enc_dropout=params['enc_dropout'],
dec_rnn_dim=params['dec_rnn_dim'],
dec_dropout=params['dec_dropout'],
cont_dim=params['cont_dim'],
cat_dim=args.cat_dim,
mu_force=1.3,
t_gumbel=params['t_gumbel'],
style_embed_dim=params['style_dim'],
kl_reg=params['kl_reg'],
beta_anneal_steps=params['beta_anneal_steps'],
rnn_type=params['rnn_type'],
attention=params['attention'])
if args.restore_path:
print('restoring weights from ' + args.restore_path)
vae.load_weights(args.restore_path)
train_segments = []
test_segments = []
datasets = [
"datasets/dataset-8/JSB-8-raw.pickle",
"datasets/dataset-8/NMD-8-raw.pickle"
]
keep_pcts = [1, 0.23]
master_pct = 1
for (dataset, pct) in zip(datasets, keep_pcts):
segments = joblib.load(dataset)
test_size = 0.1 * master_pct
train_size = (1 - test_size) * master_pct
Xtr, Xte = train_test_split(segments,
train_size=train_size * pct,
def create_model_and_train(train_segments,
test_segments,
x_depth,
batch_size,
enc_rnn_dim,
dec_rnn_dim,
enc_dropout,
dec_dropout,
cont_dim,
cat_dim,
mu_force,
t_gumbel,
style_embed_dim,
kl_reg,
beta_anneal_steps,
rnn_type,
attention,
save_path,
start_epoch,
final_epoch,
weights=None):
train_iterator = load_noteseqs(train_segments, x_depth,
batch_size).get_iterator()
test_iterator = load_noteseqs(test_segments, x_depth,
batch_size).get_iterator()
vae = MVAE(x_depth=x_depth,
enc_rnn_dim=enc_rnn_dim,
enc_dropout=enc_dropout,
dec_rnn_dim=dec_rnn_dim,
dec_dropout=dec_dropout,
cont_dim=cont_dim,
cat_dim=cat_dim,
mu_force=mu_force,
t_gumbel=t_gumbel,
style_embed_dim=style_embed_dim,
kl_reg=kl_reg,
beta_anneal_steps=beta_anneal_steps,
rnn_type=rnn_type,
attention=attention)
optimizer = tfk.optimizers.Adam(learning_rate=5e-4)
vae.compile(optimizer=optimizer)
vae.run_eagerly = True
save_path = save_path
if (os.path.exists(save_path) == False):
os.makedirs(save_path)
callbacks = [
tfk.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, _: generate_and_save_samples(
vae, epoch, save_path, cat_dim)),
tfk.callbacks.LambdaCallback(
on_epoch_start=lambda epoch, _: vae.reset_trackers()),
tfk.callbacks.EarlyStopping(monitor='val_p_acc',
min_delta=0.01,
patience=5,
mode='max'),
tfk.callbacks.CSVLogger(save_path + 'log.csv', append=True),
tfk.callbacks.ModelCheckpoint(save_path + 'weights/' +
'/weights.{epoch:02d}',
monitor='val_p_acc',
save_weights_only=True,
save_best_only=True,
mode='max'),
tfk.callbacks.TensorBoard(log_dir=save_path,
write_graph=True,
update_freq='epoch',
histogram_freq=40,
profile_batch='10,20')
]
if weights != None:
vae.load_weights(save_path + weights)
history = vae.fit(train_iterator,
epochs=final_epoch,
initial_epoch=start_epoch,
callbacks=callbacks,
validation_data=test_iterator)
vae.save_weights(save_path + 'weights/weights-final')
return history