title: Keras: "a tensor specified as input to your model was not an Input tensor." How to properly transform the output tensor to input tensor?
body: <p>I am building a Network like below. model_A is a classification model, and its one-hot-encoding outputs combined with the original inputs becomes the inputs of model_B. </p>
  
  <pre><code>import keras
  from keras.layers import Input, Dense
  from keras.models import Model
  
  inputs = Input(shape=(12,))
  
  # ---------------------------------------
  # model_A
  x = Dense(64, activation='relu')(inputs)
  x = Dense(64, activation='relu')(x)
  predictions_A = Dense(3, activation='softmax')(x)
  model_A = Model(inputs=inputs, outputs=predictions_A)
  
  # ---------------------------------------
  # model_B
  inputs_B = keras.layers.concatenate([inputs, predictions_A])
  x1 = Dense(64, activation='relu')(inputs_B)
  x1 = Dense(64, activation='relu')(x1)
  predictions_B = Dense(1, activation='sigmoid')(x1)
  model_B = Model(inputs=inputs_B, outputs=predictions_B)
  </code></pre>
  
  <p>The model_A part works just fine. However, when I started to add the model_B, I got the following error:</p>
  
  <pre><code>workspace/git/tensorplay/venv/lib/python3.7/site-packages/keras/engine/network.py:180: UserWarning: Model inputs must come from `keras.layers.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to your model was not an Input tensor, it was generated by layer concatenate_7.
  Note that input tensors are instantiated via `tensor = keras.layers.Input(shape)`.
  The tensor that caused the issue was: concatenate_7/concat:0
    str(x.name))
  </code></pre>
  
  <p>Any idea what's the properly handle the inputs for model_B? Thanks!</p>