Pytorch 实现线性回归

import torch

from torch.utils import data

from torch import nn

# 合成数据

def synthetic_data(w, b, num_examples):

    """y = Xw + b + zs"""

    X = torch.normal(0, 1, (num_examples, len(w)))

    y = torch.matmul(X, w) + b

    y += torch.normal(0, 0.01, y.shape)

    return X, y.reshape((-1, 1))

# 用于合成数据的模板

true_w = torch.tensor([2, -3.4, 2])

true_b = 4.2

# 合成1000个数据

features, labels = synthetic_data(true_w, true_b, 1000)

# 随机批量加载数据

def load_array(data_arrays, batch_size, is_train=True):

    dataset = data.TensorDataset(*data_arrays)

    return data.DataLoader(dataset, batch_size, shuffle=is_train)

batch_size = 10

data_iter = load_array((features, labels), batch_size)

# 初始化线性网络，3输入1输出

net = nn.Sequential(nn.Linear(3, 1))

# 均方误差损失函数

loss = nn.MSELoss()

# 优化算法

trainer = torch.optim.SGD(net.parameters(), lr=0.03)

# 开始迭代

num_epochs = 3

for epoch in range(num_epochs):

    for X, y in data_iter:

        l = loss(net(X), y)

        trainer.zero_grad()

        l.backward()

        trainer.step()

    l = loss(net(features), labels)

    print(f'epoch {epoch + 1}, loss {l:f}')
巴特西

Pytorch 实现线性回归

Pytorch 实现线性回归

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