线性的回归

import time

import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
x_data = torch.tensor([[1],[2],[3]],dtype=torch.float32)
y_data = torch.tensor([[0],[0],[1]],dtype=torch.float32)

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

    def forward(self,x):
        y_pred = self.linear(x)
        return torch.sigmoid(y_pred)

model = Model()
criterion = torch.nn.BCELoss(size_average=False)
optim = torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch, loss.item())
    # time.sleep(0.1)

    optim.zero_grad()
    loss.backward()
    optim.step()

x = np.linspace(0,10,200)
x_t = torch.tensor(x,dtype=torch.float32).view((200,1))
y_t = model(x_t)
y = y_t.data.numpy()
plt.plot(x,y)
plt.show()