Make sure the generator training step gets a full batch of noise even if the data loader just ran out.
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040d553d2a
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3
main.py
3
main.py
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@ -203,6 +203,9 @@ for epoch in range(opt.niter):
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for p in netD.parameters():
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for p in netD.parameters():
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p.requires_grad = False # to avoid computation
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p.requires_grad = False # to avoid computation
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netG.zero_grad()
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netG.zero_grad()
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# in case our last batch was the tail batch of the dataloader,
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# make sure we feed a full batch of noise
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noise.data.resize_(opt.batchSize, nz, 1, 1)
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noise.data.normal_(0, 1)
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noise.data.normal_(0, 1)
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fake = netG(noise)
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fake = netG(noise)
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errG = netD(fake)
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errG = netD(fake)
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