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View Code? Open in Web Editor NEWVirtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976
Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976
Is there a reason for using a single loop training model compared to a
for epoch in range(n_epochs):
for iter in range(n_iters):
train.....
I understand that it may not be trivial to get the number of iterations when there are two data loaders with different sizes of samples. But any explanation is really appreciated.
In data_loader.py, when finding instances with no labels, you have used following command:
unlabels_set = list(set(range(len(train_dataset))) - set(val_set))
shouldn't you consider indexes in train set here as well?
unlabels_set = list(set(range(len(train_dataset))) - set(val_set)-set(labels_set))
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