Refine minist unsuper.
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@ -20,8 +20,8 @@ np.random.seed(seed)
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random.seed(seed)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# device = torch.device("cpu")
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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device = torch.device("cpu")
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# device = torch.device("mps")
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num_epochs = 1
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@ -128,8 +128,8 @@ for epoch in range(epochs):
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all = range(0, sample.shape[0])
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# ratio_max = abs / mean
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# ratio_nor = (max - abs) / max
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ratio_nor = torch.pow(abs / mean, 4)
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# ratio_nor[all, max_index] = ratio_max[all, max_index].clone()
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ratio_nor = torch.pow(abs / mean, 4)
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ratio_nor = torch.where(torch.isnan(ratio_nor), 1.0, ratio_nor)
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label = sample * ratio_nor
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@ -197,7 +197,6 @@ with torch.no_grad():
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labels = labels.to(device)
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outputs = model(images)
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# max returns (value ,index)
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_, predicted = torch.max(outputs.data, 1)
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n_samples += labels.size(0)
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n_correct += (predicted == labels).sum().item()
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