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)))