Deploy to GitHub Pages: 63bc2ff8

develop/doc/_sources/design/api.md.txt

@@ -3,7 +3,7 @@
 ## Ingredients
 
 As our design principle is starting from the essence: how could we
-allow users to express and solve their problems at neural networks.
+allow users to express and solve their problems as neural networks.
 Some essential concepts that our API have to provide include:
 
 1. A *topology* is an expression of *layers*.
@@ -233,7 +233,7 @@ paddle.dist_train(model,
                   num_parameter_servers=15)
 ```
 
-The pseudo code if `paddle.dist_train` is as follows:
+The pseudo code of `paddle.dist_train` is as follows:
 
 ```python
 def dist_train(topology, parameters, trainer, reader, ...):

develop/doc/_sources/design/auto_gradient_check.md.txt

 ## Auto Gradient Checker Design
 
 ## Backgraound：
-- Operator forward computing is easy to check if the result is right because it has a clear definition. **But** backpropagation is a notoriously difficult algorithm to debug and get right:
-  - 1. you should get the right backpropagation formula according to the forward computation.
-  - 2. you should implement it right in CPP.
-  - 3. it's difficult to prepare test data.
+- Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right:
+  1. you should get the right backpropagation formula according to the forward computation.
+  2. you should implement it right in CPP.
+  3. it's difficult to prepare test data.
 
-- Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
-  - 1. numeric gradient checker only need forward operator.
-  - 2. user only need to prepare the input data for forward Operator.
+- Auto gradient checking gets a numerical gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
+  1. numerical gradient checker only need forward operator.
+  2. user only need to prepare the input data for forward Operator.
 
 ## Mathematical Theory
-The following two document from stanford has a detailed explanation of how to get numeric gradient and why it's useful.
+The following two document from Stanford has a detailed explanation of how to get numerical gradient and why it's useful.
 
 - [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
 - [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
@@ -20,7 +20,7 @@ The following two document from stanford has a detailed explanation of how to ge
 ## Numeric Gradient Implementation
 ### Python Interface
 ```python
-def get_numeric_gradient(op,
+def get_numerical_gradient(op,
                          input_values,
                          output_name,
                          input_to_check,
@@ -30,13 +30,13 @@ def get_numeric_gradient(op,
     Get Numeric Gradient for an operator's input.
 
     :param op: C++ operator instance, could be an network
-    :param input_values: The input variables. Should be an dictionary, key is
-    variable name. Value is numpy array.
+    :param input_values: The input variables. Should be an dictionary, whose key is
+    variable name, and value is numpy array.
     :param output_name: The final output variable name.
-    :param input_to_check: The input variable need to get gradient.
+    :param input_to_check: The input variable with respect to which to compute the gradient.
     :param delta: The perturbation value for numeric gradient method. The
     smaller delta is, the more accurate result will get. But if that delta is
-     too small, it could occur numerical stability problem.
+     too small, it will suffer from numerical stability problem.
     :param local_scope: The local scope used for get_numeric_gradient.
     :return: The gradient array in numpy format.
     """
@@ -45,28 +45,28 @@ def get_numeric_gradient(op,
 ### Explaination:
 
 - Why need `output_name`
-  - One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.
+  - An Operator may have multiple Output, one can get independent gradient from each Output. So caller should specify the name of the output variable.
 
 - Why need `input_to_check`
-  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
+  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
 
 
 ### Core Algorithm Implementation
 
 
 ```python
-    # we only compute gradient of one element each time.
-    # we use a for loop to compute the gradient of every element.
+    # we only compute gradient of one element a time.
+    # we use a for loop to compute the gradient of each element.
     for i in xrange(tensor_size):
-        # get one input element throw it's index i.
+        # get one input element by its index i.
         origin = tensor_to_check.get_float_element(i)
 
-        # add delta to it, run op and then get the sum of the result tensor.
+        # add delta to it, run op and then get the new value of the result tensor.
         x_pos = origin + delta
         tensor_to_check.set_float_element(i, x_pos)
         y_pos = get_output()
 
-        # plus delta to this element, run op and get the sum of the result tensor.
+        # plus delta to this element, run op and get the new value of the result tensor.
         x_neg = origin - delta
         tensor_to_check.set_float_element(i, x_neg)
         y_neg = get_output()
@@ -85,15 +85,15 @@ def get_numeric_gradient(op,
 
 Each Operator Kernel has three kinds of Gradient:
 
-- 1. Numeric Gradient
-- 2. CPU Operator Gradient
-- 3. GPU Operator Gradient(if supported)
+1. Numerical gradient
+2. CPU kernel gradient
+3. GPU kernel gradient (if supported)
 
-Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.
+The numerical gradient only relies on forward Operator. So we use the numerical gradient as the reference value. And the gradient checking is performed in the following three steps:
 
-- 1. calculate the numeric gradient.
-- 2. calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.
-- 3. calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)
+1. calculate the numerical gradient
+2. calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient
+3. calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient (if supported)
 
 #### Python Interface
 
@@ -110,8 +110,8 @@ Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as
         :param forward_op: used to create backward_op
         :param input_vars: numpy value of input variable. The following
             computation will use these variables.
-        :param inputs_to_check: inputs var names that should check gradient.
-        :param output_name: output name that used to
+        :param inputs_to_check: the input variable with respect to which to compute the gradient.
+        :param output_name: The final output variable name.
         :param max_relative_error: The relative tolerance parameter.
         :param no_grad_set: used when create backward ops
         :param only_cpu: only compute and check gradient on cpu kernel.
@@ -120,24 +120,24 @@ Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as
 ```
 
 ### How to check if two numpy array is close enough?
-if `abs_numeric_grad` is nearly zero, then use abs error for numeric_grad, not relative
+if `abs_numerical_grad` is nearly zero, then use abs error for numerical_grad
 
 ```python
-numeric_grad = ...
+numerical_grad = ...
 operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
 
-abs_numeric_grad = numpy.abs(numeric_grad)
-# if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative
+abs_numerical_grad = numpy.abs(numerical_grad)
+# if abs_numerical_grad is nearly zero, then use abs error for numeric_grad, not relative
 # error.
-abs_numeric_grad[abs_numeric_grad < 1e-3] = 1
+abs_numerical_grad[abs_numerical_grad < 1e-3] = 1
 
-diff_mat = numpy.abs(abs_numeric_grad - operator_grad) / abs_numeric_grad
+diff_mat = numpy.abs(abs_numerical_grad - operator_grad) / abs_numerical_grad
 max_diff = numpy.max(diff_mat)
 ```
 
 
 #### Notes：
-1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.
+The Input data for auto gradient checker should be reasonable to avoid numerical  stability problem.
 
 
 #### Refs:

develop/doc/_sources/design/functions_operators_layers.md.txt

@@ -53,12 +53,12 @@ Let's explain using an example.  Suppose that we are going to compose the FC usi
 ```python
 def operator.mul(X1, X2):
     O = Var()
-    paddle.cpp.create_operator("mul", input={X1, Y1], output=O)
+    paddle.cpp.create_operator("mul", input={X1, Y1}, output=O)
     return O
 
 def operator.add(X1, X2):
     O = Var()
-    paddle.cpp.create_operator("add", input={X1, X2], output=O)
+    paddle.cpp.create_operator("add", input={X1, X2}, output=O)
     return O
 ```
 

develop/doc/_sources/design/graph.md.txt

@@ -56,7 +56,7 @@ For each parameter, like W and b created by `layer.fc`, marked as double circles
 
 ## Block and Graph
 
-The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
+The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block](https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
 
 A Block keeps operators in an array `BlockDesc::ops`
 
@@ -67,4 +67,4 @@ message BlockDesc {
 }
 ```
 
-in the order that there appear in user programs, like the Python program at the beginning of this article.  We can imagine that in `ops`,  we have some forward operators, followed by some gradient operators, and then some optimization operators.
+in the order that they appear in user programs, like the Python program at the beginning of this article.  We can imagine that in `ops`,  we have some forward operators, followed by some gradient operators, and then some optimization operators.

develop/doc/_sources/design/parameters_in_cpp.md.txt

 # Design Doc: The C++ Class `Parameters`
 
-`Parameters` is a concept we designed in Paddle V2 API. `Parameters` is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
+`Parameters` is a concept we designed in PaddlePaddle V2 API. `Parameters` is a container of parameters, which makes PaddlePaddle capable of  sharing parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
 
-We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
+We used Python to implement Parameters when designing V2 API before. There are several defects for the current implementation:
 * We just use `memcpy` to share Parameters between topologies, but this is very inefficient. 
-* We did not implement share Parameters while training. We just trigger `memcpy` when start training.
+* We did not support sharing Parameters while training. We just trigger `memcpy` when start training.
 
-It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with `Parameters`:
+It is necessary that we implement Parameters in CPP side. However, it could result a code refactoring for PaddlePaddle, because PaddlePaddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current PaddlePaddle implementation, there are three concepts associated with `Parameters`:
 
 1. `paddle::Parameter`. A `Parameters` is a container for `paddle::Parameter`.
 It is evident that we should use `paddle::Parameter` when developing `Parameters`.
 However, the `Parameter` class contains many functions and does not have a clear interface.
 It contains `create/store Parameter`, `serialize/deserialize`, `optimize(i.e SGD)`, `randomize/zero`.
 When we developing `Parameters`, we only use `create/store Parameter` functionality.
-We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
+We should extract functionalities of Parameter into many classes to clean PaddlePaddle CPP implementation.
 
 2. `paddle::GradientMachine` and its sub-classes, e.g., `paddle::MultiGradientMachine`, `paddle::NeuralNetwork`.
 We should pass `Parameters` to `paddle::GradientMachine` when `forward/backward` to avoid `memcpy` between topologies.
@@ -24,7 +24,7 @@ Also, we should handle multi-GPU/CPU training, because `forward` and `backward`
 So `Parameters` should be used by `paddle::ParameterUpdater`, and `paddle::ParameterUpdater` should optimize `Parameters` (by SGD).
 
 
-The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
+The step by step approach for implementation Parameters in PaddlePaddle C++ core is listed below. Each step should be a PR and could be merged into PaddlePaddle one by one.
 
 1. Clean `paddle::Parameter` interface. Extract the functionalities of `paddle::Parameter` to prepare for the implementation of Parameters.
 

develop/doc/_sources/design/reader/README.md.txt

@@ -52,7 +52,7 @@ Here are valid outputs:
 # a mini batch of three data items, each data item is a list (single column).
 [([1,1,1],),
 ([2,2,2],),
-([3,3,3],),
+([3,3,3],)]
 ```
 
 Please note that each item inside the list must be a tuple, below is an invalid output:

develop/doc/_sources/design/refactorization.md.txt

@@ -15,7 +15,7 @@ The goal of refactorizaiton include:
 
 1. Users write Python programs to describe the graphs and run it (locally or remotely).
 
-1. A graph is composed of *variabels* and *operators*.
+1. A graph is composed of *variables* and *operators*.
 
 1. The description of graphs must be able to be serialized/deserialized, so it
 
@@ -140,7 +140,7 @@ Compile Time -> IR -> Runtime
     * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel.
     * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`.
 * Hand-writing `GPUKernel` and `CPU` code
-    * Do not write `.h`. CPU Kernel should be in `.cc`. CPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
+    * Do not write `.h`. CPU Kernel should be in `.cc`. GPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
 ---
 # Operator Register
 

develop/doc/_sources/design/releasing_process.md.txt

-# Paddle发行规范
+# PaddlePaddle发行规范
 
-Paddle使用git-flow branching model做分支管理，使用[Semantic Versioning](http://semver.org/)标准表示Paddle版本号。
+PaddlePaddle使用git-flow branching model做分支管理，使用[Semantic Versioning](http://semver.org/)标准表示PaddlePaddle版本号。
 
-Paddle每次发新的版本，遵循以下流程:
+PaddlePaddle每次发新的版本，遵循以下流程:
 
 1. 从`develop`分支派生出新的分支，分支名为`release/版本号`。例如，`release/0.10.0`
 2. 将新分支的版本打上tag，tag为`版本号rc.Patch号`。第一个tag为`0.10.0rc1`，第二个为`0.10.0rc2`，依次类推。
@@ -27,14 +27,14 @@ Paddle每次发新的版本，遵循以下流程:
 
 需要注意的是:
 
-* `release/版本号`分支一旦建立，一般不允许再从`develop`分支合入`release/版本号`。这样保证`release/版本号`分支功能的封闭，方便测试人员测试Paddle的行为。
+* `release/版本号`分支一旦建立，一般不允许再从`develop`分支合入`release/版本号`。这样保证`release/版本号`分支功能的封闭，方便测试人员测试PaddlePaddle的行为。
 * 在`release/版本号`分支存在的时候，如果有bugfix的行为，需要将bugfix的分支同时merge到`master`, `develop`和`release/版本号`这三个分支。
 
-# Paddle 分支规范
+# PaddlePaddle 分支规范
 
-Paddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，并适应github的特性做了一些区别。
+PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，并适应github的特性做了一些区别。
 
-* Paddle的主版本库遵循[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范。其中:
+* PaddlePaddle的主版本库遵循[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范。其中:
 	* `master`分支为稳定(stable branch)版本分支。每一个`master`分支的版本都是经过单元测试和回归测试的版本。
 	* `develop`分支为开发(develop branch)版本分支。每一个`develop`分支的版本都经过单元测试，但并没有经过回归测试。
 	* `release/版本号`分支为每一次Release时建立的临时分支。在这个阶段的代码正在经历回归测试。
@@ -42,18 +42,18 @@ Paddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branch
 * 其他用户的fork版本库并不需要严格遵守[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，但所有fork的版本库的所有分支都相当于特性分支。
 	* 建议，开发者fork的版本库使用`develop`分支同步主版本库的`develop`分支
 	* 建议，开发者fork的版本库中，再基于`develop`版本fork出自己的功能分支。
-	* 当功能分支开发完毕后，向Paddle的主版本库提交`Pull Reuqest`，进而进行代码评审。
+	* 当功能分支开发完毕后，向PaddlePaddle的主版本库提交`Pull Reuqest`，进而进行代码评审。
 		* 在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。 
 
 * BugFix分支也是在开发者自己的fork版本库维护，与功能分支不同的是，BugFix分支需要分别给主版本库的`master`、`develop`与可能有的`release/版本号`分支，同时提起`Pull Request`。
 
-# Paddle回归测试列表
+# PaddlePaddle回归测试列表
 
-本列表说明Paddle发版之前需要测试的功能点。
+本列表说明PaddlePaddle发版之前需要测试的功能点。
 
-## Paddle Book中所有章节
+## PaddlePaddle Book中所有章节
 
-Paddle每次发版本首先要保证Paddle Book中所有章节功能的正确性。功能的正确性包括验证Paddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
+PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
 
 | | 新手入门章节 | 识别数字 | 图像分类 | 词向量 | 情感分析 | 语意角色标注 | 机器翻译 | 个性化推荐 |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |

develop/doc/_sources/design/scope.md.txt

@@ -17,7 +17,7 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
 
 1. Scope only contains a map of a name to variable.
 
-   All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state(momentum) etc.
+   All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state (momentum) etc.
 
 1. Variable can only be created by Scope and a variable can only be got from Scope. User cannot create or get a variable outside a scope. This is a constraints of our framework, and will keep our framework simple and clear.
 
@@ -32,7 +32,7 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
 
 1. Scope should destruct all Variables inside it when itself is destructed. User can never store `Variable` pointer somewhere else. 
 
-   Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store `Variable` pointer to private data member or some global variable, the pointer will be a invalid pointer when associated `Scope` is destroyed.
+   Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store `Variable` pointer to private data member or some global variable, the pointer will be an invalid pointer when associated `Scope` is destroyed.
 
