def setUp(self): super().setUp() self.config = events_rnn_model.EventSequenceRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), contrib_training.HParams())
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)), }
def setUp(self): self.enc = polyphony_encoder_decoder.PolyphonyOneHotEncoding()
def setUp(self): self.config = events_rnn_model.EventSequenceRnnConfig( None, magenta.music.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), tf.contrib.training.HParams())