Add One-sided smoothing

1 files changed, 5 insertions, 2 deletions

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)