add doc

fluid/PaddleCV/image_classification/README.md

@@ -6,6 +6,7 @@ Image classification, which is an important field of computer vision, is to clas
 - [Installation](#installation)
 - [Data preparation](#data-preparation)
 - [Training a model with flexible parameters](#training-a-model)
+- [Using Mixed-Precision Training](#using-mixed-precision-training)
 - [Finetuning](#finetuning)
 - [Evaluation](#evaluation)
 - [Inference](#inference)
@@ -112,6 +113,13 @@ The error rate curves of AlexNet, ResNet50 and SE-ResNeXt-50 are shown in the fi
 Training and validation Curves
 </p>
 
+
+## Using Mixed-Precision Training
+
+You may add `--fp16 1` to start train using mixed precisioin training, which the training process will use float16 and the output model ("master" parameters) is saved as float32. You also may need to pass `--scale_loss` to overcome accuracy issues, usually `--scale_loss 8.0` will do.
+
+Note that currently `--fp16` can not use together with `--with_mem_opt`, so pass `--with_mem_opt 0` to disable memory optimization pass.
+
 ## Finetuning
 
 Finetuning is to finetune model weights in a specific task by loading pretrained weights. After initializing ```path_to_pretrain_model```, one can finetune a model as:

fluid/PaddleCV/image_classification/README_cn.md

@@ -109,6 +109,11 @@ End pass 9, train_loss 3.3745200634, train_acc1 0.303871691227, train_acc5 0.545
 训练集合与验证集合上的错误率曲线
 </p>
 
+## 混合精度训练
+
+可以通过开启`--fp16 1`启动混合精度训练，这样训练过程会使用float16数据，并输出float32的模型参数（"master"参数）。您可能需要同时传入`--scale_loss`来解决fp16训练的精度问题，通常传入`--scale_loss 8.0`即可。
+
+注意，目前混合精度训练不能和内存优化功能同时使用，所以需要传`--with_mem_opt 0`这个参数来禁用内存优化功能。
 
 ## 参数微调