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提交 53f55a9b 编写于 作者: sahduashufa's avatar sahduashufa
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grad_edge/matrix_grad.h
浏览文件 @ 53f55a9b
......@@ -25,7 +25,7 @@ struct edge_network
// cout_mat(data);
// cout<<"weights: "<<endl;
// cout_mat(weights);
Matrix output = mul(get_T(weights),data);
Matrix output = mul(weights,data);
output = add(output,bais,0);
Matrix output1 = e_sigmoid(output);
// cout<<"----------forward data--------"<<endl;
......@@ -40,7 +40,7 @@ struct edge_network
}
Matrix forward_without_act(Matrix data,Matrix weights,Matrix bais)
{
Matrix output = mul(get_T(weights),data);
Matrix output = mul(weights,data);
output = add(output,bais,0);
// Matrix output1 = e_sigmoid(output);
// cout<<"----------forward data--------"<<endl;
......@@ -64,24 +64,24 @@ struct edge_network
// cout<<"-----------TTTTTweights--------------"<<endl;
// cout_mat(padding(mul_simple(mul(get_T(weights),grad_next),output_before),2,2));
// cout<<"[[[[[";
cout<<"grad_next:"<<endl;
cout_mat(grad_next);
cout<<"weights"<<endl;
cout_mat(weights);
// cout<<"grad_next:"<<endl;
// cout_mat(grad_next);
// cout<<"weights"<<endl;
// cout_mat(weights);
for(int index = 0;index<output_before.row;index++)
{
Node z = output_before.matrix[index][0];
// Node sigmoid = 1/(1+(1/exp(z)));
Node anyone = p_(z);
change_va(output_before,index,0,anyone.gradient(z));
cout<<"grad_edge: "<<anyone.gradient(z)<<endl;
//cout<<"grad_edge: "<<anyone.gradient(z)<<endl;
}
cout<<"output_before"<<endl;
cout_mat(output_before);
cout<<"mul(weights,grad_next)"<<endl;
cout_mat(mul(weights,grad_next));
cout<<"grad_backward: "<<endl;
cout_mat(mul_simple(mul(weights,grad_next),output_before));
//cout<<"output_before"<<endl;
//cout_mat(output_before);
//cout<<"mul(weights,grad_next)"<<endl;
//cout_mat(mul(weights,grad_next));
//cout<<"grad_backward: "<<endl;
//cout_mat(mul_simple(mul(weights,grad_next),output_before));
return mul_simple(mul(weights,grad_next),output_before);
}
Matrix end_layer_backward(Matrix label,Matrix acti_val,Node loss_fun(Node,Node),Node act_fun(Node))
......@@ -99,14 +99,14 @@ struct edge_network
Node act = act_fun(z31);
act_output.matrix[index_x][0] = act.gradient(z31);
loss_act.matrix[index_x][0] = loss.gradient(a13);
cout<<"z31: "<<z31<<endl;
cout<<"z31_grad: "<<act.gradient(z31)<<endl;
cout<<"a13: "<<a13<<"t1: "<<t1<<endl;
cout<<"loss: "<<loss<<endl;
cout<<"a13_gradient: "<<loss.gradient(a13)<<endl;
// cout<<"z31: "<<z31<<endl;
// cout<<"z31_grad: "<<act.gradient(z31)<<endl;
// cout<<"a13: "<<a13<<"t1: "<<t1<<endl;
// cout<<"loss: "<<loss<<endl;
// cout<<"a13_gradient: "<<loss.gradient(a13)<<endl;
}
Matrix mid_grad_end = mul_simple(loss_act,act_output);
cout<<"mid_grad_end";
// cout<<"mid_grad_end";
cout_mat(mid_grad_end);
return mid_grad_end;
}
......
