线性回归的简单实现python

from mxnet import autograd,nd
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)
from mxnet.gluon import data as gdata
batch_size = 10
dataset = gdata.ArrayDataset(features, labels)
data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)
for x,y in data_iter:
    print(x,y)
    break
from mxnet.gluon import nn
net = nn.Sequential()
net.add(nn.Dense(1))
from mxnet import init
net.initialize(init.Normal(sigma=0.01))
from mxnet.gluon import loss as gloss
loss = gloss.L2Loss()
from mxnet import gluon
trainer = gluon.Trainer(net.collect_params(),'sgd',{'learning_rate':0.03})
num_epochs = 8
for epoch in range(1,num_epochs +1):
    for x,y in data_iter:
        with autograd.record():
            l = loss(net(x),y)
        l.backward()
        trainer.step(batch_size)
    l=loss(net(features),labels)
    print('epoch %d, loss: %f' %(epoch, l.mean().asnumpy()))
dense=net[0]
print(true_w,dense.weight.data())
print(true_b,dense.bias.data())