pytorch 线性回归

%matplotlib inline
import torch
from IPython import display
from matplotlib import pyplot as plt
import numpy as np
import random


num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2
features = torch.from_numpy(np.random.normal(0, 1, (num_examples, num_inputs)))

labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
labels += torch.from_numpy(np.random.normal(0, 0.01, size=labels.size()))

def use_svg_display():
    # 用用矢量图显示
    display.set_matplotlib_formats('svg')
    
def set_figsize(figsize=(3.5, 2.5)):
    use_svg_display()
    # 设置图的尺寸寸
    plt.rcParams['figure.figsize'] = figsize

set_figsize()

plt.scatter(features[:, 1].numpy(), labels.numpy(), 1);

# 本函数已保存在d2lzh包中方便以后使用用
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