 ```cpp
 class Scope {
@@ -50,7 +50,7 @@ class Scope {
 
 Just like [scope](https://en.wikipedia.org/wiki/Scope_(computer_science)) in programming languages, `Scope` in the neural network can also be a local scope. There are two attributes about local scope.
 
-1.  We can create local variables in a local scope. When that local scope are destroyed, all local variables should also be destroyed.
+1.  We can create local variables in a local scope. When that local scope is destroyed, all local variables should also be destroyed.
 2.  Variables in a parent scope can be retrieved from local scopes of that parent scope, i.e., when user get a variable from a scope, it will try to search this variable in current scope. If there is no such variable in the local scope, `scope` will keep searching from its parent, until the variable is found or there is no parent.
 
 ```cpp
@@ -121,4 +121,4 @@ Also, as the parent scope is a `shared_ptr`, we can only `Create()` a scope shar
 
 ## Orthogonal interface
 
-`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `NewVar` will return a `Error` when there is a name conflict locally. Combine `FindVar` and `NewVar`, we can implement `NewVar` easily.
+`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `NewVar` will return an `Error` when there is a name conflict locally. Combine `FindVar` and `NewVar`, we can implement `NewVar` easily.

develop/doc/_sources/design/simple_op_design.md.txt

@@ -6,9 +6,9 @@ The Interaction between Python and C++ can be simplified as two steps:
 
 1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
 
-2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
+2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ for finishing the Op construction task.
 
-### Message form C++ to Python
+### Message from C++ to Python
 
 We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”
 

develop/doc/_sources/design/var_desc.md.txt

 ## Background
 PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
 
-PaddlePaddle use proto message to describe compile time graph for
+PaddlePaddle use proto message to describe compile time graph because
 
 1. Computation graph should be able to be saved to a file.
 1. In distributed training, the graph will be serialized and send to multiple workers.

develop/doc/design/api.html

@@ -182,7 +182,7 @@
 <div class="section" id="ingredients">
 <span id="ingredients"></span><h2>Ingredients<a class="headerlink" href="#ingredients" title="Permalink to this headline">¶</a></h2>
 <p>As our design principle is starting from the essence: how could we
-allow users to express and solve their problems at neural networks.
+allow users to express and solve their problems as neural networks.
 Some essential concepts that our API have to provide include:</p>
 <ol class="simple">
 <li>A <em>topology</em> is an expression of <em>layers</em>.</li>
@@ -385,7 +385,7 @@ access a Kubernetes cluster, s/he should be able to call</p>
                   <span class="n">num_parameter_servers</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>The pseudo code if <code class="docutils literal"><span class="pre">paddle.dist_train</span></code> is as follows:</p>
+<p>The pseudo code of <code class="docutils literal"><span class="pre">paddle.dist_train</span></code> is as follows:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">dist_train</span><span class="p">(</span><span class="n">topology</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">trainer</span><span class="p">,</span> <span class="n">reader</span><span class="p">,</span> <span class="o">...</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;KUBERNETES_SERVICE_HOST&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="bp">None</span><span class="p">:</span>
         <span class="n">image_name</span> <span class="o">=</span> <span class="n">k8s_user</span> <span class="o">+</span> <span class="s1">&#39;/&#39;</span> <span class="o">+</span> <span class="n">k8s_job</span>

develop/doc/design/auto_gradient_check.html

@@ -183,37 +183,22 @@
 <div class="section" id="backgraound">
 <span id="backgraound"></span><h1>Backgraound：<a class="headerlink" href="#backgraound" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
-<li>Operator forward computing is easy to check if the result is right because it has a clear definition. <strong>But</strong> backpropagation is a notoriously difficult algorithm to debug and get right:<ul>
-<li><ol class="first">
+<li>Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right:<ol>
 <li>you should get the right backpropagation formula according to the forward computation.</li>
-</ol>
-</li>
-<li><ol class="first">
 <li>you should implement it right in CPP.</li>
-</ol>
-</li>
-<li><ol class="first">
 <li>it&#8217;s difficult to prepare test data.</li>
 </ol>
 </li>
-</ul>
-</li>
-<li>Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator&#8217;s result. It has several advantages:<ul>
-<li><ol class="first">
-<li>numeric gradient checker only need forward operator.</li>
-</ol>
-</li>
-<li><ol class="first">
+<li>Auto gradient checking gets a numerical gradient by forward Operator and use it as a reference of the backward Operator&#8217;s result. It has several advantages:<ol>
+<li>numerical gradient checker only need forward operator.</li>
 <li>user only need to prepare the input data for forward Operator.</li>
 </ol>
 </li>
 </ul>
-</li>
-</ul>
 </div>
 <div class="section" id="mathematical-theory">
 <span id="mathematical-theory"></span><h1>Mathematical Theory<a class="headerlink" href="#mathematical-theory" title="Permalink to this headline">¶</a></h1>
-<p>The following two document from stanford has a detailed explanation of how to get numeric gradient and why it&#8217;s useful.</p>
+<p>The following two document from Stanford has a detailed explanation of how to get numerical gradient and why it&#8217;s useful.</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization">Gradient checking and advanced optimization(en)</a></li>
@@ -225,7 +210,7 @@
 <span id="numeric-gradient-implementation"></span><h1>Numeric Gradient Implementation<a class="headerlink" href="#numeric-gradient-implementation" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="python-interface">
 <span id="python-interface"></span><h2>Python Interface<a class="headerlink" href="#python-interface" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_numeric_gradient</span><span class="p">(</span><span class="n">op</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_numerical_gradient</span><span class="p">(</span><span class="n">op</span><span class="p">,</span>
                          <span class="n">input_values</span><span class="p">,</span>
                          <span class="n">output_name</span><span class="p">,</span>
                          <span class="n">input_to_check</span><span class="p">,</span>
@@ -235,13 +220,13 @@
 <span class="sd">    Get Numeric Gradient for an operator&#39;s input.</span>
 
 <span class="sd">    :param op: C++ operator instance, could be an network</span>
-<span class="sd">    :param input_values: The input variables. Should be an dictionary, key is</span>
-<span class="sd">    variable name. Value is numpy array.</span>
+<span class="sd">    :param input_values: The input variables. Should be an dictionary, whose key is</span>
+<span class="sd">    variable name, and value is numpy array.</span>
 <span class="sd">    :param output_name: The final output variable name.</span>
-<span class="sd">    :param input_to_check: The input variable need to get gradient.</span>
+<span class="sd">    :param input_to_check: The input variable with respect to which to compute the gradient.</span>
 <span class="sd">    :param delta: The perturbation value for numeric gradient method. The</span>
 <span class="sd">    smaller delta is, the more accurate result will get. But if that delta is</span>
-<span class="sd">     too small, it could occur numerical stability problem.</span>
+<span class="sd">     too small, it will suffer from numerical stability problem.</span>
 <span class="sd">    :param local_scope: The local scope used for get_numeric_gradient.</span>
 <span class="sd">    :return: The gradient array in numpy format.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -252,29 +237,29 @@
 <span id="explaination"></span><h2>Explaination:<a class="headerlink" href="#explaination" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
 <li>Why need <code class="docutils literal"><span class="pre">output_name</span></code><ul>
-<li>One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.</li>
+<li>An Operator may have multiple Output, one can get independent gradient from each Output. So caller should specify the name of the output variable.</li>
 </ul>
 </li>
 <li>Why need <code class="docutils literal"><span class="pre">input_to_check</span></code><ul>
-<li>One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> multiple times.</li>
+<li>One operator may have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numeric Gradient needs to calculate them one by one. So <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> multiple times.</li>
 </ul>
 </li>
 </ul>
 </div>
 <div class="section" id="core-algorithm-implementation">
 <span id="core-algorithm-implementation"></span><h2>Core Algorithm Implementation<a class="headerlink" href="#core-algorithm-implementation" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-python"><div class="highlight"><pre><span></span>    <span class="c1"># we only compute gradient of one element each time.</span>
-    <span class="c1"># we use a for loop to compute the gradient of every element.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>    <span class="c1"># we only compute gradient of one element a time.</span>
+    <span class="c1"># we use a for loop to compute the gradient of each element.</span>
     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">tensor_size</span><span class="p">):</span>
-        <span class="c1"># get one input element throw it&#39;s index i.</span>
+        <span class="c1"># get one input element by its index i.</span>
         <span class="n">origin</span> <span class="o">=</span> <span class="n">tensor_to_check</span><span class="o">.</span><span class="n">get_float_element</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
 
-        <span class="c1"># add delta to it, run op and then get the sum of the result tensor.</span>
+        <span class="c1"># add delta to it, run op and then get the new value of the result tensor.</span>
         <span class="n">x_pos</span> <span class="o">=</span> <span class="n">origin</span> <span class="o">+</span> <span class="n">delta</span>
         <span class="n">tensor_to_check</span><span class="o">.</span><span class="n">set_float_element</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">x_pos</span><span class="p">)</span>
         <span class="n">y_pos</span> <span class="o">=</span> <span class="n">get_output</span><span class="p">()</span>
 
-        <span class="c1"># plus delta to this element, run op and get the sum of the result tensor.</span>
+        <span class="c1"># plus delta to this element, run op and get the new value of the result tensor.</span>
         <span class="n">x_neg</span> <span class="o">=</span> <span class="n">origin</span> <span class="o">-</span> <span class="n">delta</span>
         <span class="n">tensor_to_check</span><span class="o">.</span><span class="n">set_float_element</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">x_neg</span><span class="p">)</span>
         <span class="n">y_neg</span> <span class="o">=</span> <span class="n">get_output</span><span class="p">()</span>
@@ -294,35 +279,17 @@
 <div class="section" id="auto-graident-checker-framework">
 <span id="auto-graident-checker-framework"></span><h1>Auto Graident Checker Framework<a class="headerlink" href="#auto-graident-checker-framework" title="Permalink to this headline">¶</a></h1>
 <p>Each Operator Kernel has three kinds of Gradient:</p>
-<ul class="simple">
-<li><ol class="first">
-<li>Numeric Gradient</li>
+<ol class="simple">
+<li>Numerical gradient</li>
+<li>CPU kernel gradient</li>
+<li>GPU kernel gradient (if supported)</li>
 </ol>
-</li>
-<li><ol class="first">
-<li>CPU Operator Gradient</li>
+<p>The numerical gradient only relies on forward Operator. So we use the numerical gradient as the reference value. And the gradient checking is performed in the following three steps:</p>
+<ol class="simple">
+<li>calculate the numerical gradient</li>
+<li>calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient</li>
+<li>calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient (if supported)</li>
 </ol>
-</li>
-<li><ol class="first">
-<li>GPU Operator Gradient(if supported)</li>
-</ol>
-</li>
-</ul>
-<p>Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.</p>
-<ul class="simple">
-<li><ol class="first">
-<li>calculate the numeric gradient.</li>
-</ol>
-</li>
-<li><ol class="first">
-<li>calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.</li>
-</ol>
-</li>
-<li><ol class="first">
-<li>calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)</li>
-</ol>
-</li>
-</ul>
 <div class="section" id="python-interface">
 <span id="id1"></span><h2>Python Interface<a class="headerlink" href="#python-interface" title="Permalink to this headline">¶</a></h2>
 <div class="highlight-python"><div class="highlight"><pre><span></span>    <span class="k">def</span> <span class="nf">check_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
@@ -337,8 +304,8 @@
 <span class="sd">        :param forward_op: used to create backward_op</span>
 <span class="sd">        :param input_vars: numpy value of input variable. The following</span>
 <span class="sd">            computation will use these variables.</span>
-<span class="sd">        :param inputs_to_check: inputs var names that should check gradient.</span>
-<span class="sd">        :param output_name: output name that used to</span>
+<span class="sd">        :param inputs_to_check: the input variable with respect to which to compute the gradient.</span>
+<span class="sd">        :param output_name: The final output variable name.</span>
 <span class="sd">        :param max_relative_error: The relative tolerance parameter.</span>
 <span class="sd">        :param no_grad_set: used when create backward ops</span>
 <span class="sd">        :param only_cpu: only compute and check gradient on cpu kernel.</span>
@@ -349,22 +316,22 @@
 </div>
 <div class="section" id="how-to-check-if-two-numpy-array-is-close-enough">
 <span id="how-to-check-if-two-numpy-array-is-close-enough"></span><h2>How to check if two numpy array is close enough?<a class="headerlink" href="#how-to-check-if-two-numpy-array-is-close-enough" title="Permalink to this headline">¶</a></h2>
-<p>if <code class="docutils literal"><span class="pre">abs_numeric_grad</span></code> is nearly zero, then use abs error for numeric_grad, not relative</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">numeric_grad</span> <span class="o">=</span> <span class="o">...</span>
+<p>if <code class="docutils literal"><span class="pre">abs_numerical_grad</span></code> is nearly zero, then use abs error for numerical_grad</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">numerical_grad</span> <span class="o">=</span> <span class="o">...</span>
 <span class="n">operator_grad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">scope</span><span class="o">.</span><span class="n">find_var</span><span class="p">(</span><span class="n">grad_var_name</span><span class="p">(</span><span class="n">name</span><span class="p">))</span><span class="o">.</span><span class="n">get_tensor</span><span class="p">())</span>
 
-<span class="n">abs_numeric_grad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">numeric_grad</span><span class="p">)</span>
-<span class="c1"># if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative</span>
+<span class="n">abs_numerical_grad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">numerical_grad</span><span class="p">)</span>
+<span class="c1"># if abs_numerical_grad is nearly zero, then use abs error for numeric_grad, not relative</span>
 <span class="c1"># error.</span>
-<span class="n">abs_numeric_grad</span><span class="p">[</span><span class="n">abs_numeric_grad</span> <span class="o">&lt;</span> <span class="mf">1e-3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">abs_numerical_grad</span><span class="p">[</span><span class="n">abs_numerical_grad</span> <span class="o">&lt;</span> <span class="mf">1e-3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
 
-<span class="n">diff_mat</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">abs_numeric_grad</span> <span class="o">-</span> <span class="n">operator_grad</span><span class="p">)</span> <span class="o">/</span> <span class="n">abs_numeric_grad</span>
+<span class="n">diff_mat</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">abs_numerical_grad</span> <span class="o">-</span> <span class="n">operator_grad</span><span class="p">)</span> <span class="o">/</span> <span class="n">abs_numerical_grad</span>
 <span class="n">max_diff</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">diff_mat</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="section" id="notes">
 <span id="notes"></span><h3>Notes：<a class="headerlink" href="#notes" title="Permalink to this headline">¶</a></h3>
-<p>1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.</p>
+<p>The Input data for auto gradient checker should be reasonable to avoid numerical  stability problem.</p>
 </div>
 <div class="section" id="refs">
 <span id="refs"></span><h3>Refs:<a class="headerlink" href="#refs" title="Permalink to this headline">¶</a></h3>

develop/doc/design/functions_operators_layers.html

@@ -217,12 +217,12 @@
 <p>Let&#8217;s explain using an example.  Suppose that we are going to compose the FC using mul and add in Python, we&#8217;d like to have Python functions <code class="docutils literal"><span class="pre">mul</span></code> and <code class="docutils literal"><span class="pre">add</span></code> defined in module <code class="docutils literal"><span class="pre">operator</span></code>:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">operator</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">):</span>
     <span class="n">O</span> <span class="o">=</span> <span class="n">Var</span><span class="p">()</span>
-    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">Y1</span><span class="p">],</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
+    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">Y1</span><span class="p">},</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">O</span>
 
 <span class="k">def</span> <span class="nf">operator</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">):</span>
     <span class="n">O</span> <span class="o">=</span> <span class="n">Var</span><span class="p">()</span>
-    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;add&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">],</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
+    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;add&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">},</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">O</span>
 </pre></div>
 </div>

develop/doc/design/graph.html

@@ -226,7 +226,7 @@
 </div>
 <div class="section" id="block-and-graph">
 <span id="block-and-graph"></span><h2>Block and Graph<a class="headerlink" href="#block-and-graph" title="Permalink to this headline">¶</a></h2>
-<p>The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.</p>
+<p>The word block and graph are interchangable in the desgin of PaddlePaddle.  A <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/3708">Block</a> is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.</p>
 <p>A Block keeps operators in an array <code class="docutils literal"><span class="pre">BlockDesc::ops</span></code></p>
 <div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">BlockDesc</span> <span class="p">{</span>
   <span class="k">repeated</span> <span class="n">OpDesc</span> <span class="na">ops</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
@@ -234,7 +234,7 @@
 <span class="p">}</span>
 </pre></div>
 </div>
-<p>in the order that there appear in user programs, like the Python program at the beginning of this article.  We can imagine that in <code class="docutils literal"><span class="pre">ops</span></code>,  we have some forward operators, followed by some gradient operators, and then some optimization operators.</p>
+<p>in the order that they appear in user programs, like the Python program at the beginning of this article.  We can imagine that in <code class="docutils literal"><span class="pre">ops</span></code>,  we have some forward operators, followed by some gradient operators, and then some optimization operators.</p>
 </div>
 </div>
 

develop/doc/design/parameters_in_cpp.html

@@ -179,20 +179,20 @@
             
   <div class="section" id="design-doc-the-c-class-parameters">
 <span id="design-doc-the-c-class-parameters"></span><h1>Design Doc: The C++ Class <code class="docutils literal"><span class="pre">Parameters</span></code><a class="headerlink" href="#design-doc-the-c-class-parameters" title="Permalink to this headline">¶</a></h1>
-<p><code class="docutils literal"><span class="pre">Parameters</span></code> is a concept we designed in Paddle V2 API. <code class="docutils literal"><span class="pre">Parameters</span></code> is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of <code class="docutils literal"><span class="pre">Parameter</span></code> in <a class="reference internal" href="api.html"><span class="doc">api.md</span></a>.</p>
