怎么使用C#完成常用神经网络

这篇文章主要介绍"怎么使用C#完成常用神经网络"，在日常操作中，相信很多人在怎么使用C#完成常用神经网络问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答"怎么使用C#完成常用神经网络"的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

WeaveNetC#编写的用于神经网络的计算图框架

这是一个由c#编写的神经网络，可以看到内部任何细节的实现，可以对学习神经网络，以及理解其中的计算方式。此架构不包含自动 Backward 向后传播，为了展示更多的计算细节。

源码包含 cnn,bp,fcn,lstm,convlstm,GRU 等示例内容，包含示例所用的数据内容。

1. LOSS支持：MESLOSS,cross-entropy

2. 激活函数支持：ReLu，Tanh，Sigmod，Softmax

3. 数据类型支持： float[][] 与 float[][][,]，二维与四维

4. 池化支持：平均池化Averpooling，最大池化Maxpooling

5. 其他支持：ConvLayer，Conv2DLayer，MulLayer,ConvTranspose2DLayer

6. 每个支持类都包含了向前传播Forward，与Backward向后传播的方法

以下几个小例子

CNN的训练实现

 public class CNN    {        Conv2DLayer cl;                             Conv2DLayer cl2;        Conv2DLayer cl3;        //TanhLayer sl = new TanhLayer();        //TanhLayer sl2 = new TanhLayer();        //TanhLayer sl3 = new TanhLayer();        Maxpooling ap1;        Maxpooling ap2;        SigmodLayer sl = new SigmodLayer();        SigmodLayer sl2 = new SigmodLayer();        //SigmodLayer sl3 = new SigmodLayer();        Softmax sl3 = new Softmax();        //Averpooling ap2;        //Averpooling ap1;        public CNN()        {              cl = new Conv2DLayer(1, 0, 5, 1, 6);              //ap1 = new Averpooling(2);            ap1 = new Maxpooling(2);            cl2 = new Conv2DLayer(1, 0, 5, 6, 12);            // ap2 = new Averpooling(2);            ap2 = new Maxpooling(2);              cl3 = new Conv2DLayer(in_channels: 12, out_channels: 10, _inSize: 4,_full:true );        }        public dynamic Forward(float[][][,] matrices)        {            dynamic data = cl.Forward(matrices);            data = sl.Forward(data);            data = ap1.Forward(data);            data = cl2.Forward(data);            data = sl2.Forward(data);            data = ap2.Forward(data);            data = cl3.Forward(data);            data = sl3.Forward(data);            return data;        }        dynamic cl3grid;        dynamic cl2grid;        dynamic clgrid;        public void backward(dynamic grid)        {            dynamic grid2 = sl3.Backward(grid);            cl3grid = cl3.backweight(grid2);//获取cl3的权重            //--------------------------------                          grid2 = cl3.Backward(grid2);            grid2 =ap2.Backward(grid2);            grid2 = sl2.Backward(grid2);            cl2grid = cl2.backweight(grid2);//获取cl2的权重            //-------------------------------------            grid2 = cl2.Backward(grid2);            grid2 = ap1.Backward(grid2);            grid2 = sl.Backward(grid2);            clgrid = cl.backweight(grid2);//获取cl的权重        }        float lr = 1.0f;        public void update()        {                    //    int channl = cl3grid.grid.Length;            cl3.wdata = Matrix.MatrixSub(cl3.wdata, Matrix.multiply(cl3grid.grid, lr));             cl3.basicData = Matrix.MatrixSub(cl3.basicData, Matrix.multiply(cl3grid.basic, lr));            cl2.weights = Matrix.MatrixSub(cl2.weights, Matrix.multiply(cl2grid.grid, lr));            cl2.basicData = Matrix.MatrixSub(cl2.basicData, Matrix.multiply(cl2grid.basic, lr));            cl.weights = Matrix.MatrixSub(cl.weights, Matrix.multiply(clgrid.grid, lr));            cl.basicData = Matrix.MatrixSub(cl.basicData, Matrix.multiply(clgrid.basic, lr));        }    }

