def setUp(self):
self.enc = performance_encoder_decoder.PerformanceOneHotEncoding(
num_velocity_bins=16)
pitch_histogram_window_size=None):
super(PerformanceRnnConfig, self).__init__(details, encoder_decoder,
hparams)
self.num_velocity_bins = num_velocity_bins
self.density_bin_ranges = density_bin_ranges
self.density_window_size = density_window_size
self.pitch_histogram_window_size = pitch_histogram_window_size
default_configs = {
'performance':
PerformanceRnnConfig(
magenta.protobuf.generator_pb2.GeneratorDetails(
id='performance', description='Performance RNN'),
magenta.music.OneHotEventSequenceEncoderDecoder(
performance_encoder_decoder.PerformanceOneHotEncoding()),
tf.contrib.training.HParams(batch_size=64,
rnn_layer_sizes=[512, 512, 512],
dropout_keep_prob=1.0,
clip_norm=3,
learning_rate=0.001)),
'performance_with_dynamics':
PerformanceRnnConfig(
magenta.protobuf.generator_pb2.GeneratorDetails(
id='performance_with_dynamics',
description='Performance RNN with dynamics'),
magenta.music.OneHotEventSequenceEncoderDecoder(
performance_encoder_decoder.PerformanceOneHotEncoding(
num_velocity_bins=32)),
tf.contrib.training.HParams(batch_size=64,
rnn_layer_sizes=[512, 512, 512],
def setUp(self):
self.config = performance_model.PerformanceRnnConfig(
None,
magenta.music.OneHotEventSequenceEncoderDecoder(
performance_encoder_decoder.PerformanceOneHotEncoding()),
tf.contrib.training.HParams())