-<p>We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:</p>
+<p><code class="docutils literal"><span class="pre">Parameters</span></code> is a concept we designed in PaddlePaddle V2 API. <code class="docutils literal"><span class="pre">Parameters</span></code> is a container of parameters, which makes PaddlePaddle capable of  sharing parameter between topologies. We described usages of <code class="docutils literal"><span class="pre">Parameter</span></code> in <a class="reference internal" href="api.html"><span class="doc">api.md</span></a>.</p>
+<p>We used Python to implement Parameters when designing V2 API before. There are several defects for the current implementation:</p>
 <ul class="simple">
 <li>We just use <code class="docutils literal"><span class="pre">memcpy</span></code> to share Parameters between topologies, but this is very inefficient.</li>
-<li>We did not implement share Parameters while training. We just trigger <code class="docutils literal"><span class="pre">memcpy</span></code> when start training.</li>
+<li>We did not support sharing Parameters while training. We just trigger <code class="docutils literal"><span class="pre">memcpy</span></code> when start training.</li>
 </ul>
-<p>It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with <code class="docutils literal"><span class="pre">Parameters</span></code>:</p>
+<p>It is necessary that we implement Parameters in CPP side. However, it could result a code refactoring for PaddlePaddle, because PaddlePaddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current PaddlePaddle implementation, there are three concepts associated with <code class="docutils literal"><span class="pre">Parameters</span></code>:</p>
 <ol class="simple">
 <li><code class="docutils literal"><span class="pre">paddle::Parameter</span></code>. A <code class="docutils literal"><span class="pre">Parameters</span></code> is a container for <code class="docutils literal"><span class="pre">paddle::Parameter</span></code>.
 It is evident that we should use <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> when developing <code class="docutils literal"><span class="pre">Parameters</span></code>.
 However, the <code class="docutils literal"><span class="pre">Parameter</span></code> class contains many functions and does not have a clear interface.
 It contains <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code>, <code class="docutils literal"><span class="pre">serialize/deserialize</span></code>, <code class="docutils literal"><span class="pre">optimize(i.e</span> <span class="pre">SGD)</span></code>, <code class="docutils literal"><span class="pre">randomize/zero</span></code>.
 When we developing <code class="docutils literal"><span class="pre">Parameters</span></code>, we only use <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code> functionality.
-We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.</li>
+We should extract functionalities of Parameter into many classes to clean PaddlePaddle CPP implementation.</li>
 <li><code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> and its sub-classes, e.g., <code class="docutils literal"><span class="pre">paddle::MultiGradientMachine</span></code>, <code class="docutils literal"><span class="pre">paddle::NeuralNetwork</span></code>.
 We should pass <code class="docutils literal"><span class="pre">Parameters</span></code> to <code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> when <code class="docutils literal"><span class="pre">forward/backward</span></code> to avoid <code class="docutils literal"><span class="pre">memcpy</span></code> between topologies.
 Also, we should handle multi-GPU/CPU training, because <code class="docutils literal"><span class="pre">forward</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> would perform on multi-GPUs and multi-CPUs.
@@ -200,7 +200,7 @@ Also, we should handle multi-GPU/CPU training, because <code class="docutils lit
 <li><code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>. The ParameterUpdater is used to update parameters in Paddle.
 So <code class="docutils literal"><span class="pre">Parameters</span></code> should be used by <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>, and <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code> should optimize <code class="docutils literal"><span class="pre">Parameters</span></code> (by SGD).</li>
 </ol>
-<p>The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.</p>
+<p>The step by step approach for implementation Parameters in PaddlePaddle C++ core is listed below. Each step should be a PR and could be merged into PaddlePaddle one by one.</p>
 <ol class="simple">
 <li>Clean <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> interface. Extract the functionalities of <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> to prepare for the implementation of Parameters.</li>
 <li>Implementation a <code class="docutils literal"><span class="pre">Parameters</span></code> class. It just stores the <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> inside. Make <code class="docutils literal"><span class="pre">GradientMachine</span></code> uses <code class="docutils literal"><span class="pre">Parameters</span></code> as a class member.</li>

develop/doc/design/reader/README.html

@@ -223,7 +223,7 @@
 <span class="c1"># a mini batch of three data items, each data item is a list (single column).</span>
 <span class="p">[([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],),</span>
 <span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],),</span>
-<span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],),</span>
+<span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],)]</span>
 </pre></div>
 </div>
 <p>Please note that each item inside the list must be a tuple, below is an invalid output:</p>

develop/doc/design/refactorization.html

@@ -192,7 +192,7 @@
 <li>PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.</li>
 <li>Please dig into <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md">computation graphs</a> for a solid example.</li>
 <li>Users write Python programs to describe the graphs and run it (locally or remotely).</li>
-<li>A graph is composed of <em>variabels</em> and <em>operators</em>.</li>
+<li>A graph is composed of <em>variables</em> and <em>operators</em>.</li>
 <li>The description of graphs must be able to be serialized/deserialized, so it<ol>
 <li>could to be sent to the cloud for distributed execution, and</li>
 <li>be sent to clients for mobile or enterprise deployment.</li>
@@ -346,7 +346,7 @@
 </ul>
 </li>
 <li>Hand-writing <code class="docutils literal"><span class="pre">GPUKernel</span></code> and <code class="docutils literal"><span class="pre">CPU</span></code> code<ul>
-<li>Do not write <code class="docutils literal"><span class="pre">.h</span></code>. CPU Kernel should be in <code class="docutils literal"><span class="pre">.cc</span></code>. CPU kernel should be in <code class="docutils literal"><span class="pre">.cu</span></code>. (<code class="docutils literal"><span class="pre">GCC</span></code> cannot compile GPU code.)</li>
+<li>Do not write <code class="docutils literal"><span class="pre">.h</span></code>. CPU Kernel should be in <code class="docutils literal"><span class="pre">.cc</span></code>. GPU kernel should be in <code class="docutils literal"><span class="pre">.cu</span></code>. (<code class="docutils literal"><span class="pre">GCC</span></code> cannot compile GPU code.)</li>
 </ul>
 </li>
 </ul>

develop/doc/design/releasing_process.html

@@ -8,7 +8,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Paddle发行规范 &mdash; PaddlePaddle  documentation</title>
+  <title>PaddlePaddle发行规范 &mdash; PaddlePaddle  documentation</title>
   
 
   
@@ -168,7 +168,7 @@
 <div role="navigation" aria-label="breadcrumbs navigation">
   <ul class="wy-breadcrumbs">
       
-    <li>Paddle发行规范</li>
+    <li>PaddlePaddle发行规范</li>
   </ul>
 </div>
       
@@ -177,10 +177,10 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="paddle">
-<span id="paddle"></span><h1>Paddle发行规范<a class="headerlink" href="#paddle" title="Permalink to this headline">¶</a></h1>
-<p>Paddle使用git-flow branching model做分支管理，使用<a class="reference external" href="http://semver.org/">Semantic Versioning</a>标准表示Paddle版本号。</p>
-<p>Paddle每次发新的版本，遵循以下流程:</p>
+  <div class="section" id="paddlepaddle">
+<span id="paddlepaddle"></span><h1>PaddlePaddle发行规范<a class="headerlink" href="#paddlepaddle" title="Permalink to this headline">¶</a></h1>
+<p>PaddlePaddle使用git-flow branching model做分支管理，使用<a class="reference external" href="http://semver.org/">Semantic Versioning</a>标准表示PaddlePaddle版本号。</p>
+<p>PaddlePaddle每次发新的版本，遵循以下流程:</p>
 <ol>
 <li><p class="first">从<code class="docutils literal"><span class="pre">develop</span></code>分支派生出新的分支，分支名为<code class="docutils literal"><span class="pre">release/版本号</span></code>。例如，<code class="docutils literal"><span class="pre">release/0.10.0</span></code></p>
 </li>
@@ -223,15 +223,15 @@
 </ol>
 <p>需要注意的是:</p>
 <ul class="simple">
-<li><code class="docutils literal"><span class="pre">release/版本号</span></code>分支一旦建立，一般不允许再从<code class="docutils literal"><span class="pre">develop</span></code>分支合入<code class="docutils literal"><span class="pre">release/版本号</span></code>。这样保证<code class="docutils literal"><span class="pre">release/版本号</span></code>分支功能的封闭，方便测试人员测试Paddle的行为。</li>
+<li><code class="docutils literal"><span class="pre">release/版本号</span></code>分支一旦建立，一般不允许再从<code class="docutils literal"><span class="pre">develop</span></code>分支合入<code class="docutils literal"><span class="pre">release/版本号</span></code>。这样保证<code class="docutils literal"><span class="pre">release/版本号</span></code>分支功能的封闭，方便测试人员测试PaddlePaddle的行为。</li>
 <li>在<code class="docutils literal"><span class="pre">release/版本号</span></code>分支存在的时候，如果有bugfix的行为，需要将bugfix的分支同时merge到<code class="docutils literal"><span class="pre">master</span></code>, <code class="docutils literal"><span class="pre">develop</span></code>和<code class="docutils literal"><span class="pre">release/版本号</span></code>这三个分支。</li>
 </ul>
 </div>
-<div class="section" id="paddle">
-<span id="id1"></span><h1>Paddle 分支规范<a class="headerlink" href="#paddle" title="Permalink to this headline">¶</a></h1>
-<p>Paddle开发过程使用<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范，并适应github的特性做了一些区别。</p>
+<div class="section" id="paddlepaddle">
+<span id="id1"></span><h1>PaddlePaddle 分支规范<a class="headerlink" href="#paddlepaddle" title="Permalink to this headline">¶</a></h1>
+<p>PaddlePaddle开发过程使用<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范，并适应github的特性做了一些区别。</p>
 <ul class="simple">
-<li>Paddle的主版本库遵循<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范。其中:<ul>
+<li>PaddlePaddle的主版本库遵循<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范。其中:<ul>
 <li><code class="docutils literal"><span class="pre">master</span></code>分支为稳定(stable branch)版本分支。每一个<code class="docutils literal"><span class="pre">master</span></code>分支的版本都是经过单元测试和回归测试的版本。</li>
 <li><code class="docutils literal"><span class="pre">develop</span></code>分支为开发(develop branch)版本分支。每一个<code class="docutils literal"><span class="pre">develop</span></code>分支的版本都经过单元测试，但并没有经过回归测试。</li>
 <li><code class="docutils literal"><span class="pre">release/版本号</span></code>分支为每一次Release时建立的临时分支。在这个阶段的代码正在经历回归测试。</li>
@@ -240,7 +240,7 @@
 <li>其他用户的fork版本库并不需要严格遵守<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范，但所有fork的版本库的所有分支都相当于特性分支。<ul>
 <li>建议，开发者fork的版本库使用<code class="docutils literal"><span class="pre">develop</span></code>分支同步主版本库的<code class="docutils literal"><span class="pre">develop</span></code>分支</li>
 <li>建议，开发者fork的版本库中，再基于<code class="docutils literal"><span class="pre">develop</span></code>版本fork出自己的功能分支。</li>
-<li>当功能分支开发完毕后，向Paddle的主版本库提交<code class="docutils literal"><span class="pre">Pull</span> <span class="pre">Reuqest</span></code>，进而进行代码评审。<ul>
+<li>当功能分支开发完毕后，向PaddlePaddle的主版本库提交<code class="docutils literal"><span class="pre">Pull</span> <span class="pre">Reuqest</span></code>，进而进行代码评审。<ul>
 <li>在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。</li>
 </ul>
 </li>
@@ -249,12 +249,12 @@
 <li>BugFix分支也是在开发者自己的fork版本库维护，与功能分支不同的是，BugFix分支需要分别给主版本库的<code class="docutils literal"><span class="pre">master</span></code>、<code class="docutils literal"><span class="pre">develop</span></code>与可能有的<code class="docutils literal"><span class="pre">release/版本号</span></code>分支，同时提起<code class="docutils literal"><span class="pre">Pull</span> <span class="pre">Request</span></code>。</li>
 </ul>
 </div>
-<div class="section" id="paddle">
-<span id="id2"></span><h1>Paddle回归测试列表<a class="headerlink" href="#paddle" title="Permalink to this headline">¶</a></h1>
-<p>本列表说明Paddle发版之前需要测试的功能点。</p>
-<div class="section" id="paddle-book">
-<span id="paddle-book"></span><h2>Paddle Book中所有章节<a class="headerlink" href="#paddle-book" title="Permalink to this headline">¶</a></h2>
-<p>Paddle每次发版本首先要保证Paddle Book中所有章节功能的正确性。功能的正确性包括验证Paddle目前的<code class="docutils literal"><span class="pre">paddle_trainer</span></code>训练和纯使用<code class="docutils literal"><span class="pre">Python</span></code>训练模型正确性。</p>
+<div class="section" id="paddlepaddle">
+<span id="id2"></span><h1>PaddlePaddle回归测试列表<a class="headerlink" href="#paddlepaddle" title="Permalink to this headline">¶</a></h1>
+<p>本列表说明PaddlePaddle发版之前需要测试的功能点。</p>
+<div class="section" id="paddlepaddle-book">
+<span id="paddlepaddle-book"></span><h2>PaddlePaddle Book中所有章节<a class="headerlink" href="#paddlepaddle-book" title="Permalink to this headline">¶</a></h2>
+<p>PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的<code class="docutils literal"><span class="pre">paddle_trainer</span></code>训练和纯使用<code class="docutils literal"><span class="pre">Python</span></code>训练模型正确性。</p>
 <p>| | 新手入门章节 | 识别数字 | 图像分类 | 词向量 | 情感分析 | 语意角色标注 | 机器翻译 | 个性化推荐 |
 | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; |
 | API.V2 + Docker + GPU  |  |  |  |  |  |  |  |  |

develop/doc/design/scope.html

@@ -193,7 +193,7 @@
 <p>Scope is an association of a name to variable. All variables belong to <code class="docutils literal"><span class="pre">Scope</span></code>. You need to specify a scope to run a Net, i.e., <code class="docutils literal"><span class="pre">net.Run(&amp;scope)</span></code>. One net can run in different scopes and update different variable in the scope.</p>
 <ol>
 <li><p class="first">Scope only contains a map of a name to variable.</p>
-<p>All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state(momentum) etc.</p>
+<p>All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state (momentum) etc.</p>
 </li>
 <li><p class="first">Variable can only be created by Scope and a variable can only be got from Scope. User cannot create or get a variable outside a scope. This is a constraints of our framework, and will keep our framework simple and clear.</p>
 </li>
@@ -208,7 +208,7 @@
 <p>Variable can not belong to many scopes. If you want to use variables from parent scope, you can use <code class="docutils literal"><span class="pre">parent</span> <span class="pre">scope</span></code>.</p>
 </li>
 <li><p class="first">Scope should destruct all Variables inside it when itself is destructed. User can never store <code class="docutils literal"><span class="pre">Variable</span></code> pointer somewhere else.</p>
-<p>Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store <code class="docutils literal"><span class="pre">Variable</span></code> pointer to private data member or some global variable, the pointer will be a invalid pointer when associated <code class="docutils literal"><span class="pre">Scope</span></code> is destroyed.</p>
+<p>Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store <code class="docutils literal"><span class="pre">Variable</span></code> pointer to private data member or some global variable, the pointer will be an invalid pointer when associated <code class="docutils literal"><span class="pre">Scope</span></code> is destroyed.</p>
 </li>
 </ol>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Scope</span> <span class="p">{</span>
@@ -226,7 +226,7 @@
 <span id="parent-scope-and-local-scope"></span><h2>Parent scope and local scope<a class="headerlink" href="#parent-scope-and-local-scope" title="Permalink to this headline">¶</a></h2>
 <p>Just like <a class="reference external" href="https://en.wikipedia.org/wiki/Scope_(computer_science)">scope</a> in programming languages, <code class="docutils literal"><span class="pre">Scope</span></code> in the neural network can also be a local scope. There are two attributes about local scope.</p>
 <ol class="simple">
-<li>We can create local variables in a local scope. When that local scope are destroyed, all local variables should also be destroyed.</li>
+<li>We can create local variables in a local scope. When that local scope is destroyed, all local variables should also be destroyed.</li>
 <li>Variables in a parent scope can be retrieved from local scopes of that parent scope, i.e., when user get a variable from a scope, it will try to search this variable in current scope. If there is no such variable in the local scope, <code class="docutils literal"><span class="pre">scope</span></code> will keep searching from its parent, until the variable is found or there is no parent.</li>
 </ol>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Scope</span> <span class="p">{</span>
@@ -295,7 +295,7 @@
 </div>
 <div class="section" id="orthogonal-interface">
 <span id="orthogonal-interface"></span><h2>Orthogonal interface<a class="headerlink" href="#orthogonal-interface" title="Permalink to this headline">¶</a></h2>
-<p><code class="docutils literal"><span class="pre">FindVar</span></code> will return <code class="docutils literal"><span class="pre">nullptr</span></code> when <code class="docutils literal"><span class="pre">name</span></code> is not found. It can be used as <code class="docutils literal"><span class="pre">Contains</span></code> method. <code class="docutils literal"><span class="pre">NewVar</span></code> will return a <code class="docutils literal"><span class="pre">Error</span></code> when there is a name conflict locally. Combine <code class="docutils literal"><span class="pre">FindVar</span></code> and <code class="docutils literal"><span class="pre">NewVar</span></code>, we can implement <code class="docutils literal"><span class="pre">NewVar</span></code> easily.</p>
+<p><code class="docutils literal"><span class="pre">FindVar</span></code> will return <code class="docutils literal"><span class="pre">nullptr</span></code> when <code class="docutils literal"><span class="pre">name</span></code> is not found. It can be used as <code class="docutils literal"><span class="pre">Contains</span></code> method. <code class="docutils literal"><span class="pre">NewVar</span></code> will return an <code class="docutils literal"><span class="pre">Error</span></code> when there is a name conflict locally. Combine <code class="docutils literal"><span class="pre">FindVar</span></code> and <code class="docutils literal"><span class="pre">NewVar</span></code>, we can implement <code class="docutils literal"><span class="pre">NewVar</span></code> easily.</p>
 </div>
 </div>
 

develop/doc/design/simple_op_design.html

@@ -183,10 +183,10 @@
 <p>The Interaction between Python and C++ can be simplified as two steps:</p>
 <ol class="simple">
 <li>C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.</li>
-<li>Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.</li>
+<li>Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ for finishing the Op construction task.</li>
 </ol>
-<div class="section" id="message-form-c-to-python">
-<span id="message-form-c-to-python"></span><h2>Message form C++ to Python<a class="headerlink" href="#message-form-c-to-python" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="message-from-c-to-python">
+<span id="message-from-c-to-python"></span><h2>Message from C++ to Python<a class="headerlink" href="#message-from-c-to-python" title="Permalink to this headline">¶</a></h2>
 <p>We define a Protobuf message class <code class="docutils literal"><span class="pre">OpProto</span></code> to hold message needed in the first step. What should an <code class="docutils literal"><span class="pre">OpProto</span></code> contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”</p>
 <p>Following message are necessary:</p>
 <ol class="simple">

develop/doc/design/var_desc.html

@@ -180,7 +180,7 @@
   <div class="section" id="background">
 <span id="background"></span><h1>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h1>
 <p>PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.</p>
-<p>PaddlePaddle use proto message to describe compile time graph for</p>
+<p>PaddlePaddle use proto message to describe compile time graph because</p>
 <ol class="simple">
 <li>Computation graph should be able to be saved to a file.</li>
 <li>In distributed training, the graph will be serialized and send to multiple workers.</li>

develop/doc_cn/_sources/design/api.md.txt

@@ -3,7 +3,7 @@
 ## Ingredients
 
 As our design principle is starting from the essence: how could we
-allow users to express and solve their problems at neural networks.
+allow users to express and solve their problems as neural networks.
 Some essential concepts that our API have to provide include:
 
 1. A *topology* is an expression of *layers*.
@@ -233,7 +233,7 @@ paddle.dist_train(model,
                   num_parameter_servers=15)
 ```
 
