def train_tvae(seq_file, train_list, val_list,
decoder_file, qy_file, qz_file,
epochs, batch_size,
preproc_file, output_path,
num_samples_y, num_samples_z,
px_form, qy_form, qz_form,
min_kl, **kwargs):
set_float_cpu(float_keras())
sr_args = SR.filter_args(**kwargs)
sr_val_args = SR.filter_val_args(**kwargs)
opt_args = KOF.filter_args(**kwargs)
cb_args = KCF.filter_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SR(seq_file, train_list, batch_size=batch_size,
preproc=preproc, **sr_args)
max_length = sr.max_batch_seq_length
gen_val = None
if val_list is not None:
sr_val = SR(seq_file, val_list, batch_size=batch_size,
preproc=preproc,
shuffle_seqs=False,
seq_split_mode='sequential', seq_split_overlap=0,
reset_rng=True,
**sr_val_args)
max_length = max(max_length, sr_val.max_batch_seq_length)
gen_val = data_generator(sr_val, max_length)
gen_train = data_generator(sr, max_length)
t1 = time.time()
decoder = load_model_arch(decoder_file)
qy = load_model_arch(qy_file)
if qz_file is None:
vae = TVAEY(qy, decoder, px_cond_form=px_form,
qy_form=qy_form, min_kl=min_kl)
vae.build(num_samples=num_samples_y,
max_seq_length = max_length)
else:
qz = load_model_arch(qz_file)
vae = TVAEYZ(qy, qz, decoder, px_cond_form=px_form,
qy_form=qy_form, qz_form=qz_form, min_kl=min_kl)
vae.build(num_samples_y=num_samples_y, num_samples_z=num_samples_z,
max_seq_length = max_length)
logging.info(time.time()-t1)
cb = KCF.create_callbacks(vae, output_path, **cb_args)
opt = KOF.create_optimizer(**opt_args)
h = vae.fit_generator(gen_train, x_val=gen_val,
steps_per_epoch=sr.num_batches,
validation_steps=sr_val.num_batches,
optimizer=opt, epochs=epochs,
callbacks=cb, max_q_size=10)
# if vae.x_chol is not None:
# x_chol = np.array(K.eval(vae.x_chol))
# logging.info(x_chol[:4,:4])
logging.info('Train elapsed time: %.2f' % (time.time() - t1))
vae.save(output_path + '/model')
def train_tvae(seq_file, train_list, val_list, gmm_file, decoder_file, qy_file,
qz_file, init_path, epochs, batch_size, preproc_file,
output_path, num_samples_y, num_samples_z, px_form, qy_form,
qz_form, min_kl, **kwargs):
set_float_cpu(float_keras())
sr_args = SR.filter_args(**kwargs)
sr_val_args = SR.filter_val_args(**kwargs)
opt_args = KOF.filter_args(**kwargs)
cb_args = KCF.filter_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
gmm = DiagGMM.load_from_kaldi(gmm_file)
sr = SR(seq_file,
train_list,
batch_size=batch_size,
preproc=preproc,
**sr_args)
max_length = sr.max_batch_seq_length
gen_val = None
if val_list is not None:
sr_val = SR(seq_file,
val_list,
batch_size=batch_size,
preproc=preproc,
shuffle_seqs=False,
seq_split_mode='sequential',
seq_split_overlap=0,
reset_rng=True,
**sr_val_args)
max_length = max(max_length, sr_val.max_batch_seq_length)
gen_val = data_generator(sr_val, gmm, max_length)
gen_train = data_generator(sr, gmm, max_length)
t1 = time.time()
if init_path is None:
decoder = load_model_arch(decoder_file)
qy = load_model_arch(qy_file)
# if qz_file is None:
# vae = TVAEY(qy, decoder, px_cond_form=px_form,
# qy_form=qy_form, min_kl=min_kl)
# vae.build(num_samples=num_samples_y,
# max_seq_length = max_length)
# else:
qz = load_model_arch(qz_file)
vae = TVAEYZ(qy,
qz,
decoder,
px_cond_form=px_form,
qy_form=qy_form,
qz_form=qz_form,
min_kl=min_kl)
else:
vae = TVAEYZ.load(init_path)
vae.build(num_samples_y=num_samples_y,
num_samples_z=num_samples_z,
max_seq_length=max_length)
logging.info(time.time() - t1)
cb = KCF.create_callbacks(vae, output_path, **cb_args)
opt = KOF.create_optimizer(**opt_args)
h = vae.fit_generator(gen_train,
x_val=gen_val,
steps_per_epoch=sr.num_batches,
validation_steps=sr_val.num_batches,
optimizer=opt,
epochs=epochs,
callbacks=cb,
max_queue_size=10)
# if vae.x_chol is not None:
# x_chol = np.array(K.eval(vae.x_chol))
# logging.info(x_chol[:4,:4])
logging.info('Train elapsed time: %.2f' % (time.time() - t1))
vae.save(output_path + '/model')
sr_val.reset()
y_val, sy_val, z_val, srz_val = vae.encoder_net.predict_generator(
gen_val, steps=400)
from scipy import linalg as la
yy = y_val - np.mean(y_val, axis=0)
cy = np.dot(yy.T, yy) / yy.shape[0]
l, v = la.eigh(cy)
np.savetxt(output_path + '/l1.txt', l)
sr_val.reset()
y_val2, sy_val2 = vae.qy_net.predict_generator(gen_val, steps=400)
yy = y_val2 - np.mean(y_val, axis=0)
cy = np.dot(yy.T, yy) / yy.shape[0]
l, v = la.eigh(cy)
np.savetxt(output_path + '/l2.txt', l)
logging.info(y_val - y_val2)