diff --git a/docs/source/LogisticRegression.md b/docs/source/LogisticRegression.md
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--- a/docs/source/LogisticRegression.md
+++ b/docs/source/LogisticRegression.md
@@ -8,9 +8,29 @@ Logistic regression is a statistical analysis method used to predict a data valu
 
 The dependent variable of logistics regression can be two-category or multi-category, but the two-category is more common and easier to explain. So the most common use in practice is the logistics of the two classifications.
 
-逻辑回归的因变量可以是二分类的，也可以是多分类的，但是二分类的更为常用，也更加容易解释。所以实际中最常用的就是二分类的物流回归。
+逻辑回归的因变量可以是二分类的，也可以是多分类的，但是二分类的更为常用，也更加容易解释。
 
-The general steps for regression problems are as follows:
+
+Logistics regression corresponds to a hidden status p through the function trumpetp = S(ax+b), then determine the value of the dependent
+variable according to the size of p and 1-p.The function S here is the Sigmoid function:
+                                         S(t)=1/(1+e^(-t)
+By changing t to ax+b, you can get the parameter form of the logistic regression model:
+                                          P(x;a,b) = 1 / (1 + e^(-ax+b))
+
+logistic回归通过函数S将ax+b对应到一个隐状态p，p = S(ax+b)，然后根据p与1-p的大小决定因变量的值。这里的函数S就是Sigmoid函数:
+                                          S(t)=1/(1+e^(-t)
+                                      
+将t换成ax+b，可以得到逻辑回归模型的参数形式：
+                                          P(x;a,b) = 1 / (1 + e^(-ax+b))
+
+![image](https://github.com/SciEvan/TensorFlow.NET/tree/master/docs/source/sigmoid.png)
+
+sigmoid函数的图像
+
+By the function of the function S, we can limit the output value to the interval [0, 1],
+p(x) can then be used to represent the probability p(y=1|x), the probability that y is divided into 1 group when an x occurs.
+
+通过函数S的作用，我们可以将输出的值限制在区间[0， 1]上，p(x)则可以用来表示概率p(y=1|x)，即当一个x发生时，y被分到1那一组的概率
 
 
 The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/LogisticRegression.cs).
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