title: All inputs to the layer should be tensors
body: <blockquote>
    <p>ValueError: Layer concatenate_1 was called with an input that isn't a symbolic tensor. Received type: . Full input: [9]. All inputs to the layer should be tensors.</p>
  </blockquote>
  
  <p>Problem on line <code>paired_encoders_merger = concatenate(paired_encoder_size)</code></p>
  
  <pre><code>def __init__(self, input_size=18, paired_encoder_size=9, common_encoder_size=126, size_class=10):
  
      inputs = []
      [inputs.append(Input(shape=(input_size,1), name=('input_'+str(i)))) for i in range(7)]
  
      paired_encoders = []
      for i in range(7):
          paired_encoders.append(Dense(paired_encoder_size, activation='relu')(inputs[i]))
  
      paired_encoders_merger = concatenate(paired_encoder_size)
      common_encoder = Dense(common_encoder_size, activation='relu')(paired_encoders_merger)
      classes = Dense(size_class, activation='relu')(common_encoder)
      common_decoded = Dense(common_encoder_size, activation='relu')(classes)
      paired_decoded = []
      for i in range(7):
          paired_decoded.append(Dense(paired_encoder_size, activation='relu')(common_decoded))        
  
      out_pair = []
  
      for i in range(7):
          out_pair.append(Dense(input_size, activation='relu', name=('out_'+str(i)))(paired_decoded[i]))
  
      out_merger = concatenate(out_pair)
  
      self.model = Model(inputs=inputs,  outputs=out_merger)
      self.model.compile(optimizer='ADAM', loss='binary_crossentropy')
  </code></pre>