()
return contrib_training.HParams(
      use_dynamic_rnn=True,
      batch_size=BATCH_SIZE,
      lr=0.0002,
      l2_reg=2.5e-5,
      clip_norm=5,
      initial_learning_rate=0.5,
      decay_steps=1000,
      decay_rate=0.85,
      rnn_layer_sizes=[100],
      skip_first_n_losses=32,
      one_hot_length=NUM_CLASSES,
      exponentially_decay_learning_rate=True)
Generates the hparams used to train note rnn used in paper.
Generates the hparams used to train note rnn used in paper.
Generates the hparams used to train note rnn used in paper .
def default_hparams():
  """Generates the hparams used to train note rnn used in paper."""
  return contrib_training.HParams(
      use_dynamic_rnn=True,
      batch_size=BATCH_SIZE,
      lr=0.0002,
      l2_reg=2.5e-5,
      clip_norm=5,
      initial_learning_rate=0.5,
      decay_steps=1000,
      decay_rate=0.85,
      rnn_layer_sizes=[100],
      skip_first_n_losses=32,
      one_hot_length=NUM_CLASSES,
      exponentially_decay_learning_rate=True)
def default_hparams ( ) : return contrib_training . HParams ( use_dynamic_rnn = True , batch_size = BATCH_SIZE , lr = 0.0002 , l2_reg = 2.5e-5 , clip_norm = 5 , initial_learning_rate = 0.5 , decay_steps = 1000 , decay_rate = 0.85 , rnn_layer_sizes = [ 100 ] , skip_first_n_losses = 32 , one_hot_length = NUM_CLASSES , exponentially_decay_learning_rate = True )