Adding fused_batch_norm parameter

research/gan/cifar/networks.py

@@ -46,7 +46,7 @@ def generator(noise, is_training=True):
   Returns:
     A single Tensor with a batch of generated CIFAR images.
   """
-  images, _ = dcgan.generator(noise, is_training=is_training)
+  images, _ = dcgan.generator(noise, is_training=is_training, fused_batch_norm=True)
 
   # Make sure output lies between [-1, 1].
   return tf.tanh(images)
@@ -68,7 +68,7 @@ def conditional_generator(inputs, is_training=True):
   noise, one_hot_labels = inputs
   noise = tfgan.features.condition_tensor_from_onehot(noise, one_hot_labels)
 
-  images, _ = dcgan.generator(noise, is_training=is_training)
+  images, _ = dcgan.generator(noise, is_training=is_training, fused_batch_norm=True)
 
   # Make sure output lies between [-1, 1].
   return tf.tanh(images)
@@ -94,7 +94,7 @@ def discriminator(img, unused_conditioning, is_training=True):
     images are real. The output can lie in [-inf, inf], with positive values
     indicating high confidence that the images are real.
   """
-  logits, _ = dcgan.discriminator(img, is_training=is_training)
+  logits, _ = dcgan.discriminator(img, is_training=is_training, fused_batch_norm=True)
   return logits
 
 
@@ -118,7 +118,7 @@ def conditional_discriminator(img, conditioning, is_training=True):
     images are real. The output can lie in [-inf, inf], with positive values
     indicating high confidence that the images are real.
   """
-  logits, end_points = dcgan.discriminator(img, is_training=is_training)
+  logits, end_points = dcgan.discriminator(img, is_training=is_training, fused_batch_norm=True)
 
   # Condition the last convolution layer.
   _, one_hot_labels = conditioning