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.randn(num_examples,num_inputs,dtype=torch.float32)
print("生成的数据集X:",features)
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)
def use_svg_display():
display.set_matplotlib_formats('svg')
def set_figsize(figsize=(3.5,2.5)):
use_svg_display()
plt.rcParams['figure.figsize'] = figsize
set_figsize()
plt.scatter(features[:, 1].numpy(),labels.numpy(),1)
def data_iter(batch_size,features,labels):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices)
for i in range(0,num_examples,batch_size):
j = torch.LongTensor(indices[i: min(i + batch_size,num_examples)])
yield features.index_select(0,j), labels.index_select(0,j)
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)
def linreg(X, w, b):
return torch.mm(X,w) +b
def squared_loss(y_hat, y):
return (y_hat - y.view(y_hat.size())) ** 2 / 2
def sgd(params, lr, batch_size):
for param in params:
param.data -= lr * param.grad / batch_size
lr = 0.03
num_epochs = 3
net = linreg
loss = squared_loss
for epoch in range(num_epochs):
for X,y in data_iter(batch_size,features,labels):
l = loss(net(X, w, b),y).sum()
l.backward()
sgd([w,b], lr, batch_size)
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)
D:\dev\anaconda\python.exe E:/DL-Pytorch/ch3/demo2.py
tensor([[ 1.0830, -0.8833],
[ 0.5864, 0.7240],
[ 0.9755, -0.6456],
[-1.6580, 0.9215],
[-0.1988, -1.2996],
[ 1.0861, -0.3094],
[ 0.4570, -0.8737],
[-0.8036, -0.2254],
[ 0.5252, 0.9146],
[-0.0726, -1.0572]]) tensor([ 9.3613, 2.9110, 8.3544, -2.2501, 8.2146, 7.4407, 8.0797, 3.3551,
2.1247, 7.6568])
epoch 1, loss 0.037553
epoch 2, loss 0.000137
epoch 3, loss 0.000051
[2, -3.4]
tensor([[ 1.9997],
[-3.3992]], requires_grad=True)
4.2
tensor([4.1995], requires_grad=True)
Process finished with exit code 0