tensorflow转c系列二

前面已经写了系列一：https://blog.youkuaiyun.com/yongjiankuang/article/details/102470457，系列一主要是tensorflow对mnist进行模型训练，然后将训练好的参数导出来。本博文就是利用导出来的参数，搭建c代码的mnist前向网络。具体实现如下：

#ifndef __COMMON_H_
#define __COMMON_H_

#include <iostream>
#include <stdint.h>

using namespace std;

#define u8 unsigned char
#define s8 char
#define u16 unsigned short
#define s16 short
#define u32 unsigned int
#define s32 int
#define f32 float



extern f32 W_conv1_0[];
extern f32 b_conv1_0[];
extern f32 W_conv2_0[];
extern f32 b_conv2_0[];
extern f32 W_fc1_0[];
extern f32 b_fc1_0[];
extern f32 W_fc2_0[];
extern f32 b_fc2_0[];





#endif

layer.h和layer.cpp主要是声明网络层 

#ifndef __LAYER_H_
#define __LAYER_H_

#include "common.h"




int arm_convolve_HWC_f32_basic(f32 * Im_in,
	const u16 dim_im_in_X,
	const u16 dim_im_in_Y,
	const u16 ch_im_in,
	f32 * wt,
	const u16 ch_im_out,
	const u16 dim_kernel_X,
	const u16 dim_kernel_Y,
	const u16 padding_X,
	const u16 padding_Y,
	const u16 stride_X,
	const u16 stride_Y,
	f32 * bias,
	f32 * Im_out,
	const u16 dim_im_out_X,
	const u16 dim_im_out_Y
	);


int arm_relu_f32(f32 *data, u16 size);


int arm_maxpool_f32_HWC(f32 * Im_in,
	const u16 dim_im_in,
	const u16 ch_im_in,
	const u16 dim_kernel,
	const u16 padding,
	const u16 stride, const u16 dim_im_out, f32 * Im_out);