found a bug. numbers are not good enough.

examples/Atari2600/README.md

@@ -15,8 +15,10 @@ Claimed performance in the paper can be reproduced, on several games I've tested
 
 A demo trained with Double-DQN on breakout game is available at [youtube](https://youtu.be/o21mddZtE5Y).
 
-DQN would typically take 2~3 days of training to reach a score of 400 on breakout, but it only takes <4 hours on 1 GPU with my A3C implementation.
-This is probably the fastest RL trainer you'd find.
+<!--
+   -DQN would typically take 2~3 days of training to reach a score of 400 on breakout, but my A3C implementation only takes <4 hours on 1 GPU.
+   -This is probably the fastest RL trainer you'd find.
+	 -->
 
 ## How to use
 

examples/Inception/inception-bn.py

@@ -21,7 +21,7 @@ INPUT_SHAPE = 224
 Inception-BN model on ILSVRC12.
 See "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift", arxiv:1502.03167
 
-This config reaches 71% single-crop validation error after 300k steps with 6 TitanX.
+This config reaches 71% single-crop validation accuracy after 300k steps with 6 TitanX.
 Learning rate may need a different schedule for different number of GPUs (because batch size will be different).
 """
 
@@ -171,9 +171,9 @@ def get_config():
                 ClassificationError('wrong-top5', 'val-top5-error')]),
             #HumanHyperParamSetter('learning_rate', 'hyper-googlenet.txt')
             ScheduledHyperParamSetter('learning_rate',
-                                      [(8, 0.03), (13, 0.02), (16, 5e-3),
-                                       (18, 3e-3), (24, 1e-3), (26, 2e-4),
-                                       (28, 5e-5) ])
+                                      [(8, 0.03), (14, 0.02), (17, 5e-3),
+                                       (19, 3e-3), (24, 1e-3), (26, 2e-4),
+                                       (30, 5e-5) ])
         ]),
         session_config=sess_config,
         model=Model(),