diff options
Diffstat (limited to 'become_yukarin/updater.py')
| -rw-r--r-- | become_yukarin/updater.py | 24 |
1 files changed, 21 insertions, 3 deletions
diff --git a/become_yukarin/updater.py b/become_yukarin/updater.py index 927601f..02ea5d3 100644 --- a/become_yukarin/updater.py +++ b/become_yukarin/updater.py @@ -45,11 +45,27 @@ class Updater(chainer.training.StandardUpdater): if self.discriminator is not None: pair_fake = chainer.functions.concat([y * mask, input]) pair_true = chainer.functions.concat([target * mask, input]) + + # DRAGAN + if chainer.config.train: # grad is not available on test + std = xp.std(pair_true.data, axis=0, keepdims=True) + rand = xp.random.uniform(0, 1, pair_true.shape).astype(xp.float32) + perturb = chainer.Variable(pair_true.data + 0.5 * rand * std) + grad, = chainer.grad([self.discriminator(perturb)], [perturb], enable_double_backprop=True) + grad = chainer.functions.sqrt(chainer.functions.batch_l2_norm_squared(grad)) + loss_grad = chainer.functions.mean_squared_error(grad, xp.ones_like(grad.data, numpy.float32)) + reporter.report({'grad': loss_grad}, self.discriminator) + + if xp.any(xp.isnan(loss_grad.data)): + import code + code.interact(local=locals()) + + # GAN d_fake = self.discriminator(pair_fake) d_true = self.discriminator(pair_true) - loss_dis_f = chainer.functions.mean_squared_error(d_fake, xp.zeros_like(d_fake.data, numpy.float32)) - loss_dis_t = chainer.functions.mean_squared_error(d_true, xp.ones_like(d_true.data, numpy.float32)) - loss_gen_f = chainer.functions.mean_squared_error(d_fake, xp.ones_like(d_fake.data, numpy.float32)) + loss_dis_f = chainer.functions.average(chainer.functions.softplus(d_fake)) + loss_dis_t = chainer.functions.average(chainer.functions.softplus(-d_true)) + loss_gen_f = chainer.functions.average(chainer.functions.softplus(-d_fake)) reporter.report({'fake': loss_dis_f}, self.discriminator) reporter.report({'true': loss_dis_t}, self.discriminator) @@ -63,6 +79,8 @@ class Updater(chainer.training.StandardUpdater): loss['discriminator'] = \ loss_dis_f * self.loss_config.discriminator_fake + \ loss_dis_t * self.loss_config.discriminator_true + if chainer.config.train: # grad is not available on test + loss['discriminator'] += loss_grad * self.loss_config.discriminator_grad reporter.report({'loss': loss['discriminator']}, self.discriminator) loss['predictor'] += loss_gen_f * self.loss_config.predictor_fake |