title: Training Keras model with multiple inputs
body: <p>I want to train a model with 3 different inputs using keras. The training data - <em>x_train</em>, <em>left_train</em>, <em>right_train</em> is of shape (10000,83,12). Here is part of the code.</p>
  
  <pre><code>from keras.layers import Dense, Input, LSTM
  ...
  x = Input(shape = (83,12), dtype = "float32")
  left = Input(shape = (83,12), dtype = "float32")
  right = Input(shape = (83,12), dtype = "float32")
  ...
  model = Model(inputs = [x, left, right], outputs = output)
  model.compile(optimizer = "adadelta", loss = "categorical_crossentropy", metrics = ["accuracy"])
  model.fit([x_train, left_train, right_train], y_train, validation_data=(x_test, y_test), epochs=20, batch_size=128)
  ...
  </code></pre>
  
  <p>I am getting the following error while training:</p>
  
  <pre><code>ValueError                       Traceback (most recent call last)
  &lt;ipython-input-17-261d36872e91&gt; in &lt;module&gt;()
       51 
       52 
  ---&gt; 53 model.fit([x_train, left, right], y_train, validation_data= 
  (x_test, y_test), epochs=20, batch_size=128)
       54 
       55 scores = model.evaluate(x_test, y_test)
       ...
       ValueError: Error when checking model input: the list of 
  Numpy arrays that you are passing to your model is not the size the 
  model expected. Expected to see 3 array(s), but instead got the 
  following list of 1 arrays: [array(...
  </code></pre>
  
  <p>I do pass a list of 3 inputs when calling <strong>fit</strong> method. What's the problem?</p>