-The pseudo code if `paddle.dist_train` is as follows:
+The pseudo code of `paddle.dist_train` is as follows:
 
 ```python
 def dist_train(topology, parameters, trainer, reader, ...):

develop/doc_cn/_sources/design/auto_gradient_check.md.txt

 ## Auto Gradient Checker Design
 
 ## Backgraound：
-- Operator forward computing is easy to check if the result is right because it has a clear definition. **But** backpropagation is a notoriously difficult algorithm to debug and get right:
-  - 1. you should get the right backpropagation formula according to the forward computation.
-  - 2. you should implement it right in CPP.
-  - 3. it's difficult to prepare test data.
+- Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right:
+  1. you should get the right backpropagation formula according to the forward computation.
+  2. you should implement it right in CPP.
+  3. it's difficult to prepare test data.
 
-- Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
-  - 1. numeric gradient checker only need forward operator.
-  - 2. user only need to prepare the input data for forward Operator.
+- Auto gradient checking gets a numerical gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
+  1. numerical gradient checker only need forward operator.
+  2. user only need to prepare the input data for forward Operator.
 
 ## Mathematical Theory
-The following two document from stanford has a detailed explanation of how to get numeric gradient and why it's useful.
+The following two document from Stanford has a detailed explanation of how to get numerical gradient and why it's useful.
 
 - [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
 - [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
@@ -20,7 +20,7 @@ The following two document from stanford has a detailed explanation of how to ge
 ## Numeric Gradient Implementation
 ### Python Interface
 ```python
-def get_numeric_gradient(op,
+def get_numerical_gradient(op,
                          input_values,
                          output_name,
                          input_to_check,
@@ -30,13 +30,13 @@ def get_numeric_gradient(op,
     Get Numeric Gradient for an operator's input.
 
