coqui-ai
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TTS
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Remove tabs from logging

pull/10/head
Eren G 2018-07-12 18:09:02 +02:00
parent f7f424c863
commit 5bf3ff345f
1 changed files with 9 additions and 9 deletions
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18
train.py
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@ -150,9 +150,9 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
# ('grad_norm_st', grad_norm_st.item())])
if current_step % c.print_step == 0:
print(" | | > Step:{}\tGlobalStep:{}\tTotalLoss:{:.5f}\tLinearLoss:{:.5f}\tMelLoss:\
{:.5f}\tStopLoss:{:.5f}\tGradNorm:{:.5f}\t\
GradNormST:{:.5f}\tStepTime:{:.2f}".format(num_iter, current_step,
print(" | | > Step:{} GlobalStep:{} TotalLoss:{:.5f} LinearLoss:{:.5f} MelLoss:\
{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} \
GradNormST:{:.5f} StepTime:{:.2f}".format(num_iter, current_step,
loss.item(),
linear_loss.item(),
mel_loss.item(),
@ -215,9 +215,9 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
avg_total_loss = avg_mel_loss + avg_linear_loss + avg_stop_loss
# print epoch stats
print(" | | > EPOCH END -- GlobalStep:{}\tAvgTotalLoss:{:.5f}\t\
AvgLinearLoss:{:.5f}\tAvgMelLoss:{:.5f}\t\
AvgStopLoss:{:.5f}\tEpochTime:{:.2f}".format(current_step,
print(" | | > EPOCH END -- GlobalStep:{} AvgTotalLoss:{:.5f} \
AvgLinearLoss:{:.5f} AvgMelLoss:{:.5f} \
AvgStopLoss:{:.5f} EpochTime:{:.2f}".format(current_step,
avg_total_loss,
avg_linear_loss,
avg_mel_loss,
@ -290,8 +290,8 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step):
# ('mel_loss', mel_loss.item()),
# ('stop_loss', stop_loss.item())])
if current_step % c.print_step == 0:
print(" | | > TotalLoss: {:.5f}\t LinearLoss: {:.5f}\t MelLoss: \
{:.5f}\t StopLoss: {:.5f}\t".format(loss.item(),
print(" | | > TotalLoss: {:.5f} LinearLoss: {:.5f} MelLoss: \
{:.5f} StopLoss: {:.5f} ".format(loss.item(),
linear_loss.item(),
mel_loss.item(),
stop_loss.item()))
@ -434,7 +434,7 @@ def main(args):
train_loss, current_step = train(
model, criterion, criterion_st, train_loader, optimizer, optimizer_st, epoch)
val_loss = evaluate(model, criterion, criterion_st, val_loader, current_step)
print(" | > Train Loss: {:.5f}\t Validation Loss: {:.5f}".format(train_loss, val_loss))
print(" | > Train Loss: {:.5f} Validation Loss: {:.5f}".format(train_loss, val_loss))
best_loss = save_best_model(model, optimizer, val_loss,
best_loss, OUT_PATH,
current_step, epoch)