import time """ ################################################################### # Configuration # ################################################################### """ # default configuraction. Based on Magenta default_configs = { 'drum_kit': events_rnn_model.EventSequenceRnnConfig( magenta.music.protobuf.generator_pb2.GeneratorDetails( id='drum_kit', description='Drums RNN with multiple drums and binary counters.'), magenta.music.LookbackEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding(), lookback_distances=[], binary_counter_bits=6), contrib_training.HParams(batch_size=64, rnn_layer_sizes=[256, 256], dropout_keep_prob=0.5, attn_length=32, clip_norm=3, learning_rate=0.001)) } # TensorFlow Flags. #Flags to configure evaluation and training. Spanish edition Flags = tf.app.flags.FLAGS tf.app.flags.DEFINE_string( 'run_dir', '/tmp/Deepbeat/logdir/run1', 'Directorio donde se encuentran los checkpoinst y ' 'los análisis de los eventos.Se crearán en directorios separados para '
The log likelihood of `drums` under this model. """ return self._evaluate_log_likelihood([drums])[0] # Default configurations. default_configs = { 'one_drum': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='one_drum', description='Drums RNN with 2-state encoding.'), magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding([ [39] + # use hand clap as default when decoding range(mm.MIN_MIDI_PITCH, 39) + range(39, mm.MAX_MIDI_PITCH + 1) ])), magenta.common.HParams(batch_size=128, rnn_layer_sizes=[128, 128], dropout_keep_prob=0.5, skip_first_n_losses=0, clip_norm=5, initial_learning_rate=0.001, decay_steps=1000, decay_rate=0.95)), 'drum_kit': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='drum_kit', description='Drums RNN with multiple drums and binary counters.'), magenta.music.LookbackEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding(), lookback_distances=[],
branch_factor, steps_per_iteration, modify_events_callback=modify_events_callback) def polyphonic_sequence_log_likelihood(self, sequence): """Evaluate the log likelihood of a polyphonic sequence. Args: sequence: The PolyphonicSequence object for which to evaluate the log likelihood. Returns: The log likelihood of `sequence` under this model. """ return self._evaluate_log_likelihood([sequence])[0] default_configs = { 'polyphony': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='polyphony', description='Polyphonic RNN'), magenta.music.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), tf.contrib.training.HParams(batch_size=64, rnn_layer_sizes=[256, 256, 256], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001)), }
The log likelihood of `drums` under this model. """ return self._evaluate_log_likelihood([drums])[0] # Default configurations. default_configs = { 'one_drum': events_rnn_model.EventSequenceRnnConfig( magenta.music.protobuf.generator_pb2.GeneratorDetails( id='one_drum', description='Drums RNN with 2-state encoding.'), magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding( [[39] + # use hand clap as default when decoding list(range(mm.MIN_MIDI_PITCH, 39)) + list(range(39, mm.MAX_MIDI_PITCH + 1))])), contrib_training.HParams( batch_size=128, rnn_layer_sizes=[128, 128], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001), steps_per_quarter=2), 'drum_kit': events_rnn_model.EventSequenceRnnConfig( magenta.music.protobuf.generator_pb2.GeneratorDetails( id='drum_kit', description='Drums RNN with multiple drums and binary counters.' ), magenta.music.LookbackEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding(),
def setUp(self): self.config = events_rnn_model.EventSequenceRnnConfig( None, mm.PianorollEncoderDecoder(88), tf.contrib.training.HParams())
def setUp(self): super(PianorollPipelineTest, self).setUp() self.config = events_rnn_model.EventSequenceRnnConfig( None, note_seq.PianorollEncoderDecoder(88), contrib_training.HParams())
def setUp(self): self.config = events_rnn_model.EventSequenceRnnConfig( None, magenta.music.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), magenta.common.HParams())
def setUp(self): self.config = events_rnn_model.EventSequenceRnnConfig( None, magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MultiDrumOneHotEncoding()), tf.contrib.training.HParams())
""" return self._generate_events(num_steps=num_steps, primer_events=primer_sequence, temperature=None, beam_size=beam_size, branch_factor=branch_factor, steps_per_iteration=steps_per_iteration) default_configs = { 'rnn-nade': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='rnn-nade', description='RNN-NADE'), mm.PianorollEncoderDecoder(), tf.contrib.training.HParams(batch_size=64, rnn_layer_sizes=[128, 128, 128], nade_hidden_units=128, dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001)), 'rnn-nade_attn': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='rnn-nade_attn', description='RNN-NADE with attention.'), mm.PianorollEncoderDecoder(), tf.contrib.training.HParams(batch_size=48, rnn_layer_sizes=[128, 128], attn_length=32, nade_hidden_units=128, dropout_keep_prob=0.5, clip_norm=5,
def setUp(self): self.config = events_rnn_model.EventSequenceRnnConfig( None, mm.PianorollEncoderDecoder(88), magenta.common.HParams())
beam_size: An integer, beam size to use when generating tracks via beam search. branch_factor: An integer, beam search branch factor to use. steps_per_iteration: An integer, number of steps to take per beam search iteration. Returns: The generated PolyphonicSequence object (which begins with the provided primer track). """ return self._generate_events(num_steps, primer_sequence, temperature, beam_size, branch_factor, steps_per_iteration) default_configs = { 'polyphony': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='polyphony', description='Polyphonic RNN'), magenta.music.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), magenta.common.HParams(batch_size=64, rnn_layer_sizes=[256, 256, 256], dropout_keep_prob=0.5, skip_first_n_losses=10, clip_norm=5, initial_learning_rate=0.001, decay_steps=1000, decay_rate=0.95)), }
primer_events=primer_sequence, temperature=None, beam_size=beam_size, branch_factor=branch_factor, steps_per_iteration=steps_per_iteration) default_configs = { 'rnn-nade': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='rnn-nade', description='RNN-NADE'), mm.PianorollEncoderDecoder(), magenta.common.HParams(batch_size=64, rnn_layer_sizes=[128, 128, 128], nade_hidden_units=128, dropout_keep_prob=0.5, skip_first_n_losses=10, clip_norm=5, initial_learning_rate=0.001, decay_steps=1000, decay_rate=0.95)), 'rnn-nade_attn': events_rnn_model.EventSequenceRnnConfig( magenta.protobuf.generator_pb2.GeneratorDetails( id='rnn-nade_attn', description='RNN-NADE with attention.'), mm.PianorollEncoderDecoder(), magenta.common.HParams(batch_size=48, rnn_layer_sizes=[128, 128], attn_length=32, nade_hidden_units=128,
def setUp(self): super().setUp() self.config = events_rnn_model.EventSequenceRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MultiDrumOneHotEncoding()), contrib_training.HParams())