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课程请见

《PyTorch深度学习实践》

# PyTorch
import torch
from torch import nn
from torch import optim
# For plotting
import matplotlib.pyplot as plt
# os
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

x_data = torch.Tensor([1., 2., 3.]).reshape(-1, 1)
y_data = torch.Tensor([0, 0, 1]).reshape(-1, 1)


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

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


def init_weight(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, std=0.01)


net = LogisticRegressionModel()
net.apply(init_weight)
criterion = nn.BCELoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)
num_epochs = 1000
for epoch in range(num_epochs):
    _y = net(x_data)
    loss = criterion(_y, y_data)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
print(net.linear.weight.data)

test_data = torch.Tensor(list(range(11))).reshape(-1, 1)
y_pre = net(test_data)
plt.plot(test_data.tolist(), y_pre.tolist())
plt.xlabel('x')
plt.plot([0, 11], [0.5, 0.5], c='r')
plt.ylabel('pre y')
plt.grid()
plt.show()

和老师的

一模一样