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Summary of neural network algorithm ml note 57

01

note

The first few sections have talked about some things about neural networks , Because neural networks are so important in machine learning , So we need to summarize what we have learned in a separate section .

Determine the structure of the neural network

The structure of the neural network , It's just the input layer 、 Output layer plus hidden layer , There are several layers in the hidden layer ? How many neurons are there in each layer ? Input layer 、 How many units are there in the output layer ?

How much are these , How much should it be ? These questions must be answered before training neural networks .

First , The number of units in the input layer is determined by the dimension of your independent variable ;

secondly , The number of units in the output layer is determined by how many classes the problem is divided into .

therefore , The selection of neural network structure , In essence, it is necessary to determine the number of hidden layers and the number of cells in each hidden layer .

With 3 Input units 、4 For example, a neural network with output units , The common settings of hidden layer are shown in the following figure .

According to the classification effect , The more cells in the hidden layer, the better , But too many neurons can make training quite slow , So we need to balance , Generally, the number of cells in the hidden layer is set to the number of cells in the input layer 2~4 Times is better . And the number of hidden layers is 1、2、3 Layers are common .

The general steps of neural network training

Step1: Random initialization weights ;

Step2: Implement forward propagation algorithm , Get the activation function for each input ;

Step3: Code to calculate the cost function ;

Step4: Realize back propagation to calculate the partial derivative of activation function .

Take a look at the pseudo code :

In code m It's the number of training samples .

Step5: Use gradient test to verify that the code for calculating the partial derivative by back propagation is correct , If it's correct, close the code in the gradient check section .

Step6: Combine some better algorithms to calculate the parameters that can minimize the cost function .

This article is from WeChat official account. - Teacher Gao who talks about programming (codegao) , author : Middle aged muddleheaded stone

The source and reprint of the original text are detailed in the text , If there is any infringement , Please contact the yunjia_community@tencent.com Delete .

Original publication time : 2020-11-10

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本文为[Teacher Gao who talks about programming]所创,转载请带上原文链接,感谢

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