Pytorch Deep Learning (6): Logistic Regression (Logistic Regression) deals with classification problems

Data x_data = [[1.0], [2.0], [3.0]], classification: y_data = [[0], [0], [1]] The code is as follows
:

import torch

x_data = torch.Tensor([[1.0], [2.0], [3.0]])
y_data = torch.Tensor([[0], [0], [1]])

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

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

criterion = torch.nn.BCELoss(reduction='sum')
optimizer = 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())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

An example of the print result is as follows:

You can do the following test: (weekly learning time, (0, 10) hours, collect 200 points, packaged as a matrix with 200 rows and 1 column)

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 200)
x_t = torch.Tensor(x).view((200, 1))
y_t = model(x_t)
y = y_t.data.numpy()
plt.plot(x, y)
plt.plot([0, 10], [0.5, 0.5], c='r')
plt.xlabel('Hours')
plt.ylabel('Probability of Pass')
plt.grid()
plt.show()

The printed curve is as follows:

It can be seen that more than 3 hours are qualified