pytorch查看loss曲线_pytorch 如何使用tensorboard实时查看曲线---- tensorboardX简介
classTacotron2Logger(SummaryWriter):def __init__(self, logdir):
super(Tacotron2Logger, self).__init__(logdir)deflog_training(self, reduced_loss, grad_norm, learning_rate, duration,
iteration):
self.add_scalar("training.loss", reduced_loss, iteration)
self.add_scalar("grad.norm", grad_norm, iteration)
self.add_scalar("learning.rate", learning_rate, iteration)
self.add_scalar("duration", duration, iteration)deflog_validation(self, reduced_loss, model, y, y_pred, iteration):
self.add_scalar("validation.loss", reduced_loss, iteration)
_, mel_outputs, gate_outputs, alignments=y_pred
mel_targets, gate_targets=y#plot distribution of parameters
for tag, value inmodel.named_parameters():
tag= tag.replace('.', '/')
self.add_histogram(tag, value.data.cpu().numpy(), iteration)#plot alignment, mel target and predicted, gate target and predicted
idx = random.randint(0, alignments.size(0) - 1)
self.add_image("alignment",
plot_alignment_to_numpy(alignments[idx].data.cpu().numpy().T),
iteration)
self.add_image("mel_target",
plot_spectrogram_to_numpy(mel_targets[idx].data.cpu().numpy()),
iteration)
self.add_image("mel_predicted",
plot_spectrogram_to_numpy(mel_outputs[idx].data.cpu().numpy()),
iteration)
self.add_image("gate",
plot_gate_outputs_to_numpy(
gate_targets[idx].data.cpu().numpy(),
F.sigmoid(gate_outputs[idx]).data.cpu().numpy()),
iteration)