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| author | Vasil Zlatanov <v@skozl.com> | 2019-03-05 14:32:38 +0000 |
|---|---|---|
| committer | Vasil Zlatanov <v@skozl.com> | 2019-03-05 14:32:38 +0000 |
| commit | 6b573a30a3021d259400af9751645eb1a5b4705b (patch) | |
| tree | 09d68aa13d19062369f5044bf246ddd89a579fdb /cgan.py | |
| parent | 740e1b0c6a02a7bec20008758373f0dd80baade4 (diff) | |
| parent | 2a720c237259baa2d968286244f9e43794c7e4d9 (diff) | |
| download | e4-gan-6b573a30a3021d259400af9751645eb1a5b4705b.tar.gz e4-gan-6b573a30a3021d259400af9751645eb1a5b4705b.tar.bz2 e4-gan-6b573a30a3021d259400af9751645eb1a5b4705b.zip | |
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Diffstat (limited to 'cgan.py')
| -rw-r--r-- | cgan.py | 6 |
1 files changed, 3 insertions, 3 deletions
@@ -113,7 +113,7 @@ 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_real=1, smooth_fake=0): # Load the dataset (X_train, y_train), (_, _) = mnist.load_data() @@ -147,8 +147,8 @@ class CGAN(): gen_imgs = self.generator.predict([noise, labels]) # Train the discriminator - 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) # --------------------- |