LSTM 实现例子

 public class LSTMCELL    {        ConvLayer convLayerih;        ConvLayer convLayerhh;        int input_size; int hidden_size;        public LSTMCELL(int _input_size, int _hidden_size)        {            input_size = _input_size;            hidden_size = _hidden_size;            convLayerih = new ConvLayer(input_size, hidden_size * 4 );            //convLayerih.weights = JsonConvert.DeserializeObject(util.getstr("D:\\lstmihw.json"));            //convLayerih.basicData = JsonConvert.DeserializeObject(util.getstr("D:\\lstmihb.json"));            convLayerhh = new ConvLayer( hidden_size, hidden_size * 4);            //convLayerhh.weights = JsonConvert.DeserializeObject(util.getstr("D:\\lstmhhw.json"));            //convLayerhh.basicData = JsonConvert.DeserializeObject(util.getstr("D:\\lstmhhb.json"));        }        SigmodLayer input_gate_s = new SigmodLayer();        SigmodLayer forget_gate_s = new SigmodLayer();        SigmodLayer output_gate_s = new SigmodLayer();        TanhLayer cell_memory_tl = new TanhLayer();        TanhLayer cell_tl = new TanhLayer();                MulLayer c_next_mul = new MulLayer();        MulLayer mulin_gate_mul = new MulLayer();        MulLayer h_next_mul = new MulLayer();                public dynamic Forward(float[][] input, float[][] h_prev, float[][] c_prev)        {            //a_vector = np.dot(x, self.weight_ih.T) + np.dot(h_prev, self.weight_hh.T)            //a_vector += self.bias_ih + self.bias_hh            Xinput = input;            xh_prev = h_prev;            xc_prev = c_prev;            var ih = convLayerih.Forward(input);            var hh = convLayerhh.Forward(h_prev);            var a_vector = Matrix.MatrixAdd(ih, hh);                       List liast = Matrix.chunk(a_vector,4,1);            var a_i = liast[0];            var a_f = liast[1];            var a_c = liast[2];            var a_o = liast[3];                       input_gate = input_gate_s.Forward(a_i);             forget_gate = forget_gate_s.Forward(a_f);             cell_memory = cell_memory_tl.Forward(a_c);             output_gate = output_gate_s.Forward(a_o);            var c_next_temp = c_next_mul.Forward(forget_gate, c_prev);            var mulin_gate = mulin_gate_mul.Forward(input_gate, cell_memory);            var c_next = Matrix.MatrixAdd(c_next_temp, mulin_gate);            var h_next = h_next_mul.Forward(output_gate, cell_tl.Forward(c_next));                       // dh_prev = Matrix.zroe(h_next.Length, h_next[0].Length);            return (h_next,c_next);//上次的状态，上次的记忆        }        dynamic  Xinput, xh_prev, xc_prev, input_gate, forget_gate, cell_memory, output_gate;       // dynamic dh_prev;        dynamic ihweight, hhweight;        public dynamic backward(dynamic grid)        {                         var dh  = h_next_mul.BackwardY(grid);            var d_tanh_c = cell_tl.Backward(dh);             //var dc_prev=c_next_mul.backwardY(d_tanh_c);                        var d_input_gate = mulin_gate_mul.Backward(d_tanh_c);            var d_forget_gate=c_next_mul.Backward(d_tanh_c);            var d_cell_memory = mulin_gate_mul.BackwardY(d_tanh_c);            var d_output_gate = h_next_mul.Backward(grid);// d_tanh_c            var d_ai = input_gate_s.Backward(d_input_gate);            var d_af = forget_gate_s.Backward(d_forget_gate);            var d_ao = output_gate_s.Backward(d_output_gate);            var d_ac = cell_memory_tl.Backward(d_cell_memory);            var temp=Matrix.cat(d_ai, d_af, 1);            var temp2 = Matrix.cat( d_ac, d_ao, 1);            var da= Matrix.cat(temp, temp2, 1);           // var daT=Matrix.T(da);             ihweight = convLayerih.backweight(da);             hhweight = convLayerhh.backweight(da);            return convLayerih.backward(da);        }        float lr = 0.1f;        public void update()        {            convLayerih.weights = Matrix.MatrixSub(convLayerih.weights, Matrix.multiply(ihweight.grid, lr));            convLayerih.basicData = Matrix.MatrixSub(convLayerih.basicData, Matrix.multiply(ihweight.basic, lr));            convLayerhh.weights = Matrix.MatrixSub(convLayerhh.weights, Matrix.multiply(hhweight.grid, lr));            convLayerhh.basicData = Matrix.MatrixSub(convLayerhh.basicData, Matrix.multiply(hhweight.basic, lr));        }    }

FCN实现例子

 public class FCN    {        Conv2DLayer cl;         Conv2DLayer cl2;        Conv2DLayer cl3;        ConvTranspose2DLayer Tcl1;        Maxpooling mpl = new Maxpooling();        Maxpooling mpl2 = new Maxpooling();        SigmodLayer sl = new SigmodLayer();        SigmodLayer sl2 = new SigmodLayer();        SigmodLayer sl3 = new SigmodLayer();        Softmax sl4 = new Softmax();        public FCN(int weightssize)        {            cl = new Conv2DLayer(1, weightssize / 2, weightssize, 1, 6, bias: false);            cl2 = new Conv2DLayer(1, weightssize / 2, weightssize, 6, 12, bias: false);            cl3 = new Conv2DLayer(1, weightssize / 2, weightssize, 12, 24, bias: false);            Tcl1 = new ConvTranspose2DLayer(2, 1, weightssize + 1, 24, 1, bias: false);        }        public dynamic Forward(dynamic data)        {            dynamic data2= cl.Forward(data);            data2=sl.Forward(data2);            data2=mpl.Forward(data2);            data2 = cl2.Forward(data2);            data2 = sl2.Forward(data2);            data2 = mpl2.Forward(data2);            data2 = cl3.Forward(data2);            data2 = sl3.Forward(data2);            data2=Tcl1.Forward(data2);            data2 = sl4.Forward(data2);            return data2;        }        public dynamic backward(dynamic grid)        {            var grid2 = sl4.Backward(grid);            grid2= Tcl1.Backward(grid2);            grid2 = sl3.Backward(grid2);            grid2 = cl3.Backward(grid2);            grid2 = mpl2.Backward(grid2);            grid2 = sl2.Backward(grid2);            grid2 = cl2.Backward(grid2);            grid2 = mpl.Backward(grid2);            grid2 = sl.Backward(grid2);            grid2 = cl.Backward(grid2);            return grid2;        }    }

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