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

python neural-network keras recurrent-neural-network
tryingtolearn picture tryingtolearn · Jun 30, 2017 · Viewed 55k times · Source

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

Answer

Aditya Gune picture Aditya Gune · Jun 30, 2017

It looks like you're not actually giving an input to your LSTM layer. You specify the number of recurrent neurons and the shape of the input, but do not provide an input. Try:

lstm_out = LSTM(128, input_shape=(maxlen, len(chars)))(net_input)

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