new_contents: #!/usr/bin/env python
  
  import climate
  import logging
  import matplotlib.pyplot as plt
  import numpy.random as rng
  import theanets
  
  climate.enable_default_logging()
  
  TIME = 10
  BATCH_SIZE = 32
  
  e = theanets.Experiment(
      theanets.recurrent.Autoencoder,
      layers=(3, 100, 3),
      recurrent_error_start=TIME - 1,
      batch_size=BATCH_SIZE)
  
  def generate():
      r = rng.randn(TIME, BATCH_SIZE, 3).astype('f')
      r[-1] = r[0]
      return [r]
  
  batch = generate()
  logging.info('data batches: %s', batch[0].shape)
  
  e.train(generate)
  
  target = batch[0][-1]
  predict = e.network.predict(batch[0])[-1]
  vm = max(abs(target).max(), abs(predict).max())
  
  ax = plt.subplot(211)
  ax.set_frame_on(False)
  for loc, spine in ax.spines.items():
      spine.set_color('none')
  ax.imshow(target.T, cmap='gray', vmin=-vm, vmax=vm)
  ax.set_xticks([])
  ax.set_yticks([])
  
  ax = plt.subplot(212)
  ax.set_frame_on(False)
  for loc, spine in ax.spines.items():
      spine.set_color('none')
  ax.imshow(predict.T, cmap='gray', vmin=-vm, vmax=vm)
  ax.set_yticks([])
  
  plt.show()
  
subject: Add example for "memory" recurrent task.