python pytorch 纯算法实现前馈神经网络训练（数据集随机生成）-续

上一次的代码博主看了，有两个小问题其实，一个是，SGD优化的时候，那个梯度应该初始化为0，还一个是我并没有用到随机生成batch。
博主修改了代码，并且加入了accuracy图像绘制的代码。代码如下：


#coding=gbkimport torch
from torch.autograd import Variable
from torch.utils import data
import matplotlib.pyplot as pltdim=5
batch=32
neuron_num=10
def generate_data():torch.manual_seed(3)X1=torch.randint(0,4,(1000,dim))X2=torch.randint(6,10,(1000,dim))Y1=torch.randint(0,1,(1000,))Y2=torch.randint(1,2,(1000,))print(X1)print(X2)print(Y1)print(Y2)X_data=torch.cat([X1,X2],0)Y_label=torch.cat([Y1,Y2],0)print(X_data)print(Y_label)return X_data,Y_labeldef sampling(X_data,Y_label,batch):data_size=Y_label.size()#print(data_size)index_sequense=torch.randperm(data_size[0])return index_sequensedef loss_function_crossEntropy(Y_predict,Y_real):if Y_real==1:return -torch.log(Y_predict)else:return -torch.log(1-Y_predict)X_data,Y_label=generate_data()
index_sequense=sampling(X_data,Y_label,batch)def test():l=loss_function_crossEntropy(torch.tensor([0.1]),torch.tensor([1]))print(l)def neuron_net(X,W,b):result=torch.matmul(X.type(dtype=torch.float32),W)+bresult=torch.relu(result).reshape(1,result.size(0))#print(result)#print(result.size())return resultdef grad(X,W,b,y_predict,y_real,W2,b2):g1=y_real/y_predict+(y_real-1)/(1-y_predict)result=torch.matmul(X.type(dtype=torch.float32),W)+bresult=torch.relu(result).reshape(1,result.size(0))g2=y_predict*(1-y_predict)g3=neuron_net(X,W,b)g4=W2C=torch.matmul(X.type(dtype=torch.float32),W)+ba=[]for i in C:if i<=0:a.append(0)else:a.append(1)g5=torch.tensor(a)g6=Xgrad_w=g1*g2*g3grad_b=g1*g2#print("grad_w",grad_w)#print(grad_b)grad_w2=g1*g2*g4grad_w2=grad_w2.reshape(1,10)grad_w2=grad_w2*g5#  print(grad_w2.size())grad_w2=grad_w2.reshape(10,1)g6=g6.reshape(1,5)grad_b2=grad_w2grad_w2=torch.matmul(grad_w2.type(dtype=torch.float32),g6.type(dtype=torch.float32))# print(grad_b2.size())return grad_w,grad_b,grad_w2,grad_b2#print(g1,g2,g3,g4,g5,g6)#print(grad_w2)#print(grad_b2)def flat_dense(X,W,b):return torch.sigmoid(torch.matmul(X.type(dtype=torch.float32),W)+b)W=torch.randn(dim,neuron_num)
b=torch.randn(neuron_num)
W2=torch.randn(neuron_num,1)
b2=torch.randn(1)def net(X,W,b,W2,b2):result=neuron_net(X,W,b)ans=flat_dense(result,W2,b2)return  ansy_predict=net(X_data[0],W,b,W2,b2)
print(y_predict)grad_w,grad_b,grad_w2,grad_b2=grad(X_data[0],W,b,y_predict,Y_label[0],W2,b2)loss_list=[]
accuracy_list=[]
learn_rating=0.01
epoch=2000
def train():index=0global W,W2,b,b2for i in range(epoch):W_g=torch.zeros(dim,neuron_num)b_g=torch.zeros(neuron_num)W2_g=torch.zeros(neuron_num,1)b2_g=torch.zeros(1)loss=torch.tensor([0.0])co=0for j in range(32):try:y_predict=net(X_data[index_sequense[index]],W,b,W2,b2)grad_w,grad_b,grad_w2,grad_b2=grad(X_data[index_sequense[index]],W,b,y_predict,Y_label[index_sequense[index]],W2,b2)#   print(grad_w2.size(),W_g.size())grad_w2=torch.t(grad_w2)W_g=W_g+grad_w2grad_b2=grad_b2.reshape(10)#print("b_g",b_g)#print("grad_b2",grad_b2)b_g=grad_b2+b_gW2_g=W2_g+torch.t(grad_w)b2_g=b2_g+torch.t(grad_b)#   print("fdafaf",grad_w,grad_b,grad_w2,grad_b2)loss=loss+loss_function_crossEntropy(y_predict,Y_label[index_sequense[index]])# print( Y_label[index],y_predict[0][0])if (Y_label[index_sequense[index]]==1) &( y_predict[0][0]>0.5):co=co+1if (Y_label[index_sequense[index]]==0) &( y_predict[0][0]<=0.5):co=co+1index=index+1except:index=0print("loss:",loss[0])print("accuracy:",co/32)loss_list.append(loss[0])accuracy_list.append(co/32)W_g=W_g/batchb_g=b_g/batchW2_g=W2_g/batchb2_g=b2_g/batch#print(W.size())#print(b.size())#print(W2.size())#print(b2.size())W=W+learn_rating*W_g#   print("b*********************",b,b_g)b=b+learn_rating*(b_g)#print(W2_g.size())#print(b2_g.size())W2=W2+learn_rating*W2_gb2=b2+learn_rating*b2_g#print(W.size())#print(b.size())#print(W2.size())#print(b2.size())train()
epoch_list=list(range(epoch))
plt.plot(epoch_list,loss_list,label='SGD')
plt.title("loss")
plt.legend()plt.show()epoch_list=list(range(epoch))
plt.plot(epoch_list,accuracy_list,label='SGD')
plt.title("loss")
plt.legend()
plt.show()