def _hparams_from_flags():
keys = ("""
dataset quantization_level num_instruments separate_instruments
crop_piece_len architecture num_layers num_filters use_residual
batch_size maskout_method mask_indicates_context optimize_mask_only
rescale_loss patience corrupt_ratio eval_freq run_id
""".split())
hparams = lib_hparams.Hyperparameters(**dict(
(key, getattr(FLAGS, key)) for key in keys))
return hparams
def _hparams_from_flags():
"""Instantiate hparams based on flags set in FLAGS."""
keys = ("""
dataset quantization_level num_instruments separate_instruments
crop_piece_len architecture use_sep_conv num_initial_regular_conv_layers
sep_conv_depth_multiplier num_dilation_blocks dilate_time_only
num_layers num_filters use_residual
batch_size maskout_method mask_indicates_context optimize_mask_only
rescale_loss patience corrupt_ratio eval_freq run_id
""".split())
hparams = lib_hparams.Hyperparameters(**dict(
(key, getattr(FLAGS, key)) for key in keys))
return hparams
def save_checkpoint(self):
logdir = tempfile.mkdtemp()
save_path = os.path.join(logdir, 'model.ckpt')
hparams = lib_hparams.Hyperparameters(**{})
tf.gfile.MakeDirs(logdir)
config_fpath = os.path.join(logdir, 'config')
with tf.gfile.Open(config_fpath, 'w') as p:
hparams.dump(p)
with tf.Graph().as_default():
lib_graph.build_graph(is_training=True, hparams=hparams)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.save(sess, save_path)
return logdir
