Update improved_wgan.py

Correctly sets the gradient_penalty hyperparameter to match the paper…

examples/improved_wgan.py

@@ -55,7 +55,7 @@ def wasserstein_loss(y_true, y_pred):
     return K.mean(y_true * y_pred)
 
 
-def gradient_penalty_loss(y_true, y_pred, averaged_samples):
+def gradient_penalty_loss(y_true, y_pred, averaged_samples, gradient_penalty_weight):
     """Calculates the gradient penalty loss for a batch of "averaged" samples.
 
     In Improved WGANs, the 1-Lipschitz constraint is enforced by adding a term to the loss function
@@ -74,7 +74,7 @@ def gradient_penalty_loss(y_true, y_pred, averaged_samples):
     gradients = K.gradients(y_pred, averaged_samples)
     gradients = K.concatenate([K.flatten(tensor) for tensor in gradients])
     gradient_l2_norm = K.sqrt(K.sum(K.square(gradients)))
-    gradient_penalty = 2 * GRADIENT_PENALTY_WEIGHT * K.square(1 - gradient_l2_norm)
+    gradient_penalty = gradient_penalty_weight * K.square(1 - gradient_l2_norm)
     return gradient_penalty
 
 
@@ -215,10 +215,12 @@ averaged_samples = RandomWeightedAverage()([real_samples, generated_samples_for_
 # output for these samples - we're only running them to get the gradient norm for the gradient penalty loss.
 averaged_samples_out = discriminator(averaged_samples)
 
-# The gradient penalty loss function requires a list of trainable weights to get gradients on. However,
+# The gradient penalty loss function requires the input averaged samples to get gradients. However,
 # Keras loss functions can only have two arguments, y_true and y_pred. We get around this by making a partial()
-# of the function with the discriminator's trainable weights here.
-partial_gp_loss = partial(gradient_penalty_loss, averaged_samples=averaged_samples)
+# of the function with the averaged samples here.
+partial_gp_loss = partial(gradient_penalty_loss,
+                          averaged_samples=averaged_samples,
+                          gradient_penalty_weight=GRADIENT_PENALTY_WEIGHT)
 partial_gp_loss.__name__ = 'gradient_penalty'  # Functions need names or Keras will throw an error
 
 # Keras requires that inputs and outputs have the same number of samples. This is why we didn't concatenate the