()
return contrib_training.HParams(
      batch_size=8,
      learning_rate=0.0006,
      decay_steps=10000,
      decay_rate=0.98,
      clip_norm=3.0,
      transform_audio=False,
      max_expected_train_example_len=625,  # 20 seconds
      offset_network=True,
      onset_lstm_units=256,
      offset_lstm_units=256,
      velocity_lstm_units=0,
      frame_lstm_units=0,
      combined_lstm_units=256,
      bidirectional=True,
      acoustic_rnn_stack_size=1,
      acoustic_rnn_dropout_keep_prob=1.0,
      combined_rnn_stack_size=1,
      combined_rnn_dropout_keep_prob=1.0,
      activation_loss=False,
      stop_activation_gradient=False,
      stop_onset_gradient=True,
      stop_offset_gradient=True,
      weight_frame_and_activation_loss=False,
      share_conv_features=False,
      temporal_sizes=[3, 3, 3],
      freq_sizes=[3, 3, 3],
      num_filters=[48, 48, 96],
      pool_sizes=[1, 2, 2],
      dropout_keep_amts=[1.0, 0.25, 0.25],
      fc_size=768,
      fc_dropout_keep_amt=0.5,
      use_tflite_compatible=False,
      use_lengths=False,
      predict_frame_threshold=0.5,
      predict_onset_threshold=0.5,
      predict_offset_threshold=0,
  )
Returns the default hyperparameters.

  Returns:
    A tf.contrib.training.HParams object representing the default
    hyperparameters for the model.
Returns the default hyperparameters.
Returns the default hyperparameters .
def get_default_hparams():
  """Returns the default hyperparameters.

  Returns:
    A tf.contrib.training.HParams object representing the default
    hyperparameters for the model.
  """
  return contrib_training.HParams(
      batch_size=8,
      learning_rate=0.0006,
      decay_steps=10000,
      decay_rate=0.98,
      clip_norm=3.0,
      transform_audio=False,
      max_expected_train_example_len=625,  # 20 seconds
      offset_network=True,
      onset_lstm_units=256,
      offset_lstm_units=256,
      velocity_lstm_units=0,
      frame_lstm_units=0,
      combined_lstm_units=256,
      bidirectional=True,
      acoustic_rnn_stack_size=1,
      acoustic_rnn_dropout_keep_prob=1.0,
      combined_rnn_stack_size=1,
      combined_rnn_dropout_keep_prob=1.0,
      activation_loss=False,
      stop_activation_gradient=False,
      stop_onset_gradient=True,
      stop_offset_gradient=True,
      weight_frame_and_activation_loss=False,
      share_conv_features=False,
      temporal_sizes=[3, 3, 3],
      freq_sizes=[3, 3, 3],
      num_filters=[48, 48, 96],
      pool_sizes=[1, 2, 2],
      dropout_keep_amts=[1.0, 0.25, 0.25],
      fc_size=768,
      fc_dropout_keep_amt=0.5,
      use_tflite_compatible=False,
      use_lengths=False,
      predict_frame_threshold=0.5,
      predict_onset_threshold=0.5,
      predict_offset_threshold=0,
  )
def get_default_hparams ( ) : return contrib_training . HParams ( batch_size = 8 , learning_rate = 0.0006 , decay_steps = 10000 , decay_rate = 0.98 , clip_norm = 3.0 , transform_audio = False , max_expected_train_example_len = 625 , # 20 seconds offset_network = True , onset_lstm_units = 256 , offset_lstm_units = 256 , velocity_lstm_units = 0 , frame_lstm_units = 0 , combined_lstm_units = 256 , bidirectional = True , acoustic_rnn_stack_size = 1 , acoustic_rnn_dropout_keep_prob = 1.0 , combined_rnn_stack_size = 1 , combined_rnn_dropout_keep_prob = 1.0 , activation_loss = False , stop_activation_gradient = False , stop_onset_gradient = True , stop_offset_gradient = True , weight_frame_and_activation_loss = False , share_conv_features = False , temporal_sizes = [ 3 , 3 , 3 ] , freq_sizes = [ 3 , 3 , 3 ] , num_filters = [ 48 , 48 , 96 ] , pool_sizes = [ 1 , 2 , 2 ] , dropout_keep_amts = [ 1.0 , 0.25 , 0.25 ] , fc_size = 768 , fc_dropout_keep_amt = 0.5 , use_tflite_compatible = False , use_lengths = False , predict_frame_threshold = 0.5 , predict_onset_threshold = 0.5 , predict_offset_threshold = 0 , )