From f00bc97bcb820d30d73fed37eb5c0d5ffddcd9ca Mon Sep 17 00:00:00 2001 From: nunzip Date: Mon, 4 Mar 2019 21:06:27 +0000 Subject: Add One-sided smoothing --- cgan.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'cgan.py') diff --git a/cgan.py b/cgan.py index 5ab0c10..880a8b8 100755 --- a/cgan.py +++ b/cgan.py @@ -107,7 +107,7 @@ class CGAN(): return Model([img, label], validity) - def train(self, epochs, batch_size=128, sample_interval=50, graph=False): + def train(self, epochs, batch_size=128, sample_interval=50, graph=False, smooth=False): # Load the dataset (X_train, y_train), (_, _) = mnist.load_data() @@ -140,7 +140,10 @@ class CGAN(): gen_imgs = self.generator.predict([noise, labels]) # Train the discriminator - d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid) + if smooth == True: + d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid*0.9) + else: + d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid) d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], fake) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) -- cgit v1.2.3-54-g00ecf