Gluon sgd

from mxnet import nd,autograd,init,gluon
from mxnet.gluon import data as gdata,loss as gloss,nn

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)

# 造小批量数据集
dataset = gdata.ArrayDataset(features,labels)
batch_size = 10
data_iter = gdata.DataLoader(dataset,batch_size,shuffle=True)

# 定义网络
net = nn.Sequential()
net.add(nn.Dense(1))

net.initialize(init.Normal(sigma=0))


# 损失函数
loss = gloss.L2Loss()

# 优化算法
trainer = gluon.Trainer(net.collect_params(),'sgd',{'learning_rate':0.01})

num_epochs = 3
for epoch in range(1, num_epochs + 1):
    for X, y in data_iter:
        print(X)
        print(y)
        with autograd.record():
            l = loss(net(X), y)
        print(l)
        l.backward()
        trainer.step(batch_size)
    l = loss(net(features), labels)
    print('epoch %d, loss: %f' % (epoch, l.mean().asnumpy()))
    

从最简单的线性回归来说，小批量随机梯度下降的时候，X，y 从迭代器中取出，也是bach_size大小的数据集，那么网络的计算，同样也是小批量的。

即代码 l = loss(net(X),y) 包含了，小批量数据集，每一个数据丢到网络中，计算出返回值以后，和真实值得损失。