fix cifar preprocessing

examples/GAN/DiscoGAN-CelebA.py

@@ -220,7 +220,7 @@ if __name__ == '__main__':
         dataflow=data,
         callbacks=[ModelSaver()],
         steps_per_epoch=300,
-        max_epoch=1000,
+        max_epoch=250,
         session_init=SaverRestore(args.load) if args.load else None
     )
 

examples/ResNet/cifar10-resnet.py

@@ -22,7 +22,7 @@ Identity Mappings in Deep Residual Networks, arxiv:1603.05027
 
 I can reproduce the results on 2 TitanX for
 n=5, about 7.1% val error after 67k steps (20.4 step/s)
-n=18, about 5.95% val error after 80k steps (5.6 step/s)
+n=18, about 5.95% val error after 80k steps (5.6 step/s, not converged)
 n=30: a 182-layer network, about 5.6% val error after 51k steps (3.4 step/s)
 This model uses the whole training set instead of a train-val split.
 
@@ -46,7 +46,7 @@ class Model(ModelDesc):
 
     def _build_graph(self, inputs):
         image, label = inputs
-        image = image / 128.0 - 1
+        image = image / 128.0
         image = tf.transpose(image, [0, 3, 1, 2])
 
         def residual(name, l, increase_dim=False, first=False):