title: Error passing multiple inputs into Keras model
body: <p>I want to train a binary classifier using Keras and my training data is of shape <code>(2000,2,128)</code> and labels of shape <code>(2000,)</code> as Numpy arrays.</p>
  
  <p>The idea is to train such that embeddings together in a single array means they are either same or different, labelled using 0 or 1 respectively.</p>
  
  <p>The training data looks like:
  <code>[[[0 1 2 ....128][129.....256]][[1 2 3 ...128][9 9 3 5...]].....]</code>
   and the labels looks like <code>[1 1 0 0 1 1 0 0..]</code>.</p>
  
  <p>Here is the code:</p>
  
  <pre><code>import keras
  from keras.layers import Input, Dense
  
  from keras.models import Model
  
  frst_input = Input(shape=(128,), name='frst_input')
  scnd_input = Input(shape=(128,),name='scnd_input')
  x = keras.layers.concatenate([frst_input, scnd_input])
  x = Dense(128, activation='relu')(x)
  x=(Dense(1, activation='softmax'))(x)
  model=Model(inputs=[frst_input, scnd_input], outputs=[x])
  model.compile(optimizer='rmsprop', loss='binary_crossentropy',
                loss_weights=[ 0.2],metrics=['accuracy'])
  </code></pre>
  
  <p>I am getting the following error while running this code:</p>
  
  <pre><code>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 2 array(s), but instead got the following list of 1 arrays: [array([[[ 0.07124118, -0.02316936, -0.12737238, ...,  0.15822273,
        0.00129827, -0.02457245],
      [ 0.15869428, -0.0570458 , -0.10459555, ...,  0.0968155 ,
        0.0183982 , -0.077924...
  </code></pre>
  
  <p>How can I resolve this issue? Is my code correct to train the classifier using two inputs to classify?</p>