loss = loss_rec - current_alpha * loss_clf
reset_grad([self.Encoder, self.Decoder])
loss.backward()
grad_clip([self.Encoder, self.Decoder], self.hps.max_grad_norm)
self.ae_opt.step()
info = {
f'{flag}/loss_rec': loss_rec.item(),
f'{flag}/G_loss_clf': loss_clf.item(),
f'{flag}/alpha': current_alpha,
f'{flag}/G_acc': acc,
}
slot_value = (iteration + 1, hps.iters) + tuple(
[value for value in info.values()])
log = 'G:[%06d/%06d], loss_rec=%.3f, loss_clf=%.2f, alpha=%.2e, acc=%.2f'
print(log % slot_value)
if iteration % 100 == 0:
for tag, value in info.items():
self.logger.scalar_summary(tag, value, iteration + 1)
if iteration % 1000 == 0 or iteration + 1 == hps.iters:
self.save_model(model_path, iteration)
if __name__ == '__main__':
hps = Hps()
hps.load('./hps/v7.json')
hps_tuple = hps.get_tuple()
dataset = myDataset('/home/daniel/Documents/voice_integrador/vctk.h5',\
'/home/daniel/Documents/programacion/multitarget-voice-conversion-vctk/preprocess/speaker_id_by_gender.json')
data_loader = DataLoader(dataset)
solver = Solver(hps_tuple, data_loader)
all_result = model.predict_step(batch_x)
for result in all_result:
for word_idx in result:
f_out.write('{} '.format(word_idx))
f_out.write('\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-hps_path', default='./hps/cd_v3.json')
parser.add_argument('-vocab_path', default='/home/jjery2243542/datasets/summary/structured/26693_50_30/vocab.pkl')
parser.add_argument('-model_path', default='./model/model.ckpt-2999')
parser.add_argument('-dataset_path', default='/home/jjery2243542/datasets/summary/structured/26693_50_30/giga_40_10.h5')
parser.add_argument('-dataset_type', default='valid')
parser.add_argument('-output_path', default='result.txt')
args = parser.parse_args()
hps = Hps()
hps.load(args.hps_path)
hps_tuple = hps.get_tuple()
print(hps_tuple)
vocab = Vocab(args.vocab_path, args.dataset_path + '.unk.json')
data_generator = DataGenerator(args.dataset_path)
model = PointerModel(hps_tuple, vocab)
model.load_model(args.model_path)
dg = DataGenerator(args.dataset_path)
iterator = dg.iterator(
batch_size=hps_tuple.batch_size,
dataset_type=args.dataset_type,
infinite=False,
shuffle=False
)
predict(model, iterator, args.output_path)
def valid_step(self, batch_x, batch_y):
loss = self.sess.run(
self._log_loss,
feed_dict={self.x:batch_x, self.y:batch_y, self.kp:1.0}
)
return loss
def train_step(self, batch_x, batch_y, coverage=False):
if not coverage:
_, loss = self.sess.run(
[self._nll_opt, self._log_loss],
feed_dict={self.x:batch_x, self.y:batch_y, self.kp:self._hps.keep_prob}
)
else:
_, loss = self.sess.run(
[self._coverage_opt, self._coverage_loss],
feed_dict={self.x:batch_x, self.y:batch_y, self.kp:self._hps.keep_prob}
)
return loss
if __name__ == '__main__':
vocab = Vocab()
hps = Hps()
hps.load('./hps/cd_v3.json')
hps_tuple = hps.get_tuple()
model = PointerModel(hps_tuple, vocab)
model.init()
print('model build OK')
model.tt()