http://hihocoder.com/contest/tupu2018/problem/2
题目2 : Standard 2D Convolution
描述
Implement such below standard convolution
conv2d(input tensor, filters = 8, kernel size=[5,5], stride = 2, zero padding = 'SAME', activation = relu)
The shape of input tensor: [height = 32, width = 32, channels = 3]
输入
The first line will contain an image. The value of pixels is [0, 255]. The image should be preprocessed (/127.5 - 1) before fed into the convolution function. Weights[kernel height, kernel width, input channels, output channels] and biases[output channels] are followed in the next two lines respectively.
输出
Print the result tensor in one line printed in the same way as input file. The precision is (1E-4).
注意
All data are arranged into one line using C-like order, with the last axis index changing fastest, back to the first axis index changing slowest.
- 样例输入
-
Download the sample input from: https://media.hihocoder.com/contests/tupu-campus-hiring-2017/conv_sample_input.txt
- 样例输出
-
Download the sample output from: https://media.hihocoder.com/contests/tupu-campus-hiring-2017/conv_sample_output.txt
题意：实现conv2d()卷积函数。
#input channels = 3, output channels = 8
#input_tensor = 32 * 32 * 3
#weights = 5 * 5 * 3 * 8
#biases = 1*8
参数解释：stride是步长参数；zero padding表示是否用零填充边缘进行，same表示在stride = 1的时候输出矩阵大小不变； activation是激励函数；ReLU函数为f(x) = max(x, 0)。
#include <iostream>
#include <stdio.h>
#include <cmath>
#include <vector>
#include <string>
using namespace std;
#define Height 32
#define Width 32
#define Channels 3
#define Filters 8
#define kernel 5
#define Eps 1e-5
float weight[kernel][kernel][Channels][Filters];
float biases[Filters];
, , , string padding = "SAME", string activation = "relu")
{
int feaMapH = ceil(Height * 1.0 / stride);
) * stride + kernelSize;
;
int paddingR = (HeightAfterPadding - Height) - paddingL;
vector<vector< vector<float> > >a;
;i < HeightAfterPadding;i++){
vector< vector<float> >b;
; j < HeightAfterPadding;j++){
vector<float>c;
;k < Channels;k++){
c.push_back();
}
b.push_back(c);
}
a.push_back(b);
}
//cout << HeightAfterPadding << endl;
;i < Height;i++){
; j < Width;j++){
;k < Channels;k++){
a[paddingL + i][paddingL + j][k] = ((float)(inputTensor[i][j][k])) / 127.5 - 1.0;
}
}
}
;i <= HeightAfterPadding - kernelSize;i += stride){
; j <= HeightAfterPadding - kernelSize;j += stride){
;nn < filters;nn++){
;
;k < kernelSize;k++){
;l < kernelSize;l++){
;mm < Channels;mm++){
sum += a[i + k][j + l][mm] * weight[k][l][mm][nn];
}
}
}
sum += biases[nn];
if(sum < Eps) cout << "0.0000 " ;
else printf("%.4f ",sum);
}
//cout << endl;
}
}
return ;
}
int main()
{
int a[Height][Width][Channels];
//freopen("conv_sample_input.txt","r",stdin);
;i < Height;i++){
;j < Width;j++){
;k < Channels;k++){
cin >> a[i][j][k];
}
}
}
;i < kernel;i++){
;j < kernel;j++){
;k < Channels;k++){
;l < Filters;l++){
cin >> weight[i][j][k][l];
}
}
}
}
;l < Filters;l++){
cin >> biases[l];
}
conv2d(a,Filters,kernel);
;
}