用pytorch实现神经网络线性回归

用pytorch实现神经网络线性回归

import torch
import matplotlib.pyplot as plt
from torch import nn, optim
from time import perf_counter
x = torch.unsqueeze(torch.linspace(-3, 3, 10000), dim=1)
y = x + 1.2 * torch.rand(x.size())


class LR(nn.Module):
    def __init__(self):
        super(LR, self).__init__()
        self.linear = nn.Linear(1, 1)

    def forward(self, x):
        out = self.linear(x)
        return out


CUDA = torch.cuda.is_available()
if CUDA:
    LR_model = LR().cuda()
    inputs = x.cuda()
    target = y.cuda()
else:
    LR_model = LR()
    inputs = x
    target = y


criterion = nn.MSELoss()
optimizer = optim.SGD(LR_model.parameters(), lr=1e-4)


def draw(output, loss):
    if CUDA:
        output = output.cpu()
    plt.cla()
    plt.scatter(x.numpy(), y.numpy())
    plt.plot(x.numpy(), output.data.numpy(), 'r-', lw=5)
    plt.text(0.5, 0, 'loss=%s'%(loss.item()), fontdict={
     'size':'20', 'color':'red'})
    plt.pause(0.005)


def train(model, criterion, optimizer, epochs):
    for epoch in range(epochs):
        output = model(inputs)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if epoch % 80 == 0:
            draw(output, loss)
    return model, loss


start = perf_counter()
LR_model, loss = train(LR_model, criterion, optimizer, 20000)
finish = perf_counter()
time = finish - start
print("计算时间:%s" % time)
print("final loss:", loss.item())
print("weights:", list(LR_model.parameters()))