C++元编程——深度双向RNN实验

使用C++的标准库实现了双向RNN的功能。最近对DRNN做了一些改进，同时进行了实验，首先DRNN的代码如下：

#ifndef _RNN_HPP_
#define _RNN_HPP_
#include 
#include 
#include 
#include "mat.hpp"
#include "bp.hpp"
#include "activate_function.hpp"
/*
* 									     X 
* 										  ^ 
* 										  | 
* 									     afV 
*										  ^
*							              |
*                                        +bv  
* 										  ^ 
* 										  | 
* 							 bw 		  V 
* 							 |  		  ^
*  							 v  		  | 
*	S ------> W ------> + --------> afW ------> S
*							 ^
*							 |
*							 U
*							 ^
*							 |
*							 X      	
* */
template class update_method_templ, template class activate_func>
struct rnn_unite 
{
	using target_t = typename std::template vector;
	val_t W;				//* 用于对层内加权
	val_t U;				//* 用于对上层加权
	val_t V;				//* 用于对激活值加权
	val_t bv;				//* 激活值的偏移量
	val_t bw;				//* 层内加权偏移量
	val_t S;

	update_method_templ umW, umU, umV, umBv, umBw, umS;

	activate_func afW;
	activate_func afV;	//* 对下层的计划函数

	target_t	pre_input;
	target_t	pre_S;			//* 上一层进来的S

	void print() const
	{
		printf("W:%.4lf U:%.4lf V:%.4lf bv:%.4lf bw:%.4lf S:%.4lf\r\n", W, U, V, bv, bw, S);
	}

	rnn_unite():S(0.) 
	{
		static std::default_random_engine e;
		std::uniform_real_distribution ud(0, 1);
		W = ud(e);						//* 随机初始化
		U = ud(e);
		V = ud(e);
		bv = ud(e);
		bw = ud(e);
	}
	inline val_t do_forward(const int& i, val_t& cur_S)
	{
		cur_S = afW.forward(U * pre_input[i] + W * cur_S + bw);
		pre_S[i] = cur_S;
		return afV.forward(V * cur_S + bv);
	}
	inline val_t do_backward(const int& i, const target_t& mt_delta, val_t& S_delta, const val_t& Wbak, const val_t& Ubak, const val_t& Vbak
	, val_t& Vdelta, val_t& bVdelta, val_t& Wdelta, val_t& bWdelta, val_t& Udelta)
	{
		auto delta_before_afV = mt_delta[i] * afV.backward();
		Vdelta = Vdelta + pre_S[i] * delta_before_afV;
		bVdelta = bVdelta + delta_before_afV;
		//V = umV.update(V, pre_S[i] * delta_before_afV);
		//bv = umBv.update(bv, delta_before_afV);
		auto layer_back = (delta_before_afV * Vbak + S_delta) * afW.backward();
		Wdelta = Wdelta + pre_S[i - 1] * layer_back;
		bWdelta = bWdelta + layer_back;
		Udelta = Udelta + pre_input[i] * layer_back;
		//W = umW.update(W, pre_S[i - 1] * layer_back);
		//bw = umBw.update(bw, layer_back);
		//U = umU.update(U, pre_input[i]);
		S_delta = layer_back * Wbak;
		return layer_back * Ubak;
	}
	//* 升序遍历数组，正向通过网络，返回值为层间正向输出
	target_t asc_forward(const target_t& mt_in)
	{
		pre_S.resize(mt_in.size(), 0.);
		pre_input = mt_in;
		target_t vec_ret(mt_in.size(), 0.);
		val_t cur_S = S;						//* 层内计算的值
		for (int i = 0; i < pre_input.size(); ++i)
		{
			vec_ret[i] = do_forward(i, cur_S);
		}
		return vec_ret;
	}
	//* 反向遍历数组反馈误差，返回值为层间反向传播的误差
	target_t desc_backward(const target_t& mt_delta)
	{
		val_t Wbak = W, Ubak = U, Vbak = V;
		target_t ret(mt_delta.size(), 0.);
		val_t S_delta = 0., Vdelta = 0., bVdelta = 0., Wdelta = 0., bWdelta = 0., Udelta = 0.;
		for (int i = mt_delta.size()-1; i >= 0; --i)
		{
			ret[i] = do_backward(i, mt_delta, S_delta, Wbak, Ubak, Vbak, Vdelta, bVdelta, Wdelta, bWdelta, Udelta);
		}
		V = umV.update(V, Vdelta);
		bv = umBv.update(bv, bVdelta);
		W = umW.update(W, Wdelta);
		bw = umBw.update(bw, bWdelta);
		U = umU.update(U, Udelta);
		S = umS.update(S, S_delta);
		return ret;
	}
	//* 反向遍历数组，正向通过网络
	target_t desc_forward(const target_t& mt_in)
	{
		pre_S.resize(mt_in.size(), 0.);
		pre_input = mt_in;
		target_t vec_ret(mt_in.size(), 0.);
		val_t cur_S = S;						//* 层内计算的值
		for (int i = mt_in.size()-1; i >= 0; --i)
		{
			vec_ret[i] = do_forward(i, cur_S);
		}
		return vec_ret;
	}
	//* 正向遍历数组，反向通过网络
	target_t asc_backward(const target_t& mt_delta)
	{
		val_t Wbak = W, Ubak = U, Vbak = V, bvbak = bv, bwbak = bw, Sbak = S;
		target_t ret(mt_delta.size(), 0.);
		val_t S_delta = 0., Vdelta = 0., bVdelta = 0., Wdelta = 0., bWdelta = 0., Udelta = 0.;
		for (int i = 0; i < mt_delta.size(); ++i)
		{
			ret[i] = do_backward(i, mt_delta, S_delta, Wbak, Ubak, Vbak, Vdelta, bVdelta, Wdelta, bWdelta, Udelta);
		}
		V = umV.update(V, Vdelta);
		bv = umBv.update(bv, bVdelta);
		W = umW.update(W, Wdelta);
		bw = umBw.update(bw, bWdelta);
		U = umU.update(U, Udelta);
		S = umS.update(S, S_delta);
		return ret;
	}

