MXNet动手学深度学习笔记：Gluon实现线性回归

#coding:utf-8

from mxnet import ndarray as nd
from mxnet import autograd
from mxnet import gluon

num_inputs = 2
num_examples = 1000

true_w = [2,-3.4]
true_b = 4.2

X = nd.random_normal(shape=(num_examples,num_inputs))
y = true_w[0] * X[:,0] + true_w[1] * X[:,1] + true_b
y += 0.01 * nd.random_normal(shape=y.shape)

# 读取数据
batch_size = 10
dataset = gluon.data.ArrayDataset(X,y)
data_iter = gluon.data.DataLoader(dataset,batch_size,shuffle=True)

# 定义模型
net = gluon.nn.Sequential()
# 添加Dense层
net.add(gluon.nn.Dense(1))

# 初始化模型参数
net.initialize()

# 定义损失函数:平方误差函数
square_loss = gluon.loss.L2Loss()

# 定义优化
trainer = gluon.Trainer(net.collect_params(),'sgd',{'learning_rate':0.1})

# 训练
epochs = 5
batch_size = 10

for e in range(epochs):
    total_loss = 0
    for data,label in data_iter:
        with autograd.record():
            output = net(data)
            loss = square_loss(output,label)
        loss.backward()
        trainer.step(batch_size)
        total_loss += nd.sum(loss).asscalar()
    
    print('Epoch %d,average loss:%f' % (e,total_loss / num_examples))

# 查询训练后的权重和位移参数
dense = net[0]
print('true_w:',true_w)
print('weight:',dense.weight.data())
print('true_b:',true_b)
print('bias',dense.bias.data())