• 【deep learning学习笔记】注释yusugomori的LR代码 --- LogisticRegression.cpp


    模型实现代码,关键是train函数和predict函数,都很容易。

    #include <iostream>
    #include <string>
    #include <math.h>
    #include "LogisticRegression.h"
    using namespace std;
    
    
    LogisticRegression::LogisticRegression(
    						int size, 	// N
    						int in, 	// n_in
    						int out		// n_out
    							) 
    {
      	N = size;
      	n_in = in;
      	n_out = out;
    
      	// initialize W, b
      	// W[n_out][n_in], b[n_out]
      	W = new double*[n_out];
      	for(int i=0; i<n_out; i++) 
    	  	W[i] = new double[n_in];
      	b = new double[n_out];
    
      	for(int i=0; i<n_out; i++) 
      	{
        	for(int j=0; j<n_in; j++) 
    		{
          		W[i][j] = 0;
        	}
        	b[i] = 0;
      	}
    }
    
    LogisticRegression::~LogisticRegression() 
    {
      	for(int i=0; i<n_out; i++) 
    	  	delete[] W[i];
      	delete[] W;
      	delete[] b;
    }
    
    
    void LogisticRegression::train (
    				int *x, 	// the input from input nodes in training set
    				int *y, 	// the output from output nodes in training set
    				double lr	// the learning rate
    				) 
    {
    	// the probability of P(y|x)
      	double *p_y_given_x = new double[n_out];
      	// the tmp variable which is not necessary being an array
      	double *dy = new double[n_out];
    
    	// step 1: calculate the output of softmax given input
      	for(int i=0; i<n_out; i++) 
      	{
      		// initialize
        	p_y_given_x[i] = 0;
        	for(int j=0; j<n_in; j++) 
    		{
    			// the weight of networks
          		p_y_given_x[i] += W[i][j] * x[j];
        	}
        	// the bias
        	p_y_given_x[i] += b[i];
      	}
      	// the softmax value
      	softmax(p_y_given_x);
    
    	// step 2: update the weight of networks
    	// w_new = w_old + learningRate * differential (导数)
    	//		 = w_old + learningRate * x (1{y_i=y} - p_yi_given_x) 
    	//		 = w_old + learningRate * x * (y - p_y_given_x)
      	for(int i=0; i<n_out; i++) 
      	{
        	dy[i] = y[i] - p_y_given_x[i];
        	for(int j=0; j<n_in; j++) 
    		{
          		W[i][j] += lr * dy[i] * x[j] / N;
        	}
        	b[i] += lr * dy[i] / N;
      	}
      	delete[] p_y_given_x;
      	delete[] dy;
    }
    
    void LogisticRegression::softmax (double *x) 
    {
      	double max = 0.0;
      	double sum = 0.0;
      
      	// step1: get the max in the X vector
      	for(int i=0; i<n_out; i++) 
    	  	if(max < x[i]) 
    		  	max = x[i];
      	// step 2: normalization and softmax
      	// normalize -- 'x[i]-max', it's not necessary in traditional LR.
      	// I wonder why it appears here? 
      	for(int i=0; i<n_out; i++) 
      	{
        	x[i] = exp(x[i] - max);
        	sum += x[i];
      	} 
      	for(int i=0; i<n_out; i++) 
    	  x[i] /= sum;
    }
    
    void LogisticRegression::predict(
    				int *x, 	// the input from input nodes in testing set
    				double *y	// the calculated softmax probability
    				) 
    {
    	// get the softmax output value given the current networks
      	for(int i=0; i<n_out; i++) 
      	{
        	y[i] = 0;
        	for(int j=0; j<n_in; j++) 
    		{
          		y[i] += W[i][j] * x[j];
        	}
        	y[i] += b[i];
      	}
    
      	softmax(y);
    }


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  • 原文地址:https://www.cnblogs.com/dyllove98/p/3194108.html
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