自我记录：Python学习之Pytorch 04 逻辑回归

代码取自B站刘二老师的《Pytorch深度学习实践》视频教程

import torch
import numpy as np
import matplotlib.pyplot as plt

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


# trian_set=torchvision.datasets.MNIST(root='../dataset/mnist',train=True,download=True)
# test_set=torchvision.datasets.MNIST(root='../dataset/mnist',train=False,download=True)

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()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练循环
for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.data)

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

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()

使用batch与逻辑回归对糖尿病数据集进行训练

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np


class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, dtype=np.float32, delimiter=",")
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len


dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=2)


class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x


model = Model()

# Dataset是抽象类

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

if __name__ == '__main__':

    for epoch in range(1000):
        for i, data in enumerate(train_loader, 0):
            # 1.Prepare data
            inputs, labels = data

            # 2.Forward
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()