线性回归从零开始的实现过程python实现

from IPython import display
from matplotlib import pyplot as plt
import torch
import numpy as np
from mxnet import autograd,nd
import random
import matplotlib_inline
num_inputs = 2
num_examples = 1000
true_w = [2,-3.4]
true_b = 4.2
features = nd.random.normal(scale=1, shape=(num_examples,num_inputs))
labels = true_w[0] * features[:,0] + true_w[1] * features[:,1] + true_b
labels += nd.random.normal(scale = 0.01,shape=labels.shape)
print(features[0])
print(labels[0])
def use_svg_display():
#    display.set_matplotlib_formats('svg')
    matplotlib_inline.backend_inline.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].asnumpy(),labels.asnumpy(),1)
#plt.show()
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 = nd.array(indices[i:min(i + batch_size, num_examples)])
       yield features.take(j), labels.take(j)
batch_size = 10
for x,y in data_iter(batch_size,features,labels):
    print(x,y)
    break;
w = nd.random.normal(scale=0.01, shape=(num_inputs,1))
b = nd.zeros(shape=(1,))
w.attach_grad()
b.attach_grad()
print(w)
print(b)
def linreg(x,w,b):
    return nd.dot(x,w)+b
def squared_loss(y_hat,y):
    return (y_hat - y.reshape(y_hat.shape))**2/2
def sgd(params,lr,batch_size):
    for param in params:
        param = param -lr*param.grad/batch_size
#        param[:] = param - 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):
        with autograd.record():
            l = loss(net(x,w,b),y)
            l.backward()
            sgd([w,b],lr,batch_size)
            train_l = loss(net(features,w,b),labels)
            print('epoch %d, loss %f' %(epoch+1, train_l.mean().asnumpy()))
print(true_w,w)
print(true_b,b)
print(l)