};

template class update_method_templ, template class activate_func>
struct rnn_dup
{
	using target_t = typename std::template vector;
	rnn_unite rnn_forward, rnn_backward;

	using weight_type = bp;

	weight_type w;

	rnn_dup():rnn_forward(), rnn_backward()
	{}

	target_t forward(const target_t& mt_in)
	{
		target_t mt_forward = rnn_forward.asc_forward(mt_in);
		target_t mt_backward = rnn_backward.desc_forward(mt_in);
		target_t ret(mt_in.size(), 0.);
		for (int i = 0; i < mt_in.size(); ++i)
		{
			mat<2, 1, val_t> mt;
			mt[0] = mt_forward[i];
			mt[1] = mt_backward[i];
			ret[i] = w.forward(mt)[0];
		}
		return ret;
	}

	target_t backward(const target_t& mt_delta) 
	{
		target_t mt_forward(mt_delta.size(), 0.);
		target_t mt_backward(mt_delta.size(), 0.);
		for (int i = 0; i < mt_delta.size(); ++i)
		{
			mat<1, 1, val_t> mt(mt_delta[i]);
			auto mt_out = w.backward(mt);
			mt_forward[i] = mt_out[0];
			mt_backward[i] = mt_out[1];
		}
		target_t mt_forward_out = rnn_forward.desc_backward(mt_forward);
		target_t mt_backward_out = rnn_backward.asc_backward(mt_backward);
		target_t mt_ret(mt_delta.size(), 0.);
		for (int i = 0; i < mt_delta.size(); ++i)
		{
			mt_ret[i] = mt_forward_out[i] + mt_backward_out[i];
		}
		return mt_ret;
	}

	void print() const 
	{
		printf("bp weight:\r\n");
		w.print();
		printf("forward:\r\n");
		rnn_forward.print();
		printf("backward:\r\n");
		rnn_backward.print();
	}
};

