How to save and visualize metrics during the training process in PyTorch.
In PyTorch, you can save and visualize metrics during the training process by using the TensorboardX library. Here is a simple example code showing how to save and visualize loss and accuracy metrics during training in PyTorch.
from torch.utils.tensorboard import SummaryWriter
# 创建一个TensorboardX写入器
writer = SummaryWriter()
# 在训练过程中保存损失值和准确率指标
for i in range(num_epochs):
# 在每个epoch内计算损失值和准确率
loss = ...
accuracy = ...
# 将损失值和准确率写入TensorboardX
writer.add_scalar('Loss', loss, i)
writer.add_scalar('Accuracy', accuracy, i)
# 打印训练进度
print(f'Epoch {i+1}/{num_epochs}, Loss: {loss}, Accuracy: {accuracy}')
# 关闭TensorboardX写入器
writer.close()
In the example code above, we first import the SummaryWriter class, and then create a SummaryWriter object. During the training process, we calculate the loss and accuracy within each epoch, and use the add_scalar method to write them to TensorboardX. Finally, remember to close the TensorboardX writer when training is finished. To view the visualized metrics during training, you can start the Tensorboard service by running tensorboard –logdir=runs in the terminal and then viewing the results in a browser.