def default_hparams():
    'Generates the hparams used to train note rnn used in paper.'
    return tf.contrib.training.HParams(use_dynamic_rnn=True, batch_size=BATCH_SIZE, lr=0.0002, l2_reg=2.5e-05, 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.
def default_hparams():
    
    return tf.contrib.training.HParams(use_dynamic_rnn=True, batch_size=BATCH_SIZE, lr=0.0002, l2_reg=2.5e-05, 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)