     :param op: C++ operator instance, could be an network
-    :param input_values: The input variables. Should be an dictionary, key is
-    variable name. Value is numpy array.
+    :param input_values: The input variables. Should be an dictionary, whose key is
+    variable name, and value is numpy array.
     :param output_name: The final output variable name.
-    :param input_to_check: The input variable need to get gradient.
+    :param input_to_check: The input variable with respect to which to compute the gradient.
     :param delta: The perturbation value for numeric gradient method. The
     smaller delta is, the more accurate result will get. But if that delta is
-     too small, it could occur numerical stability problem.
+     too small, it will suffer from numerical stability problem.
     :param local_scope: The local scope used for get_numeric_gradient.
     :return: The gradient array in numpy format.
     """
@@ -45,28 +45,28 @@ def get_numeric_gradient(op,
 ### Explaination:
 
 - Why need `output_name`
-  - One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.
+  - An Operator may have multiple Output, one can get independent gradient from each Output. So caller should specify the name of the output variable.
 
 - Why need `input_to_check`
-  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
+  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
 
 
 ### Core Algorithm Implementation
 
 
 ```python
-    # we only compute gradient of one element each time.
-    # we use a for loop to compute the gradient of every element.
+    # we only compute gradient of one element a time.
+    # we use a for loop to compute the gradient of each element.
     for i in xrange(tensor_size):
-        # get one input element throw it's index i.
+        # get one input element by its index i.
         origin = tensor_to_check.get_float_element(i)
 
-        # add delta to it, run op and then get the sum of the result tensor.
+        # add delta to it, run op and then get the new value of the result tensor.
         x_pos = origin + delta
         tensor_to_check.set_float_element(i, x_pos)
         y_pos = get_output()
 
-        # plus delta to this element, run op and get the sum of the result tensor.
+        # plus delta to this element, run op and get the new value of the result tensor.
         x_neg = origin - delta
         tensor_to_check.set_float_element(i, x_neg)
         y_neg = get_output()
@@ -85,15 +85,15 @@ def get_numeric_gradient(op,
 
 Each Operator Kernel has three kinds of Gradient:
 
-- 1. Numeric Gradient
-- 2. CPU Operator Gradient
-- 3. GPU Operator Gradient(if supported)
+1. Numerical gradient
+2. CPU kernel gradient
+3. GPU kernel gradient (if supported)
 
-Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.
+The numerical gradient only relies on forward Operator. So we use the numerical gradient as the reference value. And the gradient checking is performed in the following three steps:
 
-- 1. calculate the numeric gradient.
-- 2. calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.
-- 3. calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)
+1. calculate the numerical gradient
+2. calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient
+3. calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient (if supported)
 
 #### Python Interface
 
@@ -110,8 +110,8 @@ Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as
         :param forward_op: used to create backward_op
         :param input_vars: numpy value of input variable. The following
             computation will use these variables.
-        :param inputs_to_check: inputs var names that should check gradient.
-        :param output_name: output name that used to
+        :param inputs_to_check: the input variable with respect to which to compute the gradient.
+        :param output_name: The final output variable name.
         :param max_relative_error: The relative tolerance parameter.
         :param no_grad_set: used when create backward ops
         :param only_cpu: only compute and check gradient on cpu kernel.
@@ -120,24 +120,24 @@ Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as
 ```
 
 ### How to check if two numpy array is close enough?
-if `abs_numeric_grad` is nearly zero, then use abs error for numeric_grad, not relative
+if `abs_numerical_grad` is nearly zero, then use abs error for numerical_grad
 
 ```python
-numeric_grad = ...
+numerical_grad = ...
 operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
 
-abs_numeric_grad = numpy.abs(numeric_grad)
-# if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative
+abs_numerical_grad = numpy.abs(numerical_grad)
+# if abs_numerical_grad is nearly zero, then use abs error for numeric_grad, not relative
 # error.
-abs_numeric_grad[abs_numeric_grad < 1e-3] = 1
+abs_numerical_grad[abs_numerical_grad < 1e-3] = 1
 
-diff_mat = numpy.abs(abs_numeric_grad - operator_grad) / abs_numeric_grad
+diff_mat = numpy.abs(abs_numerical_grad - operator_grad) / abs_numerical_grad
 max_diff = numpy.max(diff_mat)
 ```
 
 
 #### Notes：
-1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.
+The Input data for auto gradient checker should be reasonable to avoid numerical  stability problem.
 
 
 #### Refs:

develop/doc_cn/_sources/design/functions_operators_layers.md.txt

@@ -53,12 +53,12 @@ Let's explain using an example.  Suppose that we are going to compose the FC usi
 ```python
 def operator.mul(X1, X2):
     O = Var()
-    paddle.cpp.create_operator("mul", input={X1, Y1], output=O)
+    paddle.cpp.create_operator("mul", input={X1, Y1}, output=O)
     return O
 
 def operator.add(X1, X2):
     O = Var()
-    paddle.cpp.create_operator("add", input={X1, X2], output=O)
+    paddle.cpp.create_operator("add", input={X1, X2}, output=O)
     return O
 ```
 

develop/doc_cn/_sources/design/graph.md.txt

@@ -56,7 +56,7 @@ For each parameter, like W and b created by `layer.fc`, marked as double circles
 
 ## Block and Graph
 
-The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
+The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block](https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
 
 A Block keeps operators in an array `BlockDesc::ops`
 
@@ -67,4 +67,4 @@ message BlockDesc {
 }
 ```
 
-in the order that there appear in user programs, like the Python program at the beginning of this article.  We can imagine that in `ops`,  we have some forward operators, followed by some gradient operators, and then some optimization operators.
+in the order that they appear in user programs, like the Python program at the beginning of this article.  We can imagine that in `ops`,  we have some forward operators, followed by some gradient operators, and then some optimization operators.

develop/doc_cn/_sources/design/parameters_in_cpp.md.txt

 # Design Doc: The C++ Class `Parameters`
 
-`Parameters` is a concept we designed in Paddle V2 API. `Parameters` is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
+`Parameters` is a concept we designed in PaddlePaddle V2 API. `Parameters` is a container of parameters, which makes PaddlePaddle capable of  sharing parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
 
-We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
+We used Python to implement Parameters when designing V2 API before. There are several defects for the current implementation:
 * We just use `memcpy` to share Parameters between topologies, but this is very inefficient. 
-* We did not implement share Parameters while training. We just trigger `memcpy` when start training.
+* We did not support sharing Parameters while training. We just trigger `memcpy` when start training.
 
-It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with `Parameters`:
+It is necessary that we implement Parameters in CPP side. However, it could result a code refactoring for PaddlePaddle, because PaddlePaddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current PaddlePaddle implementation, there are three concepts associated with `Parameters`:
 
 1. `paddle::Parameter`. A `Parameters` is a container for `paddle::Parameter`.
 It is evident that we should use `paddle::Parameter` when developing `Parameters`.
 However, the `Parameter` class contains many functions and does not have a clear interface.
 It contains `create/store Parameter`, `serialize/deserialize`, `optimize(i.e SGD)`, `randomize/zero`.
 When we developing `Parameters`, we only use `create/store Parameter` functionality.
-We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
+We should extract functionalities of Parameter into many classes to clean PaddlePaddle CPP implementation.
 
 2. `paddle::GradientMachine` and its sub-classes, e.g., `paddle::MultiGradientMachine`, `paddle::NeuralNetwork`.
 We should pass `Parameters` to `paddle::GradientMachine` when `forward/backward` to avoid `memcpy` between topologies.
@@ -24,7 +24,7 @@ Also, we should handle multi-GPU/CPU training, because `forward` and `backward`
 So `Parameters` should be used by `paddle::ParameterUpdater`, and `paddle::ParameterUpdater` should optimize `Parameters` (by SGD).
 
 
-The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
+The step by step approach for implementation Parameters in PaddlePaddle C++ core is listed below. Each step should be a PR and could be merged into PaddlePaddle one by one.
 
 1. Clean `paddle::Parameter` interface. Extract the functionalities of `paddle::Parameter` to prepare for the implementation of Parameters.
 

develop/doc_cn/_sources/design/reader/README.md.txt

@@ -52,7 +52,7 @@ Here are valid outputs:
 # a mini batch of three data items, each data item is a list (single column).
 [([1,1,1],),
 ([2,2,2],),
-([3,3,3],),
+([3,3,3],)]
 ```
 
 Please note that each item inside the list must be a tuple, below is an invalid output:

develop/doc_cn/_sources/design/refactorization.md.txt

@@ -15,7 +15,7 @@ The goal of refactorizaiton include:
 
 1. Users write Python programs to describe the graphs and run it (locally or remotely).
 
-1. A graph is composed of *variabels* and *operators*.
+1. A graph is composed of *variables* and *operators*.
 
 1. The description of graphs must be able to be serialized/deserialized, so it
 
@@ -140,7 +140,7 @@ Compile Time -> IR -> Runtime
     * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel.
     * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`.
 * Hand-writing `GPUKernel` and `CPU` code
-    * Do not write `.h`. CPU Kernel should be in `.cc`. CPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
+    * Do not write `.h`. CPU Kernel should be in `.cc`. GPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.)
 ---
 # Operator Register
 

develop/doc_cn/_sources/design/releasing_process.md.txt

-# Paddle发行规范
+# PaddlePaddle发行规范
 
-Paddle使用git-flow branching model做分支管理，使用[Semantic Versioning](http://semver.org/)标准表示Paddle版本号。
+PaddlePaddle使用git-flow branching model做分支管理，使用[Semantic Versioning](http://semver.org/)标准表示PaddlePaddle版本号。
 
-Paddle每次发新的版本，遵循以下流程:
+PaddlePaddle每次发新的版本，遵循以下流程:
 
 1. 从`develop`分支派生出新的分支，分支名为`release/版本号`。例如，`release/0.10.0`
 2. 将新分支的版本打上tag，tag为`版本号rc.Patch号`。第一个tag为`0.10.0rc1`，第二个为`0.10.0rc2`，依次类推。
@@ -27,14 +27,14 @@ Paddle每次发新的版本，遵循以下流程:
 
 需要注意的是:
 
-* `release/版本号`分支一旦建立，一般不允许再从`develop`分支合入`release/版本号`。这样保证`release/版本号`分支功能的封闭，方便测试人员测试Paddle的行为。
+* `release/版本号`分支一旦建立，一般不允许再从`develop`分支合入`release/版本号`。这样保证`release/版本号`分支功能的封闭，方便测试人员测试PaddlePaddle的行为。
 * 在`release/版本号`分支存在的时候，如果有bugfix的行为，需要将bugfix的分支同时merge到`master`, `develop`和`release/版本号`这三个分支。
 
-# Paddle 分支规范
+# PaddlePaddle 分支规范
 
-Paddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，并适应github的特性做了一些区别。
+PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，并适应github的特性做了一些区别。
 
-* Paddle的主版本库遵循[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范。其中:
+* PaddlePaddle的主版本库遵循[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范。其中:
 	* `master`分支为稳定(stable branch)版本分支。每一个`master`分支的版本都是经过单元测试和回归测试的版本。
 	* `develop`分支为开发(develop branch)版本分支。每一个`develop`分支的版本都经过单元测试，但并没有经过回归测试。
 	* `release/版本号`分支为每一次Release时建立的临时分支。在这个阶段的代码正在经历回归测试。
@@ -42,18 +42,18 @@ Paddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-branch
 * 其他用户的fork版本库并不需要严格遵守[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，但所有fork的版本库的所有分支都相当于特性分支。
 	* 建议，开发者fork的版本库使用`develop`分支同步主版本库的`develop`分支
 	* 建议，开发者fork的版本库中，再基于`develop`版本fork出自己的功能分支。
-	* 当功能分支开发完毕后，向Paddle的主版本库提交`Pull Reuqest`，进而进行代码评审。
+	* 当功能分支开发完毕后，向PaddlePaddle的主版本库提交`Pull Reuqest`，进而进行代码评审。
 		* 在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。 
 
 * BugFix分支也是在开发者自己的fork版本库维护，与功能分支不同的是，BugFix分支需要分别给主版本库的`master`、`develop`与可能有的`release/版本号`分支，同时提起`Pull Request`。
 
-# Paddle回归测试列表
+# PaddlePaddle回归测试列表
 
-本列表说明Paddle发版之前需要测试的功能点。
+本列表说明PaddlePaddle发版之前需要测试的功能点。
 
-## Paddle Book中所有章节
+## PaddlePaddle Book中所有章节
 
-Paddle每次发版本首先要保证Paddle Book中所有章节功能的正确性。功能的正确性包括验证Paddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
+PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
 
 | | 新手入门章节 | 识别数字 | 图像分类 | 词向量 | 情感分析 | 语意角色标注 | 机器翻译 | 个性化推荐 |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |

develop/doc_cn/_sources/design/scope.md.txt

@@ -17,7 +17,7 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
 
 1. Scope only contains a map of a name to variable.
 
-   All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state(momentum) etc.
+   All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state (momentum) etc.
 
