3 files changed, 6 insertions, 6 deletions
diff --git a/cgan.py b/cgan.py
index b9928f0..45b9bb9 100644
--- a/cgan.py
+++ b/cgan.py
@@ -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)
 
             # ---------------------
diff --git a/dcgan.py b/dcgan.py
index eca1852..8a1bc2b 100644
--- a/dcgan.py
+++ b/dcgan.py
@@ -124,7 +124,7 @@ class DCGAN():
 
         return Model(img, validity)
 
-    def train(self, epochs, batch_size=128, save_interval=50):
+    def train(self, epochs, batch_size=128, save_interval=50, VBN=False):
 
         # Load the dataset
         (X_train, _), (_, _) = mnist.load_data()
@@ -139,6 +139,7 @@ class DCGAN():
         
         xaxis = np.arange(epochs)
         loss = np.zeros((2,epochs))
+
         for epoch in tqdm(range(epochs)):
 
             # ---------------------
@@ -150,7 +151,6 @@ class DCGAN():
             imgs = X_train[idx]
 
             tf.keras.backend.get_session().run(tf.global_variables_initializer()) 
-
             # Sample noise and generate a batch of new images
             noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
             gen_imgs = self.generator.predict(noise)
diff --git a/lenet.py b/lenet.py
index a38f4e1..c1c27b5 100644
--- a/lenet.py
+++ b/lenet.py
@@ -125,8 +125,8 @@ def test_classifier(model, x_test, y_true):
   y_pred = model.predict(x_test)
   y_pred = np.argmax(y_pred, axis=1)
   y_true = np.argmax(y_true, axis=1)
-  print("Test acc:", accuracy_score(y_true, y_pred))
   plot_example_errors(y_pred, y_true, x_test)
+  return accuracy_score(y_true, y_pred)
 
 def mix_data(X_train, y_train, X_validation, y_validation, train_gen, tr_labels_gen, val_gen, val_labels_gen, split=0):