title: how to stack LSTM layers using TensorFlow
body: <p>what I have is the following, which I believe is a network with one hidden LSTM layer:</p>
  
  <pre><code># Parameters
  learning rate = 0.001
  training_iters = 100000
  batch_size = 128
  display_step = 10
  
  # Network Parameters
  n_input = 13
  n_steps = 10
  n_hidden = 512
  n_classes = 13
  
  # tf Graph input
  x = tf.placeholder("float", [None, n_steps, n_input])
  y = tf.placeholder("float", [None, n_classes])
  
  # Define weights
  weights = {
      'out' : tf.Variable(tf.random_normal([n_hidden, n_classes]))
  }
  biases = {
      'out' : tf.Variable(tf.random_normal([n_classes]))
  }
  </code></pre>
  
  <p>However, I am trying to build an LSTM network using TensorFlow to predict power consumption.  I have been looking around to find a good example, but I could not find any model with 2 hidden LSTM layers. Here's the model that I would like to build:</p>
  
  <p>1 input layer, 
  1 output layer, 
  2 hidden LSTM layers(with 512 neurons in each), 
  time step(sequence length): 10</p>
  
  <p>Could anyone guide me to build this using TensorFlow? ( from defining weights, building input shape, training, predicting, use of optimizer or cost function, etc), any help would be much appreciated.</p>
  
  <p>Thank you so much in advance!</p>