 1. Variable can only be created by Scope and a variable can only be got from Scope. User cannot create or get a variable outside a scope. This is a constraints of our framework, and will keep our framework simple and clear.
 
@@ -32,7 +32,7 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
 
 1. Scope should destruct all Variables inside it when itself is destructed. User can never store `Variable` pointer somewhere else. 
 
-   Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store `Variable` pointer to private data member or some global variable, the pointer will be a invalid pointer when associated `Scope` is destroyed.
+   Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store `Variable` pointer to private data member or some global variable, the pointer will be an invalid pointer when associated `Scope` is destroyed.
 
 ```cpp
 class Scope {
@@ -50,7 +50,7 @@ class Scope {
 
 Just like [scope](https://en.wikipedia.org/wiki/Scope_(computer_science)) in programming languages, `Scope` in the neural network can also be a local scope. There are two attributes about local scope.
 
-1.  We can create local variables in a local scope. When that local scope are destroyed, all local variables should also be destroyed.
+1.  We can create local variables in a local scope. When that local scope is destroyed, all local variables should also be destroyed.
 2.  Variables in a parent scope can be retrieved from local scopes of that parent scope, i.e., when user get a variable from a scope, it will try to search this variable in current scope. If there is no such variable in the local scope, `scope` will keep searching from its parent, until the variable is found or there is no parent.
 
 ```cpp
@@ -121,4 +121,4 @@ Also, as the parent scope is a `shared_ptr`, we can only `Create()` a scope shar
 
 ## Orthogonal interface
 
-`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `NewVar` will return a `Error` when there is a name conflict locally. Combine `FindVar` and `NewVar`, we can implement `NewVar` easily.
+`FindVar` will return `nullptr` when `name` is not found. It can be used as `Contains` method. `NewVar` will return an `Error` when there is a name conflict locally. Combine `FindVar` and `NewVar`, we can implement `NewVar` easily.

develop/doc_cn/_sources/design/simple_op_design.md.txt

@@ -6,9 +6,9 @@ The Interaction between Python and C++ can be simplified as two steps:
 
 1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
 
-2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
+2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ for finishing the Op construction task.
 
-### Message form C++ to Python
+### Message from C++ to Python
 
 We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”
 

develop/doc_cn/_sources/design/var_desc.md.txt

 ## Background
 PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
 
-PaddlePaddle use proto message to describe compile time graph for
+PaddlePaddle use proto message to describe compile time graph because
 
 1. Computation graph should be able to be saved to a file.
 1. In distributed training, the graph will be serialized and send to multiple workers.

develop/doc_cn/design/api.html

@@ -189,7 +189,7 @@
 <div class="section" id="ingredients">
 <span id="ingredients"></span><h2>Ingredients<a class="headerlink" href="#ingredients" title="永久链接至标题">¶</a></h2>
 <p>As our design principle is starting from the essence: how could we
-allow users to express and solve their problems at neural networks.
+allow users to express and solve their problems as neural networks.
 Some essential concepts that our API have to provide include:</p>
 <ol class="simple">
 <li>A <em>topology</em> is an expression of <em>layers</em>.</li>
@@ -392,7 +392,7 @@ access a Kubernetes cluster, s/he should be able to call</p>
                   <span class="n">num_parameter_servers</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>The pseudo code if <code class="docutils literal"><span class="pre">paddle.dist_train</span></code> is as follows:</p>
+<p>The pseudo code of <code class="docutils literal"><span class="pre">paddle.dist_train</span></code> is as follows:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">dist_train</span><span class="p">(</span><span class="n">topology</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">trainer</span><span class="p">,</span> <span class="n">reader</span><span class="p">,</span> <span class="o">...</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;KUBERNETES_SERVICE_HOST&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="bp">None</span><span class="p">:</span>
         <span class="n">image_name</span> <span class="o">=</span> <span class="n">k8s_user</span> <span class="o">+</span> <span class="s1">&#39;/&#39;</span> <span class="o">+</span> <span class="n">k8s_job</span>

develop/doc_cn/design/auto_gradient_check.html

@@ -190,37 +190,22 @@
 <div class="section" id="backgraound">
 <span id="backgraound"></span><h1>Backgraound：<a class="headerlink" href="#backgraound" title="永久链接至标题">¶</a></h1>
 <ul class="simple">
-<li>Operator forward computing is easy to check if the result is right because it has a clear definition. <strong>But</strong> backpropagation is a notoriously difficult algorithm to debug and get right:<ul>
-<li><ol class="first">
+<li>Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right:<ol>
 <li>you should get the right backpropagation formula according to the forward computation.</li>
-</ol>
-</li>
-<li><ol class="first">
 <li>you should implement it right in CPP.</li>
-</ol>
-</li>
-<li><ol class="first">
 <li>it&#8217;s difficult to prepare test data.</li>
 </ol>
 </li>
-</ul>
-</li>
-<li>Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator&#8217;s result. It has several advantages:<ul>
-<li><ol class="first">
-<li>numeric gradient checker only need forward operator.</li>
-</ol>
-</li>
-<li><ol class="first">
+<li>Auto gradient checking gets a numerical gradient by forward Operator and use it as a reference of the backward Operator&#8217;s result. It has several advantages:<ol>
+<li>numerical gradient checker only need forward operator.</li>
 <li>user only need to prepare the input data for forward Operator.</li>
 </ol>
 </li>
 </ul>
-</li>
-</ul>
 </div>
 <div class="section" id="mathematical-theory">
 <span id="mathematical-theory"></span><h1>Mathematical Theory<a class="headerlink" href="#mathematical-theory" title="永久链接至标题">¶</a></h1>
-<p>The following two document from stanford has a detailed explanation of how to get numeric gradient and why it&#8217;s useful.</p>
+<p>The following two document from Stanford has a detailed explanation of how to get numerical gradient and why it&#8217;s useful.</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference external" href="http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization">Gradient checking and advanced optimization(en)</a></li>
@@ -232,7 +217,7 @@
 <span id="numeric-gradient-implementation"></span><h1>Numeric Gradient Implementation<a class="headerlink" href="#numeric-gradient-implementation" title="永久链接至标题">¶</a></h1>
 <div class="section" id="python-interface">
 <span id="python-interface"></span><h2>Python Interface<a class="headerlink" href="#python-interface" title="永久链接至标题">¶</a></h2>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_numeric_gradient</span><span class="p">(</span><span class="n">op</span><span class="p">,</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_numerical_gradient</span><span class="p">(</span><span class="n">op</span><span class="p">,</span>
                          <span class="n">input_values</span><span class="p">,</span>
                          <span class="n">output_name</span><span class="p">,</span>
                          <span class="n">input_to_check</span><span class="p">,</span>
@@ -242,13 +227,13 @@
 <span class="sd">    Get Numeric Gradient for an operator&#39;s input.</span>
 
 <span class="sd">    :param op: C++ operator instance, could be an network</span>
-<span class="sd">    :param input_values: The input variables. Should be an dictionary, key is</span>
-<span class="sd">    variable name. Value is numpy array.</span>
+<span class="sd">    :param input_values: The input variables. Should be an dictionary, whose key is</span>
+<span class="sd">    variable name, and value is numpy array.</span>
 <span class="sd">    :param output_name: The final output variable name.</span>
-<span class="sd">    :param input_to_check: The input variable need to get gradient.</span>
+<span class="sd">    :param input_to_check: The input variable with respect to which to compute the gradient.</span>
 <span class="sd">    :param delta: The perturbation value for numeric gradient method. The</span>
 <span class="sd">    smaller delta is, the more accurate result will get. But if that delta is</span>
-<span class="sd">     too small, it could occur numerical stability problem.</span>
+<span class="sd">     too small, it will suffer from numerical stability problem.</span>
 <span class="sd">    :param local_scope: The local scope used for get_numeric_gradient.</span>
 <span class="sd">    :return: The gradient array in numpy format.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -259,29 +244,29 @@
 <span id="explaination"></span><h2>Explaination:<a class="headerlink" href="#explaination" title="永久链接至标题">¶</a></h2>
 <ul class="simple">
 <li>Why need <code class="docutils literal"><span class="pre">output_name</span></code><ul>
-<li>One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.</li>
+<li>An Operator may have multiple Output, one can get independent gradient from each Output. So caller should specify the name of the output variable.</li>
 </ul>
 </li>
 <li>Why need <code class="docutils literal"><span class="pre">input_to_check</span></code><ul>
-<li>One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> multiple times.</li>
+<li>One operator may have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numeric Gradient needs to calculate them one by one. So <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call <code class="docutils literal"><span class="pre">get_numeric_gradient</span></code> multiple times.</li>
 </ul>
 </li>
 </ul>
 </div>
 <div class="section" id="core-algorithm-implementation">
 <span id="core-algorithm-implementation"></span><h2>Core Algorithm Implementation<a class="headerlink" href="#core-algorithm-implementation" title="永久链接至标题">¶</a></h2>
-<div class="highlight-python"><div class="highlight"><pre><span></span>    <span class="c1"># we only compute gradient of one element each time.</span>
-    <span class="c1"># we use a for loop to compute the gradient of every element.</span>
+<div class="highlight-python"><div class="highlight"><pre><span></span>    <span class="c1"># we only compute gradient of one element a time.</span>
+    <span class="c1"># we use a for loop to compute the gradient of each element.</span>
     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">tensor_size</span><span class="p">):</span>
-        <span class="c1"># get one input element throw it&#39;s index i.</span>
+        <span class="c1"># get one input element by its index i.</span>
         <span class="n">origin</span> <span class="o">=</span> <span class="n">tensor_to_check</span><span class="o">.</span><span class="n">get_float_element</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
 
-        <span class="c1"># add delta to it, run op and then get the sum of the result tensor.</span>
+        <span class="c1"># add delta to it, run op and then get the new value of the result tensor.</span>
         <span class="n">x_pos</span> <span class="o">=</span> <span class="n">origin</span> <span class="o">+</span> <span class="n">delta</span>
         <span class="n">tensor_to_check</span><span class="o">.</span><span class="n">set_float_element</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">x_pos</span><span class="p">)</span>
         <span class="n">y_pos</span> <span class="o">=</span> <span class="n">get_output</span><span class="p">()</span>
 
-        <span class="c1"># plus delta to this element, run op and get the sum of the result tensor.</span>
+        <span class="c1"># plus delta to this element, run op and get the new value of the result tensor.</span>
         <span class="n">x_neg</span> <span class="o">=</span> <span class="n">origin</span> <span class="o">-</span> <span class="n">delta</span>
         <span class="n">tensor_to_check</span><span class="o">.</span><span class="n">set_float_element</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">x_neg</span><span class="p">)</span>
         <span class="n">y_neg</span> <span class="o">=</span> <span class="n">get_output</span><span class="p">()</span>
@@ -301,35 +286,17 @@
 <div class="section" id="auto-graident-checker-framework">
 <span id="auto-graident-checker-framework"></span><h1>Auto Graident Checker Framework<a class="headerlink" href="#auto-graident-checker-framework" title="永久链接至标题">¶</a></h1>
 <p>Each Operator Kernel has three kinds of Gradient:</p>
-<ul class="simple">
-<li><ol class="first">
-<li>Numeric Gradient</li>
+<ol class="simple">
+<li>Numerical gradient</li>
+<li>CPU kernel gradient</li>
+<li>GPU kernel gradient (if supported)</li>
 </ol>
-</li>
-<li><ol class="first">
-<li>CPU Operator Gradient</li>
+<p>The numerical gradient only relies on forward Operator. So we use the numerical gradient as the reference value. And the gradient checking is performed in the following three steps:</p>
+<ol class="simple">
+<li>calculate the numerical gradient</li>
+<li>calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient</li>
+<li>calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient (if supported)</li>
 </ol>
-</li>
-<li><ol class="first">
-<li>GPU Operator Gradient(if supported)</li>
-</ol>
-</li>
-</ul>
-<p>Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.</p>
-<ul class="simple">
-<li><ol class="first">
-<li>calculate the numeric gradient.</li>
-</ol>
-</li>
-<li><ol class="first">
-<li>calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.</li>
-</ol>
-</li>
-<li><ol class="first">
-<li>calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)</li>
-</ol>
-</li>
-</ul>
 <div class="section" id="python-interface">
 <span id="id1"></span><h2>Python Interface<a class="headerlink" href="#python-interface" title="永久链接至标题">¶</a></h2>
 <div class="highlight-python"><div class="highlight"><pre><span></span>    <span class="k">def</span> <span class="nf">check_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
@@ -344,8 +311,8 @@
 <span class="sd">        :param forward_op: used to create backward_op</span>
 <span class="sd">        :param input_vars: numpy value of input variable. The following</span>
 <span class="sd">            computation will use these variables.</span>
-<span class="sd">        :param inputs_to_check: inputs var names that should check gradient.</span>
-<span class="sd">        :param output_name: output name that used to</span>
+<span class="sd">        :param inputs_to_check: the input variable with respect to which to compute the gradient.</span>
+<span class="sd">        :param output_name: The final output variable name.</span>
 <span class="sd">        :param max_relative_error: The relative tolerance parameter.</span>
 <span class="sd">        :param no_grad_set: used when create backward ops</span>
 <span class="sd">        :param only_cpu: only compute and check gradient on cpu kernel.</span>
@@ -356,22 +323,22 @@
 </div>
 <div class="section" id="how-to-check-if-two-numpy-array-is-close-enough">
 <span id="how-to-check-if-two-numpy-array-is-close-enough"></span><h2>How to check if two numpy array is close enough?<a class="headerlink" href="#how-to-check-if-two-numpy-array-is-close-enough" title="永久链接至标题">¶</a></h2>
-<p>if <code class="docutils literal"><span class="pre">abs_numeric_grad</span></code> is nearly zero, then use abs error for numeric_grad, not relative</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">numeric_grad</span> <span class="o">=</span> <span class="o">...</span>
+<p>if <code class="docutils literal"><span class="pre">abs_numerical_grad</span></code> is nearly zero, then use abs error for numerical_grad</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">numerical_grad</span> <span class="o">=</span> <span class="o">...</span>
 <span class="n">operator_grad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">scope</span><span class="o">.</span><span class="n">find_var</span><span class="p">(</span><span class="n">grad_var_name</span><span class="p">(</span><span class="n">name</span><span class="p">))</span><span class="o">.</span><span class="n">get_tensor</span><span class="p">())</span>
 
