title: Keras lstm multi output model predict two features (time series)
body: <p>I need to ask how to use keras predict from keras functional api.
  I need to write multivariate LSTM model with multioutput.
  I have written such model:</p>
  
  <pre><code>inp = Input((train_X.shape[1],train_X.shape[2]))
  x = LSTM(192,return_sequences=True)(inp)
  x = Dropout(0.5)(x)
  x = Flatten()(x)
  out1 = Dense(1,activation='softsign')(x)
  out2 = Dense(1,activation='softsign')(x)
  model = Model(inputs =inp,outputs= (out1,out2))
  model.compile(optimizer='rmsprop',
            loss='binary_crossentropy',
            metrics=['accuracy'])
  
  history = model.fit(train_X,[y1,y2])
  ypred = model.predict([pred_X,pred_Y])
  </code></pre>
  
  <p>What I want (expect) is ypred returning two time series,but it gives me error:</p>
  
  <pre><code>ValueError: Error when checking model : the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 2 arrays: 
  </code></pre>
  
  <p>It is expecting one array as predict argument, but with only one array it will return one predicted time series.</p>
  
  <p>What I need to correct to get prediction of two feautures?</p>
  
  <p>My data have 6 columns,first two I'm trying to predict,others are features. Dimensions of my data are:</p>
  
  <pre><code>train_X.shape= (24576, 192, 6)
  pred_X.shape= (672, 192, 6)
  pred_Y.shape= (672, 192, 6)
  y1.shape = (24576,1)
  y2.shape = (24576,1)
  </code></pre>