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())
