add rounding readme.md

model_compression/rounding/README.md

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+# Rounding 
+
+###  Rounding test of Mobilenet(1.0,  224 * 224 input) on flowers102 dataset.
+
+| Model| Rounding | Without BN | Pruning| modelSize(gzip)|Accuracy | Download|
+| ---- | --- | ---- | ---  | --- | --- | ---|
+|Mobilenet ||  | |12M|97.16%|[download](https://pan.baidu.com/s/1geHkrw3)|
+|Mobilenet|√|||3.2M|97.05%|[download](https://pan.baidu.com/s/1bo5kqsR)|
+|Mobilenet|||√|4.4M|96.96%|[download](https://pan.baidu.com/s/1ge8wOp1)|
+|Mobilenet|√||√|2.3M|96.7%|[download](https://pan.baidu.com/s/1bpo5CMr)|
+|Mobilenet||√||12M|97.16%|[download](https://pan.baidu.com/s/1slodbNR)|
+|Mobilenet|√|√||3.0M|96.96%|[download](https://pan.baidu.com/s/1bo66hUR)|
+
+
+
+## Principle
+
+Given floating parameters `V`, first our goal is to represent `V` as 8-bit integers `V'`. And then we transformed back `V'` back into its approximate high-precision value by performing the inverse of the quantization operation. At last, we perform gzip to our quantized && inverse-quantized model. The whole process can reduces our model by 70%.
+
+### Process
+- First, we use UInt8 quantization, that is, the parameters are sacled to [0, 255]    
+ ![](../../source/rounding1.png)
+
+- Second, we inverse the quantization.      
+	 ![](../../source/rounding2.png)
+
+In the last, We apply gzip to compress the inverse-quantized model, and the compression ratio can be up to 70%.
+