title: Layer called with an input that isn't a symbolic tensor keras
body: <p>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.</p>
  
  <pre><code>Received type: &lt;class 'keras.layers.recurrent.LSTM'&gt;. All inputs to the layers should be tensors.
  </code></pre>
  
  <p>However, I believe I'm following the documentation quite closely:
  <a href="https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models" rel="noreferrer">https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models</a></p>
  
  <p>and am not entirely sure why this is wrong? </p>
  
  <pre><code>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])
  </code></pre>
  
  <p>Thanks</p>