ma
浏览文件 @ 53f55a9b
无法预览此类型文件
main.cpp
浏览文件 @ 53f55a9b
......@@ -60,16 +60,11 @@ int main()
int output_dim = 2;
edge_network sequaltial(input_dim,output_dim);
Matrix output1 = sequaltial.forward(data_mine,weight1,bais1);
//output1 acti_val;
Matrix output1_without_act = sequaltial.forward_without_act(data_mine,weight1,bais1);
//output1_without_act without activation;
Matrix output2 = sequaltial.forward(output1,weight2,bais2);
Matrix output2_without_act = sequaltial.forward_without_act(output1,weight2,bais2);
Matrix output_end = sequaltial.end_layer_backward(label,output2_without_act,*loss,*act);
//last output layer should feed the value without activation;
//output_end : the gradient of loss layer;
Matrix backward3 = sequaltial.backward(output_end,output1_without_act,weight2,*act);
//backward3: the gradient of the behind layer;
Matrix weight_2_grad = mul(output_end,get_T(output1));
Matrix weight_1_grad = mul(backward3,get_T(data_mine));
weight1 = subtract(weight1,times_mat(0.001,weight_1_grad));
......
nerual_test.cpp
浏览文件 @ 53f55a9b
......@@ -31,28 +31,61 @@ int main()
int input_dim = 2;
int output_dim = 2;
edge_network sequaltial(input_dim,output_dim);
Matrix data_mine = CreateMatrix(100,2);
Matrix data_mine = CreateMatrix(100,3);
Matrix label_mine = CreateRandMat(100,1);
Matrix data = iloc(data_mine,0,1,0,0);
cout<<"data: ";
cout_mat(data);
Matrix label = iloc(label_mine,0,1,0,0);
Matrix label = iloc(label_mine,0,3,0,0);
cout<<"label: ";
cout_mat(label);
Matrix bias1 = CreateRandMat(2,1);
Matrix weight1 = CreateRandMat(2,data.col);
cout<<"weight1: ";
cout_mat(weight1);
int index = 0;
int index = 1;
Matrix backward2 = CreateMatrix(2,1);
Matrix before_backward2 = CreateMatrix(2,1);
change_va(before_backward2,1,0,1);
for(index = 0;index<99;index++){
Matrix output1 = sequaltial.forward(get_T(get_row(data_mine,index)),weight1,bias1);
Matrix weight2 = CreateRandMat(output1.row,2);
Matrix output1_without_act = sequaltial.forward_without_act(get_T(get_row(data_mine,index)),weight1,bias1);
cout<<"output1_without_act: ";
cout_mat(output1_without_act);
Matrix weight2 = CreateRandMat(2,output1.row);
cout<<"weight2: "<<endl;
cout_mat(weight2);
Matrix bias2 = CreateRandMat(weight2.row,1);
Matrix output2 = sequaltial.forward(output1,weight2,bias2);
Matrix output2_without_act = sequaltial.forward_without_act(output1,weight2,bias2);
cout<<"output2_without_act: ";
cout_mat(output2_without_act);
cout<<"output2: ";
cout_mat(output2);
cout<<"output1:"<<endl;
cout_mat(output1);
Matrix weight3 = CreateRandMat(3,output2.row);
Matrix bias3 = CreateRandMat(weight3.row,1);
Matrix output3 = sequaltial.forward(output2,weight3,bias3);
Matrix output3_without_act = sequaltial.forward_without_act(output2,weight3,bias3);
cout<<"row: "<<label.row<<"col: "<<label.col;
Matrix output_end = sequaltial.end_layer_backward(label,output3_without_act,*loss,*act);
cout<<"outputend: ";
cout_mat(output_end);
cout<<"output3_without_act: ";
cout_mat(output3_without_act);
cout<<"output3: ";
cout_mat(output3);
Matrix backward3 = sequaltial.backward(output_end,output2_without_act,get_T(weight3),*act);
cout<<"backward_before: ";
cout_mat(before_backward2);
before_backward2 = add(before_backward2,backward2);
backward2 = sequaltial.backward(backward3,output1_without_act,get_T(weight2),*act);
cout<<"backward2: ";
save_txt(backward2,"a.csv");
cout_mat(before_backward2);
cout_mat(backward2);
//can change the before_backward2 values;
}
}
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