Update DCGAN

DCGAN/modules/models/DCGAN.py

@@ -71,6 +71,9 @@ class DCGAN(keras.Model):
             loss_function : Loss function
         '''
         super(DCGAN, self).compile()
+        self.discriminator.compile(optimizer=discriminator_optimizer, loss=loss_function)
+        self.generator.compile(optimizer=generator_optimizer, loss=loss_function)
+        
         self.d_optimizer   = discriminator_optimizer
         self.g_optimizer   = generator_optimizer
         self.loss_fn       = loss_function
@@ -121,11 +124,11 @@ class DCGAN(keras.Model):
         combined_images = tf.concat( [generated_images, real_images], axis=0)
 
         # Creation of labels corresponding to real or fake images
-        # 1 is generated, 0 is real
-        labels = tf.concat( [tf.ones((batch_size, 1)),  tf.zeros((batch_size, 1))], axis=0)
+        # 0 is generated, 1 is real
+        labels = tf.concat( [tf.zeros((batch_size, 1)),  tf.ones((batch_size, 1))], axis=0)
 
         # Add random noise to the labels - important trick !
-        labels += 0.05 * tf.random.uniform(tf.shape(labels))
+#         labels += 0.05 * tf.random.uniform(tf.shape(labels))
 
         # ---- Train the discriminator -----------------------------
         # ----------------------------------------------------------
@@ -155,7 +158,7 @@ class DCGAN(keras.Model):
         random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
 
         # Assemble labels that say all images are real, yes it's a lie ;-)
-        misleading_labels = tf.zeros((batch_size, 1))
+        misleading_labels = tf.ones((batch_size, 1))
 
         # ---- Train the generator ---------------------------------
         # ----------------------------------------------------------