gan3sh500 / mixmatch-pytorch Goto Github PK
View Code? Open in Web Editor NEWPytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)
Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)
did you get similar results to the paper?
Thanks for the implementation! Do let me know the test accuracy once u got it working!
When you try to use basic_generator to generate data, the first time is right, the shape is
(32, 32, 32, 3)
(32,)
But when you generate data again, the shape of data is
(32,32,3)
(32,32,3)
It will cause the following function receive wrong inputs, such as
return seq.augment(images.transpose(0, 2, 3, 1)).transpose(0, 2, 3, 1)
The shape of "images" is determined by the basic_generator.
Can you solve this problem?
Is there a complete code for the whole project on CIFAR10?
I also look forward to seeing the complete project!!!
indices in function basic_generator may be:
all_indices = np.arange(len(x))
if shuffle:
np.random.shuffle(all_indices)
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