plot_model

remove class

AirShower_WGAN/ShowerGAN.py

@@ -28,18 +28,6 @@ N = shower_maps.shape[0]
 utils.plot_multiple_signalmaps(shower_maps[:,:,:,0], log_dir=log_dir, title='Footprints', epoch='Real')
 
 
-
-class RandomWeightedAverage(_Merge):
-    """Takes a randomly-weighted average of two tensors. In geometric terms, this outputs a random point on the line
-    between each pair of input points.
-    Inheriting from _Merge is a little messy but it was the quickest solution I could think of.
-    Improvements appreciated."""
-
-    def _merge_function(self, inputs):
-        weights = K.random_uniform((BATCH_SIZE, 1, 1, 1))
-        return (weights * inputs[0]) + ((1 - weights) * inputs[1])
-
-
 # build generator
 def build_generator(latent_size):
     inputs = Input(shape=(latent_size,))    
@@ -77,9 +65,9 @@ def build_critic():
 
 
 generator = build_generator(latent_size)
-#plot_model(generator, to_file=log_dir + '/generator.png', show_shapes=True)
+print(generator.summary())
 critic = build_critic()
-#plot_model(critic, to_file=log_dir + '/critic.png', show_shapes=True)
+print(critic.summary())
 
 # make trainings model for generator
 utils.make_trainable(critic, False)
@@ -87,7 +75,7 @@ utils.make_trainable(generator, True)
 
 generator_training = utils.build_generator_graph(generator, critic, latent_size)
 generator_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0), loss=[utils.wasserstein_loss])
-#plot_model(generator_training, to_file=log_dir + '/generator_training.png', show_shapes=True)
+plot_model(generator_training, to_file=log_dir + '/generator_training.png', show_shapes=True)
 
 # make trainings model for critic
 utils.make_trainable(critic, True)
@@ -98,7 +86,7 @@ critic_training, averaged_batch = utils.build_critic_graph(generator, critic, la
 gradient_penalty = partial(utils.gradient_penalty_loss, averaged_batch=averaged_batch, penalty_weight=GRADIENT_PENALTY_WEIGHT)
 gradient_penalty.__name__ = 'gradient_penalty'
 critic_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0), loss=[utils.wasserstein_loss, utils.wasserstein_loss, gradient_penalty])
-#plot_model(critic_training, to_file=log_dir + '/critic_training.png', show_shapes=True)
+plot_model(critic_training, to_file=log_dir + '/critic_training.png', show_shapes=True)
 
 # For Wassersteinloss
 positive_y = np.ones(BATCH_SIZE)