diff --git a/training/training_loop.py b/training/training_loop.py
index 9a652093e2f783e4c1e80c605c98644597e76534..a31d9edabdaf94077e45cc343286cc25cae276bd 100755
--- a/training/training_loop.py
+++ b/training/training_loop.py
@@ -38,32 +38,13 @@ def _random_choice(inputs, n_samples=1):
 
 def apply_random_aug(x):
     with tf.name_scope('SpatialAugmentations'):
-        choice = tf.random_uniform([], 0, 2, tf.int32)
+        choice = tf.random_uniform([], 0, 6, tf.int32)
         x = tf.cond(tf.reduce_all(tf.equal(choice, tf.constant(0))), lambda: misc.zoom_in(x), lambda: tf.identity(x))
         x = tf.cond(tf.reduce_all(tf.equal(choice, tf.constant(1))), lambda: misc.zoom_out(x), lambda: tf.identity(x))
         x = tf.cond(tf.reduce_all(tf.equal(choice, tf.constant(2))), lambda: misc.X_translate(x), lambda: tf.identity(x))
         x = tf.cond(tf.reduce_all(tf.equal(choice, tf.constant(3))), lambda: misc.Y_translate(x), lambda: tf.identity(x))
         x = tf.cond(tf.reduce_all(tf.equal(choice, tf.constant(4))), lambda: misc.XY_translate(x), lambda: tf.identity(x))
         x = tf.cond(tf.reduce_all(tf.equal(choice, tf.constant(5))), lambda: misc.random_cutout(x), lambda: tf.identity(x))
-
-        # if :
-        #     print('zooming in')
-        #     x = misc.zoom_in(x)
-        # elif tf.reduce_all(tf.equal(choice, tf.constant(0))):
-        #     print('zooming out')
-        #     x = misc.zoom_out(x)
-        # elif choice == 2:
-        #     print('x trans')
-        #     x = misc.X_translate(x)
-        # elif choice == 3:
-        #     print('y trans')
-        #     x = misc.Y_translate(x)
-        # elif choice == 4:
-        #     print('xy trans')
-        #     x = misc.XY_translate(x)
-        # elif choice == 5:
-        #     print('cutout')
-        #     x = misc.random_cutout(x)
         return x
 
 def process_reals(x, labels, lod, mirror_augment, mirror_augment_v, spatial_augmentations, drange_data, drange_net, dshape):