template class update_method_templ, template class activate_func>
struct rnn_flow 
{
	using target_t = typename std::template vector;
	using rnn_type = rnn_dup;
	rnn_type* p_layers;
	int layer_num;
	rnn_flow(const int& i_layer_num) :layer_num(i_layer_num) 
	{
		p_layers = new rnn_type[layer_num];
	}

	virtual ~rnn_flow() 
	{
		delete[] p_layers;
	}

	target_t forward(const target_t& mt_in) 
	{
		auto mt = mt_in;
		for (int i = 0; i < layer_num; ++i) 
		{
			mt = p_layers[i].forward(mt);
		}
		return mt;
	}

	target_t backward(const target_t& mt_in) 
	{
		auto ret = mt_in;
		for (int i = layer_num-1; i >= 0; --i) 
		{
			ret = p_layers[i].backward(ret);
		}
		return ret;
	}

	void print() const
	{
		for (int i = 0; i < layer_num; ++i)
		{
			p_layers[i].print();
		}
	}
};

#endif

接着使用nadam进行DRNN权值更新，然后输入了两个数组1,2,3和2,3,4进行训练，最后使用各种数组进行训练结果测试，代码如下：

#include "rnn.hpp"
#include "activate_function.hpp"
#include 

const double ln2 = log(2);

template
mat cross_entropy_grad(const mat& expect, const mat& real)
{
    mat one(1.);
    return (expect / real - (one - expect)/(one - real))/ln2;
}

int main(int argc, char** argv)
{
    using tgt_mat = std::vector;
    rnn_flow rnn(2);
    //* 生成随机矩阵，根据矩阵计算出
    int train_num = 600000;
    for (int i = 0; i < train_num; ++i)
    {
        {
            tgt_mat in = {.1, .2, .3};
            auto ret = rnn.forward(in);
            tgt_mat expect = {.3, .3, .3};
            mat<1, 3, double> mt_ret(ret.begin(), ret.end());
            mat<1, 3, double> mt_exp(expect.begin(), expect.end());
            mat<1, 3, double> mt_delta = cross_entropy_grad(mt_ret, mt_exp);

            std::vector vec_delta = mt_delta.to_vector();
            mat<1, 3, double> backdelta = rnn.backward(vec_delta);
            if (i %(train_num/10) == 0)
            {
                printf("output:");
                mt_ret.print();
            }
        }
        {
            tgt_mat in = {.2, .3, .4};
            auto ret = rnn.forward(in);
            tgt_mat expect = {.45, .45, .45};
            mat<1, 3, double> mt_ret(ret.begin(), ret.end());
            mat<1, 3, double> mt_exp(expect.begin(), expect.end());
            mat<1, 3, double> mt_delta = cross_entropy_grad(mt_ret, mt_exp);

            std::vector vec_delta = mt_delta.to_vector();
            mat<1, 3, double> backdelta = rnn.backward(vec_delta);
            if (i %(train_num/10) == 0)
            {
                printf("output:");
                mt_ret.print();
            }
        }
        
        if (i %(train_num/10) == 0)
        {
            printf("--------------------------\r\n");
        }
    }
    printf("out\r\n");
    mat<1, 3, double>(rnn.forward({.1, .2, .0})).print();
    mat<1, 3, double>(rnn.forward({.2, .3, .0})).print();
    mat<1, 3, double>(rnn.forward({.3, .4, .0})).print();
    return 0;
}

训练结果如下：

 

第一个按顺序输入.1,.2,.0结果显示输入第2个的结果就是0.3228和我们训练使用的0.3有一点差距，不过差距比较小。第二个按照顺序输入.2,.3,.0，我们训练用的是.2,.3,.4；结果显示是0.456，和我们训练的输出0.45几乎一样。第三个是一个程序没有见过的输入.3,.4,.0;输出没有什么参考意义。总之，可见这个DRNN具备一定的学习能力的。