sequential()
right.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
model = Sequential()
model.add(Add()([left, right]))
But the statement model.add(Add()[left,right]) gives error: Layer add was called with an input that isn't a symbolic tensor. Received type: . Full input: [, ]. All inputs to the layer should be tensors.
keras lstm
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
$endgroup$
add a comment |
$begingroup$
I want to merge two sequential models in sum mode into one model using keras as:
left = Sequential()
left.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
right = Sequential()
right.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
model = Sequential()
model.add(Add()([left, right]))
But the statement model.add(Add()[left,right]) gives error: Layer add was called with an input that isn't a symbolic tensor. Received type: . Full input: [, ]. All inputs to the layer should be tensors.
keras lstm
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
$endgroup$
add a comment |
$begingroup$
I want to merge two sequential models in sum mode into one model using keras as:
left = Sequential()
left.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
right = Sequential()
right.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
model = Sequential()
model.add(Add()([left, right]))
But the statement model.add(Add()[left,right]) gives error: Layer add was called with an input that isn't a symbolic tensor. Received type: . Full input: [, ]. All inputs to the layer should be tensors.
keras lstm
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
$endgroup$
I want to merge two sequential models in sum mode into one model using keras as:
left = Sequential()
left.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
right = Sequential()
right.add(LSTM(64,activation='sigmoid',stateful=True,batch_input_shape=(10,look_back,dim)))
model = Sequential()
model.add(Add()([left, right]))
But the statement model.add(Add()[left,right]) gives error: Layer add was called with an input that isn't a symbolic tensor. Received type: . Full input: [, ]. All inputs to the layer should be tensors.
keras lstm
keras lstm
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
asked Mar 29 at 9:32
shaifali Guptashaifali Gupta
7810
7810
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
The error says what's the problem: the method expects a Tensors, but you are giving a Sequential model object.
Use functional model (from keras.models import Model), not Sequential.
Then, merge the models with:
merged_models = Model(inputs=[first_model_input, second_model_input], outputs=[first_model_output, second_model_output])
or whatever your input looks like.
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
$endgroup$
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Does this mean that the output of Model is not merging themYes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.
$endgroup$
– Antonio Jurić
Mar 29 at 10:06
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
The error says what's the problem: the method expects a Tensors, but you are giving a Sequential model object.
Use functional model (from keras.models import Model), not Sequential.
Then, merge the models with:
merged_models = Model(inputs=[first_model_input, second_model_input], outputs=[first_model_output, second_model_output])
or whatever your input looks like.
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
$endgroup$
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Does this mean that the output of Model is not merging themYes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.
$endgroup$
– Antonio Jurić
Mar 29 at 10:06
add a comment |
$begingroup$
The error says what's the problem: the method expects a Tensors, but you are giving a Sequential model object.
Use functional model (from keras.models import Model), not Sequential.
Then, merge the models with:
merged_models = Model(inputs=[first_model_input, second_model_input], outputs=[first_model_output, second_model_output])
or whatever your input looks like.
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
$endgroup$
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Does this mean that the output of Model is not merging themYes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.
$endgroup$
– Antonio Jurić
Mar 29 at 10:06
add a comment |
$begingroup$
The error says what's the problem: the method expects a Tensors, but you are giving a Sequential model object.
Use functional model (from keras.models import Model), not Sequential.
Then, merge the models with:
merged_models = Model(inputs=[first_model_input, second_model_input], outputs=[first_model_output, second_model_output])
or whatever your input looks like.
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
$endgroup$
The error says what's the problem: the method expects a Tensors, but you are giving a Sequential model object.
Use functional model (from keras.models import Model), not Sequential.
Then, merge the models with:
merged_models = Model(inputs=[first_model_input, second_model_input], outputs=[first_model_output, second_model_output])
or whatever your input looks like.
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
answered Mar 29 at 9:45
Antonio JurićAntonio Jurić
741111
741111
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Does this mean that the output of Model is not merging themYes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.
$endgroup$
– Antonio Jurić
Mar 29 at 10:06
add a comment |
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Does this mean that the output of Model is not merging themYes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.
$endgroup$
– Antonio Jurić
Mar 29 at 10:06
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
In my case, I need to pass the output of Add() to next layers after merging. For example Dense. If I use: merged_models = Model(inputs=[left.input, right.input], outputs=[left.output,right.output]) dd=Dense(dm, activation='linear')(merged_models.output) It again gives error that dense was expecting one input but it got two. Does this mean that the output of Model is not merging them
$endgroup$
– shaifali Gupta
Mar 29 at 10:01
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Then your approach (and the question) is wrong. Just don't create the models while you are not finished with defining all the layers. That way you won't get errors about expecting the Tensors, but something else was given. And then create the model at the end.
$endgroup$
– Antonio Jurić
Mar 29 at 10:03
$begingroup$
Does this mean that the output of Model is not merging them Yes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.$endgroup$
– Antonio Jurić
Mar 29 at 10:06
$begingroup$
Does this mean that the output of Model is not merging them Yes, the model is not merging the outputs, that's not the purpose of Functional model constructor. To merge the layers, use layers, not model constructor.$endgroup$
– Antonio Jurić
Mar 29 at 10:06
add a comment |
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