new_contents: # Example for my blog post at:
  # https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
  import functools
  import sets
  import tensorflow as tf
  
  
  def lazy_property(function):
      attribute = '_' + function.__name__
  
      @property
      @functools.wraps(function)
      def wrapper(self):
          if not hasattr(self, attribute):
              setattr(self, attribute, function(self))
          return getattr(self, attribute)
      return wrapper
  
  
  class SequenceClassification:
  
      def __init__(self, data, target, dropout, num_hidden=200, num_layers=3):
          self.data = data
          self.target = target
          self.dropout = dropout
          self._num_hidden = num_hidden
          self._num_layers = num_layers
          self.prediction
          self.error
          self.optimize
  
      @lazy_property
      def prediction(self):
          # Recurrent network.
          network = tf.contrib.rnn.GRUCell(self._num_hidden)
          network = tf.contrib.rnn.DropoutWrapper(
              network, output_keep_prob=self.dropout)
          network = tf.contrib.rnn.MultiRNNCell([network] * self._num_layers)
          output, _ = tf.nn.dynamic_rnn(network, self.data, dtype=tf.float32)
          # Select last output.
          output = tf.transpose(output, [1, 0, 2])
          last = tf.gather(output, int(output.get_shape()[0]) - 1)
          # Softmax layer.
          weight, bias = self._weight_and_bias(
              self._num_hidden, int(self.target.get_shape()[1]))
          prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
          return prediction
  
      @lazy_property
      def cost(self):
          cross_entropy = -tf.reduce_sum(self.target * tf.log(self.prediction))
          return cross_entropy
  
      @lazy_property
      def optimize(self):
          learning_rate = 0.003
          optimizer = tf.train.RMSPropOptimizer(learning_rate)
          return optimizer.minimize(self.cost)
  
      @lazy_property
      def error(self):
          mistakes = tf.not_equal(
              tf.argmax(self.target, 1), tf.argmax(self.prediction, 1))
          return tf.reduce_mean(tf.cast(mistakes, tf.float32))
  
      @staticmethod
      def _weight_and_bias(in_size, out_size):
          weight = tf.truncated_normal([in_size, out_size], stddev=0.01)
          bias = tf.constant(0.1, shape=[out_size])
          return tf.Variable(weight), tf.Variable(bias)
  
  
  def main():
      # We treat images as sequences of pixel rows.
      train, test = sets.Mnist()
      _, rows, row_size = train.data.shape
      num_classes = train.target.shape[1]
      data = tf.placeholder(tf.float32, [None, rows, row_size])
      target = tf.placeholder(tf.float32, [None, num_classes])
      dropout = tf.placeholder(tf.float32)
      model = SequenceClassification(data, target, dropout)
      sess = tf.Session()
      sess.run(tf.global_variables_initializer())
      for epoch in range(10):
          for _ in range(100):
              batch = train.sample(10)
              sess.run(model.optimize, {
                  data: batch.data, target: batch.target, dropout: 0.5})
          error = sess.run(model.error, {
              data: test.data, target: test.target, dropout: 1})
          print('Epoch {:2d} error {:3.1f}%'.format(epoch + 1, 100 * error))
  
  
  if __name__ == '__main__':
      main()
  
subject: Create tensorflow_sequence_classification.py