From a4ce2edb09f2c8d0b200b9f77f8df3fd89643b38 Mon Sep 17 00:00:00 2001 From: nunzip Date: Thu, 14 Mar 2019 13:05:42 +0000 Subject: Add artificial balancing --- ncdcgan.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) (limited to 'ncdcgan.py') diff --git a/ncdcgan.py b/ncdcgan.py index 97b137b..65c5862 100755 --- a/ncdcgan.py +++ b/ncdcgan.py @@ -148,7 +148,7 @@ class nCDCGAN(): model.summary() return model - def train(self, epochs, batch_size=128, sample_interval=50, graph=False, smooth_real=1, smooth_fake=0): + def train(self, epochs, batch_size=128, sample_interval=50, graph=False, smooth_real=1, smooth_fake=0, gdbal = 1): # Load the dataset (X_train, y_train), (_, _) = mnist.load_data() @@ -181,9 +181,12 @@ class nCDCGAN(): gen_imgs = self.generator.predict([noise, labels]) # Train the discriminator - d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid*smooth_real) - d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], valid*smooth_fake) - d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) + if epoch % gdbal == 0: + d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid*smooth_real) + d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], valid*smooth_fake) + d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) + else: + dloss = 0 # --------------------- # Train Generator -- cgit v1.2.3-54-g00ecf