深度学习之线性回归，使用maxnet工具

# -*- coding:utf-8 -*-
from matplotlib import pyplot as plt
from mxnet import autograd, nd
import matplotlib as mpl
import random
mpl.rcParams["font.sans-serif"] = ['Fangsong']
mpl.rcParams['axes.unicode_minus'] = False
def visualization(features, labels):
    plt.scatter(features[:, 1].asnumpy(), labels.asnumpy())
    plt.show()
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)  # 读取样本是随机的
    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)  # take函数根据索引返回函数对应元素
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 params in params:
        params[:] = params - lr * params.grad / batch_size
if __name__ == '__main__':
    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
    # visualization(features, labels)
    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()
    lr = 0.03
    num_epoch = 30
    net = linreg
    loss = squared_loss
    for epoch in range(num_epoch):
        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: %lf'%(epoch+1, train_l.mean().asnumpy()))
    print(true_w, true_b)
    print(w, b)

# -*- coding:utf-8 -*-
from matplotlib import pyplot as plt
from mxnet import autograd, nd, init
from mxnet.gluon import data as gdata
import matplotlib as mpl
from mxnet.gluon import nn
from mxnet import gluon
from mxnet.gluon import loss as gloss
import random
mpl.rcParams["font.sans-serif"] = ['Fangsong']
mpl.rcParams['axes.unicode_minus'] = False
def visualization(features, labels):
    plt.scatter(features[:, 1].asnumpy(), labels.asnumpy())
    plt.show()
if __name__ == '__main__':
    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
    # visualization(features, labels)
    batch_size = 10
    dataset = gdata.ArrayDataset(features, labels)
    data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)
    lr = 0.03
    num_epoch = 30
    net = nn.Sequential()
    net.add(nn.Dense(1))
    net.initialize(init.Normal(sigma=0.01))
    loss = gloss.L2Loss()
    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})
    for epoch in range(num_epoch):
        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: %lf' % (epoch + 1, l.mean().asnumpy()))
    dense = net[0]
    print(true_w, dense.weight.data())
    print(true_b, dense.bias.data())