liner.py

import torch
from IPython import display
from matplotlib import pyplot as plt
import numpy as np
import random
# This is a sample Python script.

# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.

# 本函数已保存在d2lzh包中方便以后使用
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)  # 样本的读取顺序是随机的
    for i in range(0, num_examples, batch_size):
        j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)]) # 最后一次可能不足一个batch
        yield  features.index_select(0, j), labels.index_select(0, j)

def linreg(X, w, b):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    return torch.mm(X, w) + b

def squared_loss(y_hat, y):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    # 注意这里返回的是向量, 另外, pytorch里的MSELoss并没有除以 2
    return (y_hat - y.view(y_hat.size())) ** 2 / 2

def sgd(params, lr, batch_size):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    for param in params:
        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data

def use_svg_display():
    # 用矢量图显示
    display.set_matplotlib_formats('svg')

def set_figsize(figsize=(3.5, 2.5)):
    use_svg_display()
    # 设置图的尺寸
    plt.rcParams['figure.figsize'] = figsize

def linreg(X, w, b):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    return torch.mm(X, w) + b

def squared_loss(y_hat, y):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    # 注意这里返回的是向量, 另外, pytorch里的MSELoss并没有除以 2
    return (y_hat - y.view(y_hat.size())) ** 2 / 2

def sgd(params, lr, batch_size):  # 本函数已保存在d2lzh_pytorch包中方便以后使用
    for param in params:
        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    num_inputs = 2
    num_examples = 1000
    true_w = [2, -3.4]
    true_b = 4.2
    features = torch.randn(num_examples, num_inputs,
                           dtype=torch.float32)

    labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
    labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()),
                           dtype=torch.float32)
    # print(features[0], labels[0])
    set_figsize()
    plt.scatter(features[:, 1].numpy(), labels.numpy(), 1)
    # plt.show()
    batch_size = 10

    for X, y in data_iter(batch_size, features, labels):
        print(X, y)
        break
    w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, 1)), dtype=torch.float32)
    b = torch.zeros(1, dtype=torch.float32)
    w.requires_grad_(requires_grad=True)
    b.requires_grad_(requires_grad=True)

    lr = 0.03
    num_epochs = 3
    net = linreg
    loss = squared_loss

    for epoch in range(num_epochs):  # 训练模型一共需要num_epochs个迭代周期
        # 在每一个迭代周期中，会使用训练数据集中所有样本一次（假设样本数能够被批量大小整除）。X
        # 和y分别是小批量样本的特征和标签
        for X, y in data_iter(batch_size, features, labels):
            l = loss(net(X, w, b), y).sum()  # l是有关小批量X和y的损失
            l.backward()  # 小批量的损失对模型参数求梯度
            sgd([w, b], lr, batch_size)  # 使用小批量随机梯度下降迭代模型参数

            # 不要忘了梯度清零
            w.grad.data.zero_()
            b.grad.data.zero_()
        train_l = loss(net(features, w, b), labels)
        print('epoch %d, loss %f' % (epoch + 1, train_l.mean().item()))

        print(true_w, '\n', w)
        print(true_b, '\n', b)