From 20cb5f065c0662d7bf38ad3e9734846dcbeaf7e8 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Fri, 29 Sep 2017 10:49:05 +0000 Subject: [PATCH] Deploy to GitHub Pages: e82fda25213872caa49021f4fa4d67031e581cff --- .../doc/_sources/howto/dev/new_op_en.md.txt | 34 ++++++------------ develop/doc/howto/dev/new_op_en.html | 34 ++++++------------ develop/doc/searchindex.js | 2 +- .../_sources/howto/dev/new_op_cn.md.txt | 36 ++++++------------- develop/doc_cn/howto/dev/new_op_cn.html | 36 ++++++------------- develop/doc_cn/searchindex.js | 2 +- 6 files changed, 44 insertions(+), 100 deletions(-) diff --git a/develop/doc/_sources/howto/dev/new_op_en.md.txt b/develop/doc/_sources/howto/dev/new_op_en.md.txt index bad1dbc1de9..57ff7caad19 100644 --- a/develop/doc/_sources/howto/dev/new_op_en.md.txt +++ b/develop/doc/_sources/howto/dev/new_op_en.md.txt @@ -293,41 +293,27 @@ class TestMulGradOp(GradientChecker): 'Y': np.random.random((84, 100)).astype("float32") } - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): + def test_check_grad_normal(self): # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - def test_ignore_y(self): + def test_check_grad_ingore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) ``` Some key points in the code above include: - `create_op("mul")` creates the backward operator's corresponding forward operator. -- `compare_grad` compares results between utilizing the CPU and the GPU. - `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods. - - The first variable `self.op` denotes the forward operator. - - The second variable `self.inputs` denotes the input dictionary, which has its key value identical to its `ProtoMaker` definitions. - - The third variable `["X", "Y"]` appoints `X` and `Y` to be scale tested. - - The fourth variable `"Out"` points to the network's final output target `Out`. -- `test_ignore_x` and `test_ignore_y`branches test the cases where there is only one scaling input. + - The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested. + - The second variable `"Out"` points to the network's final output target `Out`. + - The third variable `max_relative_error` points to the maximum relative tolerance error during scaling tests. +- `test_check_grad_ingore_x` and `test_check_grad_ingore_y`branches test the cases where there is only one scaling input. ### Compiling and Running diff --git a/develop/doc/howto/dev/new_op_en.html b/develop/doc/howto/dev/new_op_en.html index f5ad915f487..51559bad019 100644 --- a/develop/doc/howto/dev/new_op_en.html +++ b/develop/doc/howto/dev/new_op_en.html @@ -445,43 +445,29 @@ Registering the Op | Ops are registered in 'Y': np.random.random((84, 100)).astype("float32") } - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): + def test_check_grad_normal(self): # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - def test_ignore_y(self): + def test_check_grad_ingore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))

Some key points in the code above include:

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\ No newline at end of file diff --git a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt index 264b998f50d..9d3d02ffc31 100644 --- a/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt +++ b/develop/doc_cn/_sources/howto/dev/new_op_cn.md.txt @@ -285,41 +285,27 @@ class TestMulGradOp(GradientChecker): 'Y': np.random.random((84, 100)).astype("float32") } - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): + def test_check_grad_normal(self): # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - def test_ignore_y(self): + def test_check_grad_ingore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) ``` 下面解释代码中一些关键的地方: - 调用`create_op("mul")`创建反向Op对应的前向Op。 -- 调用`compare_grad`函数对比CPU、GPU计算结果。 -- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。 - - 第一个参数`self.op` : 前向Op。 - - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。 - - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。 - - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out` -- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。 +- `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。 + - 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。 + - 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。 + - 第三个参数`max_relative_error`：指定检测梯度时能容忍的最大错误值。 +- `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。 ### 编译和执行单元测试 diff --git a/develop/doc_cn/howto/dev/new_op_cn.html b/develop/doc_cn/howto/dev/new_op_cn.html index 01e260ebe57..162cf1ff7ba 100644 --- a/develop/doc_cn/howto/dev/new_op_cn.html +++ b/develop/doc_cn/howto/dev/new_op_cn.html @@ -452,43 +452,29 @@ Kernel实现 | CPU、GPU共享Kernel实现在'Y': np.random.random((84, 100)).astype("float32") } - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): + def test_check_grad_normal(self): # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - def test_ignore_y(self): + def test_check_grad_ingore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))

下面解释代码中一些关键的地方:

  • 调用create_op("mul")创建反向Op对应的前向Op。
    • -
  • 调用compare_grad函数对比CPU、GPU计算结果。
    • -
  • test_normal中调用check_grad使用数值法检测梯度正确性和稳定性。
      -
    • 第一个参数self.op : 前向Op。
      • -
    • 第二个参数self.inputs : 输入词典，词典的Key和ProtoMaker定义保持一致。
      • -
    • 第三个参数["X", "Y"] : 指定对输入变量X、Y做梯度检测。
      • -
    • 第四个参数"Out" : 指定前向网络最终的输出目标变量Out
      • +
    • test_check_grad_normal中调用check_grad使用数值法检测梯度正确性和稳定性。
        +
      • 第一个参数["X", "Y"] : 指定对输入变量X、Y做梯度检测。
        • +
      • 第二个参数"Out" : 指定前向网络最终的输出目标变量Out。
        • +
      • 第三个参数max_relative_error：指定检测梯度时能容忍的最大错误值。
      • -
    • test_ignore_x和test_ignore_y分支用来测试只需要计算一个输入梯度的情况。
      • +
    • test_check_grad_ingore_x和test_check_grad_ingore_y分支用来测试只需要计算一个输入梯度的情况。
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