ajinkya98 / tensorboard_pytorch

A Complete Guide to Using TensorBoard with PyTorch

Link to article: https://medium.com/@ajinkya.pahinkar98/a-complete-guide-to-using-tensorboard-with-pytorch-53cb2301e8c3

tensorboard_pytorch's Issues

Article interpretation

Hei nice article
Your article was one of the first ones that show up when learning about Tensorboard so i though that could be useful for others to mention this

In your article you wrote:
t is evident from the results that a batch size of 32, shuffle set to True, and a learning rate of 0.01 yields the best result

Under my point of view this statement is incorrect as either looking at your figure or my own results, that is only considering the accuracy. Looking at your own figure the loss is extremely high what make me think that even at 5 epoch is not the best option.

How to use tensorbard to show previous logs

Hi Mr @ajinkya98
Thank you so much for your guide and your source code but a have a problem with tensorboard yet.
I trained my model for 50 epochs. Now I want to continue training for other 15 epochs. so I start training with model.load_from_checkpoint("path")
but I don't know how to show continuation of logs with tensorboard
I thought if I write same path of previous file logs, it continue but it didn't show me the previous logs.

I'd be appreciate if you could help me because I need to see the previous training logs along with the new ones.
Part of source code is:

checkpoint_callback=ModelCheckpoint(dirpath="model",filename="newmodel_1",save_last=True,verbose=True,monitor="val_ce_loss",mode="min")
logger=TensorBoardLogger("training-logs",name="test_1")
model1 = model(config)
model_with_previous = model1.load_from_checkpoint(base_path_ckpt)
trainer = pl.Trainer(
     max_steps = max_steps,
     max_epochs=N_EPOCHS,
     gpus=(1 if torch.cuda.is_available() else 0),
     logger=logger,
     callbacks=[checkpoint_callback], 
)
%load_ext tensorboard
%tensorboard --logdir ./lightning_logs