diff --git a/_typos.toml b/_typos.toml index 2b04b84d490..264515280ef 100644 --- a/_typos.toml +++ b/_typos.toml @@ -53,7 +53,6 @@ Similarily = "Similarily" Simle = "Simle" Sovler = "Sovler" Successed = "Successed" -Ture = "Ture" accordding = "accordding" accoustic = "accoustic" accpetance = "accpetance" @@ -190,9 +189,6 @@ sucess = "sucess" sucessor = "sucessor" sucessors = "sucessors" szie = "szie" -tempory = "tempory" -thier = "thier" -traget = "traget" traing = "traing" trainning = "trainning" transfered = "transfered" diff --git a/docs/api/paddle/combinations_cn.rst b/docs/api/paddle/combinations_cn.rst index 851490cbfa4..92f06d6942b 100644 --- a/docs/api/paddle/combinations_cn.rst +++ b/docs/api/paddle/combinations_cn.rst @@ -6,7 +6,7 @@ combinations .. py:function:: paddle.combinations(x, r=2, with_replacement=False, name=None) 对输入 Tensor 计算长度为 r 的情况下的所有组合，当 `with_replacement` 设为 False，可类比 python 内置 API `itertools.combinations` 。 -当 `with_replacement` 设为 True，可类比 python 内置 API `itertools.combinations_with_replacement(with_replacement=Ture)`。 +当 `with_replacement` 设为 True，可类比 python 内置 API `itertools.combinations_with_replacement(with_replacement=True)`。 参数 :::::::::: diff --git a/docs/design/memory/memory_optimization.md b/docs/design/memory/memory_optimization.md index e73f25baed2..8a3e3a4ae50 100644 --- a/docs/design/memory/memory_optimization.md +++ b/docs/design/memory/memory_optimization.md @@ -49,7 +49,7 @@ It's not enough to only have some basic strategies. The pre-requisite of memory In our design, the neural network topology is defined as a program. Luckily, [live variable analysis](https://en.wikipedia.org/wiki/Live_variable_analysis) is a classic problem in compilers which can be used in many stages, such as register allocation. -In compilers, the front end of the compiler translates programs into an intermediate language with an unbounded number of temporary variables. This program must run on a machine with a bounded number of registers. Two temporary variables a and b can fit into the same register, if a and b are never "in use" at the same time. Thus, many temporary variables can fit in few registers; if they don't all fit, the excess tempory variables can be kept in memory. +In compilers, the front end of the compiler translates programs into an intermediate language with an unbounded number of temporary variables. This program must run on a machine with a bounded number of registers. Two temporary variables a and b can fit into the same register, if a and b are never "in use" at the same time. Thus, many temporary variables can fit in few registers; if they don't all fit, the excess temporary variables can be kept in memory. Therefore, the compiler needs to analyze the intermediate-representation program to determine which temporary variables are in use at the same time. We say a variable is "live" if it holds a value that may be needed in the future, so this analysis is called liveness analysis. diff --git a/docs/design/modules/prune.md b/docs/design/modules/prune.md index 2925338d36c..88928565d36 100644 --- a/docs/design/modules/prune.md +++ b/docs/design/modules/prune.md @@ -45,7 +45,7 @@ If an operator needs to be run, it must fall into one of the following cases: 1. It is the target. 2. It is depended by some other ops, meaning its output is some other op's input. -The first case can be checked by `op_desc.is_traget()` . The second case can be implement as +The first case can be checked by `op_desc.is_target()` . The second case can be implement as ```c++ bool HasDependentVar(const OpDesc& op_desc, const std::set& dependent_vars) { diff --git a/docs/guides/advanced/layer_and_model_en.md b/docs/guides/advanced/layer_and_model_en.md index 84e61fd33f6..256b286c7ae 100644 --- a/docs/guides/advanced/layer_and_model_en.md +++ b/docs/guides/advanced/layer_and_model_en.md @@ -11,7 +11,7 @@ In this guide, you will learn how to define and make use of models in Paddle, an In Paddle, most models consist of a series of layers. Layer serves as the foundamental logical unit of a model, composed of two parts: the variable that participates in the computation and the operator(s) that actually perform the execution. -Constructing a model from scratch could be painful, with tons of nested codes to write and maintain. To make life easier, Paddle provides foundamental data structure ``paddle.nn.Layer`` to simplify the contruction of layer or model. One may easily inherit from ``paddle.nn.Layer`` to define thier custom layers or models. In addition, since both model and layer are essentially inherited from ``paddle.nn.Layer``, model is nothing but a special layer in Paddle. +Constructing a model from scratch could be painful, with tons of nested codes to write and maintain. To make life easier, Paddle provides foundamental data structure ``paddle.nn.Layer`` to simplify the contruction of layer or model. One may easily inherit from ``paddle.nn.Layer`` to define their custom layers or models. In addition, since both model and layer are essentially inherited from ``paddle.nn.Layer``, model is nothing but a special layer in Paddle. Now let us construct a model using ``paddle.nn.Layer``: