Layer called with an input that isn't a symbolic tensor keras

user_3853339 0
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I'm trying to pass the output of one layer into two different layers and then join them back together. However, I'm being stopped by this error which is telling me that my input isn't a symbolic tensor.

Received type: <class 'keras.layers.recurrent.LSTM'>. All inputs to the layers should be tensors.

However, I believe I'm following the documentation quite closely: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models

and am not entirely sure why this is wrong? 

net_input = Input(shape=(maxlen, len(chars)), name='net_input')
lstm_out = LSTM(128, input_shape=(maxlen, len(chars)))

book_out = Dense(len(books), activation='softmax', name='book_output')(lstm_out)
char_out = Dense(len(chars-4), activation='softmax', name='char_output')(lstm_out)

x = keras.layers.concatenate([book_out, char_out])
net_output = Dense(len(chars)+len(books), activation='sigmoid', name='net_output')

model = Model(inputs=[net_input], outputs=[net_output])

Thanks

recurrent-neural-network keras neural-network python
3 Answers

I know, documentation can be confusing, but Concatenate actually only requires "axis" as parameter, while you passed the layers. The layers need to be passed as argument to the result of it as follow:

Line to modify:

x = keras.layers.concatenate([book_out, char_out])

How it should be:

x = keras.layers.Concatenate()([book_out, char_out])

user_118608020

I think you need to add axis=1 to concatenate, Try:

x = keras.layers.concatenate([book_out, char_out], axis=1)
user_107251350
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