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authornunzip <np.scarh@gmail.com>2019-03-04 21:53:18 +0000
committernunzip <np.scarh@gmail.com>2019-03-04 21:53:18 +0000
commit34057507d6b7ae5cafd2b7b8cb2b69c20780ffd5 (patch)
treea6cfd5a1f624674e010f8ca897ef63ef8eb226bd
parent6529cc095c57e375f34d69fb6bfb36d058dd2192 (diff)
downloade4-gan-34057507d6b7ae5cafd2b7b8cb2b69c20780ffd5.tar.gz
e4-gan-34057507d6b7ae5cafd2b7b8cb2b69c20780ffd5.tar.bz2
e4-gan-34057507d6b7ae5cafd2b7b8cb2b69c20780ffd5.zip
Make single sided smoothing parameters accessible
-rwxr-xr-xcgan.py10
1 files changed, 3 insertions, 7 deletions
diff --git a/cgan.py b/cgan.py
index ebdfab2..6406244 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, smooth=False):
+ def train(self, epochs, batch_size=128, sample_interval=50, graph=False, smooth_real=1, smooth_fake=0):
# Load the dataset
(X_train, y_train), (_, _) = mnist.load_data()
@@ -140,12 +140,8 @@ class CGAN():
gen_imgs = self.generator.predict([noise, labels])
# Train the discriminator
- if smooth == True:
- d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid*0.9)
- d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], valid*0.1)
- 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_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)
# ---------------------