title: LSTM Keras API predicting multiple outputs
body: <p>I'm training an LSTM model using as input a sequence of 50 steps of 3 different features laid out as below:</p>
  
  <pre><code>#x_train
  [[[a0,b0,c0],.....[a49,b49,c49]],
    [a1,b1,c1]......[a50,b50,c50]],
    ...
    [a49,b49,c49]...[a99,b99,c99]]]
  </code></pre>
  
  <p>Using the following dependent variable</p>
  
  <pre><code>#y_train
  [a50, a51, a52, ... a99]
  </code></pre>
  
  <p>The code below works to predict just a, how do I get it to predict and return a vector of [a,b,c] at a given timestep?</p>
  
  <pre><code>def build_model():
  model = Sequential()
  
  model.add(LSTM(
      input_shape=(50,3),
      return_sequences=True, units=50))
  model.add(Dropout(0.2))
  
  model.add(LSTM(
      250,
      return_sequences=False))
  model.add(Dropout(0.2))
  
  model.add(Dense(1))
  model.add(Activation("linear"))
  
  model.compile(loss="mse", optimizer="rmsprop")
  return model
  </code></pre>