-<span class="n">abs_numeric_grad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">numeric_grad</span><span class="p">)</span>
-<span class="c1"># if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative</span>
+<span class="n">abs_numerical_grad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">numerical_grad</span><span class="p">)</span>
+<span class="c1"># if abs_numerical_grad is nearly zero, then use abs error for numeric_grad, not relative</span>
 <span class="c1"># error.</span>
-<span class="n">abs_numeric_grad</span><span class="p">[</span><span class="n">abs_numeric_grad</span> <span class="o">&lt;</span> <span class="mf">1e-3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">abs_numerical_grad</span><span class="p">[</span><span class="n">abs_numerical_grad</span> <span class="o">&lt;</span> <span class="mf">1e-3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
 
-<span class="n">diff_mat</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">abs_numeric_grad</span> <span class="o">-</span> <span class="n">operator_grad</span><span class="p">)</span> <span class="o">/</span> <span class="n">abs_numeric_grad</span>
+<span class="n">diff_mat</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">abs_numerical_grad</span> <span class="o">-</span> <span class="n">operator_grad</span><span class="p">)</span> <span class="o">/</span> <span class="n">abs_numerical_grad</span>
 <span class="n">max_diff</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">diff_mat</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="section" id="notes">
 <span id="notes"></span><h3>Notes：<a class="headerlink" href="#notes" title="永久链接至标题">¶</a></h3>
-<p>1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.</p>
+<p>The Input data for auto gradient checker should be reasonable to avoid numerical  stability problem.</p>
 </div>
 <div class="section" id="refs">
 <span id="refs"></span><h3>Refs:<a class="headerlink" href="#refs" title="永久链接至标题">¶</a></h3>

develop/doc_cn/design/functions_operators_layers.html

@@ -224,12 +224,12 @@
 <p>Let&#8217;s explain using an example.  Suppose that we are going to compose the FC using mul and add in Python, we&#8217;d like to have Python functions <code class="docutils literal"><span class="pre">mul</span></code> and <code class="docutils literal"><span class="pre">add</span></code> defined in module <code class="docutils literal"><span class="pre">operator</span></code>:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">operator</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">):</span>
     <span class="n">O</span> <span class="o">=</span> <span class="n">Var</span><span class="p">()</span>
-    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">Y1</span><span class="p">],</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
+    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">Y1</span><span class="p">},</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">O</span>
 
 <span class="k">def</span> <span class="nf">operator</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">):</span>
     <span class="n">O</span> <span class="o">=</span> <span class="n">Var</span><span class="p">()</span>
-    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;add&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">],</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
+    <span class="n">paddle</span><span class="o">.</span><span class="n">cpp</span><span class="o">.</span><span class="n">create_operator</span><span class="p">(</span><span class="s2">&quot;add&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">},</span> <span class="n">output</span><span class="o">=</span><span class="n">O</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">O</span>
 </pre></div>
 </div>

develop/doc_cn/design/graph.html

@@ -233,7 +233,7 @@
 </div>
 <div class="section" id="block-and-graph">
 <span id="block-and-graph"></span><h2>Block and Graph<a class="headerlink" href="#block-and-graph" title="永久链接至标题">¶</a></h2>
-<p>The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.</p>
+<p>The word block and graph are interchangable in the desgin of PaddlePaddle.  A <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/3708">Block</a> is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.</p>
 <p>A Block keeps operators in an array <code class="docutils literal"><span class="pre">BlockDesc::ops</span></code></p>
 <div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">BlockDesc</span> <span class="p">{</span>
   <span class="k">repeated</span> <span class="n">OpDesc</span> <span class="na">ops</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
@@ -241,7 +241,7 @@
 <span class="p">}</span>
 </pre></div>
 </div>
-<p>in the order that there appear in user programs, like the Python program at the beginning of this article.  We can imagine that in <code class="docutils literal"><span class="pre">ops</span></code>,  we have some forward operators, followed by some gradient operators, and then some optimization operators.</p>
+<p>in the order that they appear in user programs, like the Python program at the beginning of this article.  We can imagine that in <code class="docutils literal"><span class="pre">ops</span></code>,  we have some forward operators, followed by some gradient operators, and then some optimization operators.</p>
 </div>
 </div>
 

develop/doc_cn/design/parameters_in_cpp.html

@@ -186,20 +186,20 @@
             
   <div class="section" id="design-doc-the-c-class-parameters">
 <span id="design-doc-the-c-class-parameters"></span><h1>Design Doc: The C++ Class <code class="docutils literal"><span class="pre">Parameters</span></code><a class="headerlink" href="#design-doc-the-c-class-parameters" title="永久链接至标题">¶</a></h1>
-<p><code class="docutils literal"><span class="pre">Parameters</span></code> is a concept we designed in Paddle V2 API. <code class="docutils literal"><span class="pre">Parameters</span></code> is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of <code class="docutils literal"><span class="pre">Parameter</span></code> in <a class="reference internal" href="api.html"><span class="doc">api.md</span></a>.</p>
-<p>We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:</p>
+<p><code class="docutils literal"><span class="pre">Parameters</span></code> is a concept we designed in PaddlePaddle V2 API. <code class="docutils literal"><span class="pre">Parameters</span></code> is a container of parameters, which makes PaddlePaddle capable of  sharing parameter between topologies. We described usages of <code class="docutils literal"><span class="pre">Parameter</span></code> in <a class="reference internal" href="api.html"><span class="doc">api.md</span></a>.</p>
+<p>We used Python to implement Parameters when designing V2 API before. There are several defects for the current implementation:</p>
 <ul class="simple">
 <li>We just use <code class="docutils literal"><span class="pre">memcpy</span></code> to share Parameters between topologies, but this is very inefficient.</li>
-<li>We did not implement share Parameters while training. We just trigger <code class="docutils literal"><span class="pre">memcpy</span></code> when start training.</li>
+<li>We did not support sharing Parameters while training. We just trigger <code class="docutils literal"><span class="pre">memcpy</span></code> when start training.</li>
 </ul>
-<p>It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with <code class="docutils literal"><span class="pre">Parameters</span></code>:</p>
+<p>It is necessary that we implement Parameters in CPP side. However, it could result a code refactoring for PaddlePaddle, because PaddlePaddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current PaddlePaddle implementation, there are three concepts associated with <code class="docutils literal"><span class="pre">Parameters</span></code>:</p>
 <ol class="simple">
 <li><code class="docutils literal"><span class="pre">paddle::Parameter</span></code>. A <code class="docutils literal"><span class="pre">Parameters</span></code> is a container for <code class="docutils literal"><span class="pre">paddle::Parameter</span></code>.
 It is evident that we should use <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> when developing <code class="docutils literal"><span class="pre">Parameters</span></code>.
 However, the <code class="docutils literal"><span class="pre">Parameter</span></code> class contains many functions and does not have a clear interface.
 It contains <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code>, <code class="docutils literal"><span class="pre">serialize/deserialize</span></code>, <code class="docutils literal"><span class="pre">optimize(i.e</span> <span class="pre">SGD)</span></code>, <code class="docutils literal"><span class="pre">randomize/zero</span></code>.
 When we developing <code class="docutils literal"><span class="pre">Parameters</span></code>, we only use <code class="docutils literal"><span class="pre">create/store</span> <span class="pre">Parameter</span></code> functionality.
-We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.</li>
+We should extract functionalities of Parameter into many classes to clean PaddlePaddle CPP implementation.</li>
 <li><code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> and its sub-classes, e.g., <code class="docutils literal"><span class="pre">paddle::MultiGradientMachine</span></code>, <code class="docutils literal"><span class="pre">paddle::NeuralNetwork</span></code>.
 We should pass <code class="docutils literal"><span class="pre">Parameters</span></code> to <code class="docutils literal"><span class="pre">paddle::GradientMachine</span></code> when <code class="docutils literal"><span class="pre">forward/backward</span></code> to avoid <code class="docutils literal"><span class="pre">memcpy</span></code> between topologies.
 Also, we should handle multi-GPU/CPU training, because <code class="docutils literal"><span class="pre">forward</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> would perform on multi-GPUs and multi-CPUs.
@@ -207,7 +207,7 @@ Also, we should handle multi-GPU/CPU training, because <code class="docutils lit
 <li><code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>. The ParameterUpdater is used to update parameters in Paddle.
 So <code class="docutils literal"><span class="pre">Parameters</span></code> should be used by <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code>, and <code class="docutils literal"><span class="pre">paddle::ParameterUpdater</span></code> should optimize <code class="docutils literal"><span class="pre">Parameters</span></code> (by SGD).</li>
 </ol>
-<p>The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.</p>
+<p>The step by step approach for implementation Parameters in PaddlePaddle C++ core is listed below. Each step should be a PR and could be merged into PaddlePaddle one by one.</p>
 <ol class="simple">
 <li>Clean <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> interface. Extract the functionalities of <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> to prepare for the implementation of Parameters.</li>
 <li>Implementation a <code class="docutils literal"><span class="pre">Parameters</span></code> class. It just stores the <code class="docutils literal"><span class="pre">paddle::Parameter</span></code> inside. Make <code class="docutils literal"><span class="pre">GradientMachine</span></code> uses <code class="docutils literal"><span class="pre">Parameters</span></code> as a class member.</li>

develop/doc_cn/design/reader/README.html

@@ -230,7 +230,7 @@
 <span class="c1"># a mini batch of three data items, each data item is a list (single column).</span>
 <span class="p">[([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],),</span>
 <span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">],),</span>
-<span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],),</span>
+<span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">],)]</span>
 </pre></div>
 </div>
 <p>Please note that each item inside the list must be a tuple, below is an invalid output:</p>

develop/doc_cn/design/refactorization.html

@@ -199,7 +199,7 @@
 <li>PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.</li>
 <li>Please dig into <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md">computation graphs</a> for a solid example.</li>
 <li>Users write Python programs to describe the graphs and run it (locally or remotely).</li>
-<li>A graph is composed of <em>variabels</em> and <em>operators</em>.</li>
+<li>A graph is composed of <em>variables</em> and <em>operators</em>.</li>
 <li>The description of graphs must be able to be serialized/deserialized, so it<ol>
 <li>could to be sent to the cloud for distributed execution, and</li>
 <li>be sent to clients for mobile or enterprise deployment.</li>
@@ -353,7 +353,7 @@
 </ul>
 </li>
 <li>Hand-writing <code class="docutils literal"><span class="pre">GPUKernel</span></code> and <code class="docutils literal"><span class="pre">CPU</span></code> code<ul>
-<li>Do not write <code class="docutils literal"><span class="pre">.h</span></code>. CPU Kernel should be in <code class="docutils literal"><span class="pre">.cc</span></code>. CPU kernel should be in <code class="docutils literal"><span class="pre">.cu</span></code>. (<code class="docutils literal"><span class="pre">GCC</span></code> cannot compile GPU code.)</li>
+<li>Do not write <code class="docutils literal"><span class="pre">.h</span></code>. CPU Kernel should be in <code class="docutils literal"><span class="pre">.cc</span></code>. GPU kernel should be in <code class="docutils literal"><span class="pre">.cu</span></code>. (<code class="docutils literal"><span class="pre">GCC</span></code> cannot compile GPU code.)</li>
 </ul>
 </li>
 </ul>

develop/doc_cn/design/releasing_process.html

@@ -8,7 +8,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Paddle发行规范 &mdash; PaddlePaddle  文档</title>
+  <title>PaddlePaddle发行规范 &mdash; PaddlePaddle  文档</title>
   
 
   
@@ -175,7 +175,7 @@
 <div role="navigation" aria-label="breadcrumbs navigation">
   <ul class="wy-breadcrumbs">
       
