pytorch纯手工实现logistic回归

import torch import matplotlib.pyplot as plt import numpy as np n_data = torch.ones(50, 2) x1 = torch.normal(2 * n_data, 1) y1 = torch.zeros(50) x2 = torch.normal(-2 * n_data, 1) y2 = torch.ones(50) x = torch.cat((x1, x2), 0).type(torch.FloatTensor) y = torch.cat((y1, y2), 0).type(torch.FloatTensor) # 人工构造的数据集 # plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn') # plt.show() # 首先通过torch.nn进行实现 import torch.utils.data as Data from torch import nn def data_iter(batch_size,features,labels): num_examples=len(features) indices=list(range(num_examples)) np.random.shuffle(indices) for i in range(0,num_examples,batch_size): j=torch.LongTensor(indices[i:min(i+batch_size,num_examples)]) yield features.index_select(0,j),labels.index_select(0,j)#yield可以理解为断点 batch_size = 10 num_inputs = 2 w=torch.tensor(np.random.normal(0,0.01,(num_inputs,1)),dtype=torch.float32) b=torch.zeros(1,dtype=torch.float32) w.requires_grad_(requires_grad=True) b.requires_grad_(requires_grad=True) def logistic_reg(X,w,b): return torch.sigmoid((torch.mm(X,w)+b)) def sgd(params,lr,batch_size): for param in params: param.data-=lr*param.grad/batch_size lr=0.03 num_epochs=40 net=logistic_reg loss=nn.BCELoss() for epoch in range(1,num_epochs+1): for Xx,yy in data_iter(batch_size,x,y): l=loss(net(Xx,w,b),yy.view(-1,1)) l.backward() sgd([w,b],lr,batch_size) w.grad.data.zero_() b.grad.data.zero_() train_l=loss(net(x,w,b),y.view(-1,1)) value = net(x,w,b) mask = value.ge(0.5).float() correct = (mask.view(-1, 1) == y.view(-1, 1)).sum() print("epoch%d,损失:%f,准确率为:%f" % (epoch, l.item(), correct / len(x)))