implement VAE #20
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implement VAE #20
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colesbury
reviewed
Dec 22, 2016
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| class VAE(nn.Container): | ||
| def __init__(self): | ||
| super().__init__() |
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| def train(epoch): | ||
| batch_data_t = torch.FloatTensor(BATCH_SIZE, 784) | ||
| if cuda: |
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| def test(epoch): | ||
| test_loss = 0 |
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| batch_data_t = torch.FloatTensor(BATCH_SIZE, 784) | ||
| if cuda: | ||
| batch_data_t = batch_data_t.cuda() | ||
| batch_data = Variable(batch_data_t, requires_grad=False) |
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| batch_data_t = batch_data_t.cuda() | ||
| batch_data = Variable(batch_data_t, volatile=True) | ||
| for i in range(0, test_data.size(0), TEST_BATCH_SIZE): | ||
| print('Testing model: {}/{}'.format(i, test_data.size(0)), end='\r') |
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…ll on model, general clean up
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@colesbury I addressed your comments and incorporated a dataloader. Thanks for the feedback! |
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thanks Joost! :) |
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This PR implements the VAE. It aims to use the most recent recommended way of defining and running models in PyTorch.
Please let me know if there is any way to make the code more torchy and I'll gladly update my PR.