-    <li>Paddle发行规范</li>
+    <li>PaddlePaddle发行规范</li>
   </ul>
 </div>
       
@@ -184,10 +184,10 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="paddle">
-<span id="paddle"></span><h1>Paddle发行规范<a class="headerlink" href="#paddle" title="永久链接至标题">¶</a></h1>
-<p>Paddle使用git-flow branching model做分支管理，使用<a class="reference external" href="http://semver.org/">Semantic Versioning</a>标准表示Paddle版本号。</p>
-<p>Paddle每次发新的版本，遵循以下流程:</p>
+  <div class="section" id="paddlepaddle">
+<span id="paddlepaddle"></span><h1>PaddlePaddle发行规范<a class="headerlink" href="#paddlepaddle" title="永久链接至标题">¶</a></h1>
+<p>PaddlePaddle使用git-flow branching model做分支管理，使用<a class="reference external" href="http://semver.org/">Semantic Versioning</a>标准表示PaddlePaddle版本号。</p>
+<p>PaddlePaddle每次发新的版本，遵循以下流程:</p>
 <ol>
 <li><p class="first">从<code class="docutils literal"><span class="pre">develop</span></code>分支派生出新的分支，分支名为<code class="docutils literal"><span class="pre">release/版本号</span></code>。例如，<code class="docutils literal"><span class="pre">release/0.10.0</span></code></p>
 </li>
@@ -230,15 +230,15 @@
 </ol>
 <p>需要注意的是:</p>
 <ul class="simple">
-<li><code class="docutils literal"><span class="pre">release/版本号</span></code>分支一旦建立，一般不允许再从<code class="docutils literal"><span class="pre">develop</span></code>分支合入<code class="docutils literal"><span class="pre">release/版本号</span></code>。这样保证<code class="docutils literal"><span class="pre">release/版本号</span></code>分支功能的封闭，方便测试人员测试Paddle的行为。</li>
+<li><code class="docutils literal"><span class="pre">release/版本号</span></code>分支一旦建立，一般不允许再从<code class="docutils literal"><span class="pre">develop</span></code>分支合入<code class="docutils literal"><span class="pre">release/版本号</span></code>。这样保证<code class="docutils literal"><span class="pre">release/版本号</span></code>分支功能的封闭，方便测试人员测试PaddlePaddle的行为。</li>
 <li>在<code class="docutils literal"><span class="pre">release/版本号</span></code>分支存在的时候，如果有bugfix的行为，需要将bugfix的分支同时merge到<code class="docutils literal"><span class="pre">master</span></code>, <code class="docutils literal"><span class="pre">develop</span></code>和<code class="docutils literal"><span class="pre">release/版本号</span></code>这三个分支。</li>
 </ul>
 </div>
-<div class="section" id="paddle">
-<span id="id1"></span><h1>Paddle 分支规范<a class="headerlink" href="#paddle" title="永久链接至标题">¶</a></h1>
-<p>Paddle开发过程使用<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范，并适应github的特性做了一些区别。</p>
+<div class="section" id="paddlepaddle">
+<span id="id1"></span><h1>PaddlePaddle 分支规范<a class="headerlink" href="#paddlepaddle" title="永久链接至标题">¶</a></h1>
+<p>PaddlePaddle开发过程使用<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范，并适应github的特性做了一些区别。</p>
 <ul class="simple">
-<li>Paddle的主版本库遵循<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范。其中:<ul>
+<li>PaddlePaddle的主版本库遵循<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范。其中:<ul>
 <li><code class="docutils literal"><span class="pre">master</span></code>分支为稳定(stable branch)版本分支。每一个<code class="docutils literal"><span class="pre">master</span></code>分支的版本都是经过单元测试和回归测试的版本。</li>
 <li><code class="docutils literal"><span class="pre">develop</span></code>分支为开发(develop branch)版本分支。每一个<code class="docutils literal"><span class="pre">develop</span></code>分支的版本都经过单元测试，但并没有经过回归测试。</li>
 <li><code class="docutils literal"><span class="pre">release/版本号</span></code>分支为每一次Release时建立的临时分支。在这个阶段的代码正在经历回归测试。</li>
@@ -247,7 +247,7 @@
 <li>其他用户的fork版本库并不需要严格遵守<a class="reference external" href="http://nvie.com/posts/a-successful-git-branching-model/">git-flow</a>分支规范，但所有fork的版本库的所有分支都相当于特性分支。<ul>
 <li>建议，开发者fork的版本库使用<code class="docutils literal"><span class="pre">develop</span></code>分支同步主版本库的<code class="docutils literal"><span class="pre">develop</span></code>分支</li>
 <li>建议，开发者fork的版本库中，再基于<code class="docutils literal"><span class="pre">develop</span></code>版本fork出自己的功能分支。</li>
-<li>当功能分支开发完毕后，向Paddle的主版本库提交<code class="docutils literal"><span class="pre">Pull</span> <span class="pre">Reuqest</span></code>，进而进行代码评审。<ul>
+<li>当功能分支开发完毕后，向PaddlePaddle的主版本库提交<code class="docutils literal"><span class="pre">Pull</span> <span class="pre">Reuqest</span></code>，进而进行代码评审。<ul>
 <li>在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。</li>
 </ul>
 </li>
@@ -256,12 +256,12 @@
 <li>BugFix分支也是在开发者自己的fork版本库维护，与功能分支不同的是，BugFix分支需要分别给主版本库的<code class="docutils literal"><span class="pre">master</span></code>、<code class="docutils literal"><span class="pre">develop</span></code>与可能有的<code class="docutils literal"><span class="pre">release/版本号</span></code>分支，同时提起<code class="docutils literal"><span class="pre">Pull</span> <span class="pre">Request</span></code>。</li>
 </ul>
 </div>
-<div class="section" id="paddle">
-<span id="id2"></span><h1>Paddle回归测试列表<a class="headerlink" href="#paddle" title="永久链接至标题">¶</a></h1>
-<p>本列表说明Paddle发版之前需要测试的功能点。</p>
-<div class="section" id="paddle-book">
-<span id="paddle-book"></span><h2>Paddle Book中所有章节<a class="headerlink" href="#paddle-book" title="永久链接至标题">¶</a></h2>
-<p>Paddle每次发版本首先要保证Paddle Book中所有章节功能的正确性。功能的正确性包括验证Paddle目前的<code class="docutils literal"><span class="pre">paddle_trainer</span></code>训练和纯使用<code class="docutils literal"><span class="pre">Python</span></code>训练模型正确性。</p>
+<div class="section" id="paddlepaddle">
+<span id="id2"></span><h1>PaddlePaddle回归测试列表<a class="headerlink" href="#paddlepaddle" title="永久链接至标题">¶</a></h1>
+<p>本列表说明PaddlePaddle发版之前需要测试的功能点。</p>
+<div class="section" id="paddlepaddle-book">
+<span id="paddlepaddle-book"></span><h2>PaddlePaddle Book中所有章节<a class="headerlink" href="#paddlepaddle-book" title="永久链接至标题">¶</a></h2>
+<p>PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的<code class="docutils literal"><span class="pre">paddle_trainer</span></code>训练和纯使用<code class="docutils literal"><span class="pre">Python</span></code>训练模型正确性。</p>
 <p>| | 新手入门章节 | 识别数字 | 图像分类 | 词向量 | 情感分析 | 语意角色标注 | 机器翻译 | 个性化推荐 |
 | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; | &#8212; |
 | API.V2 + Docker + GPU  |  |  |  |  |  |  |  |  |

develop/doc_cn/design/scope.html

@@ -200,7 +200,7 @@
 <p>Scope is an association of a name to variable. All variables belong to <code class="docutils literal"><span class="pre">Scope</span></code>. You need to specify a scope to run a Net, i.e., <code class="docutils literal"><span class="pre">net.Run(&amp;scope)</span></code>. One net can run in different scopes and update different variable in the scope.</p>
 <ol>
 <li><p class="first">Scope only contains a map of a name to variable.</p>
-<p>All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state(momentum) etc.</p>
+<p>All parameters, data, states in a Net should be variables and stored inside a scope. Each op should get inputs and outputs to do computation from a scope, such as data buffer, state (momentum) etc.</p>
 </li>
 <li><p class="first">Variable can only be created by Scope and a variable can only be got from Scope. User cannot create or get a variable outside a scope. This is a constraints of our framework, and will keep our framework simple and clear.</p>
 </li>
@@ -215,7 +215,7 @@
 <p>Variable can not belong to many scopes. If you want to use variables from parent scope, you can use <code class="docutils literal"><span class="pre">parent</span> <span class="pre">scope</span></code>.</p>
 </li>
 <li><p class="first">Scope should destruct all Variables inside it when itself is destructed. User can never store <code class="docutils literal"><span class="pre">Variable</span></code> pointer somewhere else.</p>
-<p>Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store <code class="docutils literal"><span class="pre">Variable</span></code> pointer to private data member or some global variable, the pointer will be a invalid pointer when associated <code class="docutils literal"><span class="pre">Scope</span></code> is destroyed.</p>
+<p>Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store <code class="docutils literal"><span class="pre">Variable</span></code> pointer to private data member or some global variable, the pointer will be an invalid pointer when associated <code class="docutils literal"><span class="pre">Scope</span></code> is destroyed.</p>
 </li>
 </ol>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Scope</span> <span class="p">{</span>
@@ -233,7 +233,7 @@
 <span id="parent-scope-and-local-scope"></span><h2>Parent scope and local scope<a class="headerlink" href="#parent-scope-and-local-scope" title="永久链接至标题">¶</a></h2>
 <p>Just like <a class="reference external" href="https://en.wikipedia.org/wiki/Scope_(computer_science)">scope</a> in programming languages, <code class="docutils literal"><span class="pre">Scope</span></code> in the neural network can also be a local scope. There are two attributes about local scope.</p>
 <ol class="simple">
-<li>We can create local variables in a local scope. When that local scope are destroyed, all local variables should also be destroyed.</li>
+<li>We can create local variables in a local scope. When that local scope is destroyed, all local variables should also be destroyed.</li>
 <li>Variables in a parent scope can be retrieved from local scopes of that parent scope, i.e., when user get a variable from a scope, it will try to search this variable in current scope. If there is no such variable in the local scope, <code class="docutils literal"><span class="pre">scope</span></code> will keep searching from its parent, until the variable is found or there is no parent.</li>
 </ol>
 <div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Scope</span> <span class="p">{</span>
@@ -302,7 +302,7 @@
 </div>
 <div class="section" id="orthogonal-interface">
 <span id="orthogonal-interface"></span><h2>Orthogonal interface<a class="headerlink" href="#orthogonal-interface" title="永久链接至标题">¶</a></h2>
-<p><code class="docutils literal"><span class="pre">FindVar</span></code> will return <code class="docutils literal"><span class="pre">nullptr</span></code> when <code class="docutils literal"><span class="pre">name</span></code> is not found. It can be used as <code class="docutils literal"><span class="pre">Contains</span></code> method. <code class="docutils literal"><span class="pre">NewVar</span></code> will return a <code class="docutils literal"><span class="pre">Error</span></code> when there is a name conflict locally. Combine <code class="docutils literal"><span class="pre">FindVar</span></code> and <code class="docutils literal"><span class="pre">NewVar</span></code>, we can implement <code class="docutils literal"><span class="pre">NewVar</span></code> easily.</p>
+<p><code class="docutils literal"><span class="pre">FindVar</span></code> will return <code class="docutils literal"><span class="pre">nullptr</span></code> when <code class="docutils literal"><span class="pre">name</span></code> is not found. It can be used as <code class="docutils literal"><span class="pre">Contains</span></code> method. <code class="docutils literal"><span class="pre">NewVar</span></code> will return an <code class="docutils literal"><span class="pre">Error</span></code> when there is a name conflict locally. Combine <code class="docutils literal"><span class="pre">FindVar</span></code> and <code class="docutils literal"><span class="pre">NewVar</span></code>, we can implement <code class="docutils literal"><span class="pre">NewVar</span></code> easily.</p>
 </div>
 </div>
 

develop/doc_cn/design/simple_op_design.html

@@ -190,10 +190,10 @@
 <p>The Interaction between Python and C++ can be simplified as two steps:</p>
 <ol class="simple">
 <li>C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.</li>
-<li>Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.</li>
+<li>Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ for finishing the Op construction task.</li>
 </ol>
-<div class="section" id="message-form-c-to-python">
-<span id="message-form-c-to-python"></span><h2>Message form C++ to Python<a class="headerlink" href="#message-form-c-to-python" title="永久链接至标题">¶</a></h2>
+<div class="section" id="message-from-c-to-python">
+<span id="message-from-c-to-python"></span><h2>Message from C++ to Python<a class="headerlink" href="#message-from-c-to-python" title="永久链接至标题">¶</a></h2>
 <p>We define a Protobuf message class <code class="docutils literal"><span class="pre">OpProto</span></code> to hold message needed in the first step. What should an <code class="docutils literal"><span class="pre">OpProto</span></code> contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”</p>
 <p>Following message are necessary:</p>
 <ol class="simple">

develop/doc_cn/design/var_desc.html

@@ -187,7 +187,7 @@
   <div class="section" id="background">
 <span id="background"></span><h1>Background<a class="headerlink" href="#background" title="永久链接至标题">¶</a></h1>
 <p>PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.</p>
-<p>PaddlePaddle use proto message to describe compile time graph for</p>
+<p>PaddlePaddle use proto message to describe compile time graph because</p>
 <ol class="simple">
 <li>Computation graph should be able to be saved to a file.</li>
 <li>In distributed training, the graph will be serialized and send to multiple workers.</li>