(config, eval_ratio)
return dag_pipeline.DAGPipeline(dag)
Returns the Pipeline instance which creates the RNN dataset.

  Args:
    config: An ImprovRnnConfig object.
    eval_ratio: Fraction of input to set aside for evaluation set.

  Returns:
    A pipeline.Pipeline instance.
Returns the Pipeline instance which creates the RNN dataset.
Returns the Pipeline instance which creates the RNN dataset .
def get_pipeline(config, eval_ratio):
  """Returns the Pipeline instance which creates the RNN dataset.

  Args:
    config: An ImprovRnnConfig object.
    eval_ratio: Fraction of input to set aside for evaluation set.

  Returns:
    A pipeline.Pipeline instance.
  """
  all_transpositions = config.transpose_to_key is None
  partitioner = pipelines_common.RandomPartition(
      note_seq.NoteSequence,
      ['eval_lead_sheets', 'training_lead_sheets'],
      [eval_ratio])
  dag = {partitioner: dag_pipeline.DagInput(note_seq.NoteSequence)}

  for mode in ['eval', 'training']:
    time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
        name='TimeChangeSplitter_' + mode)
    quantizer = note_sequence_pipelines.Quantizer(
        steps_per_quarter=config.steps_per_quarter, name='Quantizer_' + mode)
    lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
        min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
        ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
        name='LeadSheetExtractor_' + mode)
    encoder_pipeline = EncoderPipeline(config, name='EncoderPipeline_' + mode)

    dag[time_change_splitter] = partitioner[mode + '_lead_sheets']
    dag[quantizer] = time_change_splitter
    dag[lead_sheet_extractor] = quantizer
    dag[encoder_pipeline] = lead_sheet_extractor
    dag[dag_pipeline.DagOutput(mode + '_lead_sheets')] = encoder_pipeline

  return dag_pipeline.DAGPipeline(dag)
def get_pipeline ( config , eval_ratio ) : all_transpositions = config . transpose_to_key is None partitioner = pipelines_common . RandomPartition ( note_seq . NoteSequence , [ 'eval_lead_sheets' , 'training_lead_sheets' ] , [ eval_ratio ] ) dag = { partitioner : dag_pipeline . DagInput ( note_seq . NoteSequence ) } for mode in [ 'eval' , 'training' ] : time_change_splitter = note_sequence_pipelines . TimeChangeSplitter ( name = 'TimeChangeSplitter_' + mode ) quantizer = note_sequence_pipelines . Quantizer ( steps_per_quarter = config . steps_per_quarter , name = 'Quantizer_' + mode ) lead_sheet_extractor = lead_sheet_pipelines . LeadSheetExtractor ( min_bars = 7 , max_steps = 512 , min_unique_pitches = 3 , gap_bars = 1.0 , ignore_polyphonic_notes = False , all_transpositions = all_transpositions , name = 'LeadSheetExtractor_' + mode ) encoder_pipeline = EncoderPipeline ( config , name = 'EncoderPipeline_' + mode ) dag [ time_change_splitter ] = partitioner [ mode + '_lead_sheets' ] dag [ quantizer ] = time_change_splitter dag [ lead_sheet_extractor ] = quantizer dag [ encoder_pipeline ] = lead_sheet_extractor dag [ dag_pipeline . DagOutput ( mode + '_lead_sheets' ) ] = encoder_pipeline return dag_pipeline . DAGPipeline ( dag )