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Jan 12, 2018
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fix display error of C-API doc. #7475

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fix display error of C-API doc.
lcy-seso Jan 12, 2018
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lcy-seso Jan 12, 2018
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7 changes: 3 additions & 4 deletions doc/howto/usage/capi/workflow_of_capi_cn.md
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Expand Up @@ -26,10 +26,9 @@

### 准备预测模型

在准备预测模型部分的介绍，我们以手写数字识别任务为例。手写数字识别任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
准备预测模型部分，我们以手写数字识别任务为例进行介绍。手写数字识别任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。

调用C-API开发预测程序需要一个训练好的模型，在终端执行`python mnist_v2.py`
运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
调用C-API开发预测程序需要一个训练好的模型，运行[MNIST手写数字识别目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)下的[mnist_v2.py](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/mnist_v2.py)脚本，在终端执行`python mnist_v2.py`，会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
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@luotao1 luotao1 Jan 12, 2018

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image
在github页面上，有一个特殊符号。

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@lcy-seso lcy-seso Jan 12, 2018

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Done.


下面，我们将训练结束后存储下来的模型转换成预测模型。

Expand Down Expand Up @@ -113,7 +112,7 @@ C-API支持的所有输入数据类型和他们的组织方式，请参考“输

#### step 4. 前向计算

完成上述准备之后，通过调用 `[paddle_gradient_machine_forward](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L73)` 接口完成神经网络的前向计算。
完成上述准备之后，通过调用 [`paddle_gradient_machine_forward`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L73) 接口完成神经网络的前向计算。

#### step 5. 清理

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