add mkldnn tutorial to index.rst (PaddlePaddle#310)

lizexu123 · May 27, 2020 · cf4d540 · cf4d540
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diff --git a/demo/mkldnn_quant/quant_aware/PaddleCV_mkldnn_quantaware_tutorial_cn.md b/demo/mkldnn_quant/quant_aware/PaddleCV_mkldnn_quantaware_tutorial_cn.md
@@ -34,7 +34,7 @@ import numpy as np
 
 #### 2.1 量化训练
 
-量化训练流程可以参考 [分类模型的离线量化流程](https://paddlepaddle.github.io/PaddleSlim/tutorials/quant_aware_demo/)
+量化训练流程可以参考 [分类模型的量化训练流程](https://paddlepaddle.github.io/PaddleSlim/tutorials/quant_aware_demo/)
 
 **注意量化训练过程中config参数：**
 - **quantize_op_types:** 目前CPU上支持量化 `depthwise_conv2d`, `mul`, `conv2d`, `matmul`, `transpose2`, `reshape2`, `pool2d`, `scale`。但是训练阶段插入fake quantize/dequantize op时，只需在前四种op前后插入fake quantize/dequantize ops，因为后面四种op `matmul`, `transpose2`, `reshape2`, `pool2d`的输入输出scale不变，将从前后方op的输入输出scales获得scales,所以`quantize_op_types` 参数只需要 `depthwise_conv2d`, `mul`, `conv2d`, `matmul` 即可。

diff --git a/docs/zh_cn/tutorials/image_classification_mkldnn_quant_aware_tutorial.md b/docs/zh_cn/tutorials/image_classification_mkldnn_quant_aware_tutorial.md
@@ -1,4 +1,4 @@
-# CPU部署预测INT8模型的精度和性能
+# CPU部署预测INT8模型
 
 在Intel(R) Xeon(R) Gold 6271机器上，经过量化和DNNL加速，INT8模型在单线程上性能为原FP32模型的3~4倍；在 Intel(R) Xeon(R) Gold 6148，单线程性能为原FP32模型的1.5倍，而精度仅有极小下降。图像分类量化的样例教程请参考[图像分类INT8模型在CPU优化部署和预测](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/mkldnn_quant/quant_aware/PaddleCV_mkldnn_quantaware_tutorial_cn.md)。自然语言处理模型的量化请参考[ERNIE INT8 模型精度与性能复现](https://github.com/PaddlePaddle/benchmark/tree/master/Inference/c%2B%2B/ernie/mkldnn)
 

diff --git a/docs/zh_cn/tutorials/index.rst b/docs/zh_cn/tutorials/index.rst
@@ -6,6 +6,7 @@
    :maxdepth: 1
 
    image_classification_sensitivity_analysis_tutorial.md
+   image_classification_mkldnn_quant_aware_tutorial.md
    darts_nas_turorial.md
    paddledetection_slim_distillation_tutorial.md
    paddledetection_slim_nas_tutorial.md