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neural-enhance-super-resolution
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Add gradient clipping, helpful for preventing problems with extreme parameters/architectures.

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Alex J. Champandard 9 years ago
parent f2494f8078
commit c610623b11
1 changed files with 2 additions and 1 deletions
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enhance.py
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@ -428,7 +428,8 @@ class Model(object):
disc_losses = [self.loss_discriminator(disc_out)] disc_losses = [self.loss_discriminator(disc_out)]
disc_params = list(itertools.chain(*[l.get_params() for k, l in self.network.items() if 'disc' in k])) disc_params = list(itertools.chain(*[l.get_params() for k, l in self.network.items() if 'disc' in k]))
print(' - {} tensors learned for discriminator.'.format(len(disc_params))) print(' - {} tensors learned for discriminator.'.format(len(disc_params)))
disc_updates = lasagne.updates.adam(sum(disc_losses, 0.0), disc_params, learning_rate=self.disc_lr) grads = T.grad(sum(disc_losses, 0.0), disc_params).clip(-1.0, 1.0)
disc_updates = lasagne.updates.adam(grads, disc_params, learning_rate=self.disc_lr)
# Combined Theano function for updating both generator and discriminator at the same time. # Combined Theano function for updating both generator and discriminator at the same time.
updates = collections.OrderedDict(list(gen_updates.items()) + list(disc_updates.items())) updates = collections.OrderedDict(list(gen_updates.items()) + list(disc_updates.items()))

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