change readme

examples/README.md

@@ -10,7 +10,7 @@ Training examples with __reproducible__ and meaningful performance.
 
 ## Vision:
 + [A tiny SVHN ConvNet with 97.8% accuracy](svhn-digit-convnet.py)
-+ [Multi-GPU training of ResNet on ImageNet](ResNet)
++ Multi-GPU training of [ResNet](ResNet) and [ShuffleNet](ShuffleNet) on ImageNet
 + [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net)
 + [Generative Adversarial Network(GAN) variants](GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.
 + [Inception-BN and InceptionV3](Inception)

examples/ShuffleNet/README.md

@@ -4,7 +4,7 @@
 Reproduce [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083)
 on ImageNet.
 
-This is a 40MFlops ShuffleNet,
+This is a 40Mflops ShuffleNet,
 roughly corresponding to `ShuffleNet 0.5x (arch2)	g=8` in the paper.
 But detailed architecture may not be the same.
 After 100 epochs it reaches top-1 error of 42.62.
@@ -15,9 +15,9 @@ Print flops with tensorflow:
 ```bash
 ./shufflenet.py --flops
 ```
-It will print about 80MFlops, because TF counts FMA as 2 flops while the paper counts it as 1 flop.
+It will print about 80Mflops, because TF counts FMA as 2 flops while the paper counts it as 1 flop.
 
-Train:
+Train (takes 24 hours on 8 Maxwell TitanX):
 ```bash
 ./shufflenet.py --data /path/to/ilsvrc/
 ```