%matplotlib inline

import mxnet

from mxnet import nd,autograd

from mxnet import gluon,init

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

import gluonbook as gb

n_train, n_test, num_inputs = 20,100,200

true_w = nd.ones((num_inputs, 1)) * 0.01

true_b = 0.05

features = nd.random.normal(shape=(n_train+n_test, num_inputs))

labels = nd.dot(features,true_w) + true_b

labels += nd.random.normal(scale=0.01, shape=labels.shape)

train_feature = features[:n_train,:]

test_feature = features[n_train:,:]

train_labels = labels[:n_train]

test_labels = labels[n_train:]

#print(features,train_feature,test_feature)

# 初始化模型参数

def init_params():

    w = nd.random.normal(scale=1, shape=(num_inputs, 1))

    b = nd.zeros(shape=(1,))

    w.attach_grad()

    b.attach_grad()

    return [w,b]

# 定义，训练，测试

batch_size = 1

num_epochs = 100

lr = 0.03

train_iter = gdata.DataLoader(gdata.ArrayDataset(train_feature,train_labels),batch_size=batch_size,shuffle=True)

# 定义网络

def linreg(X, w, b):

    return nd.dot(X,w) + b

# 损失函数

def squared_loss(y_hat, y):

    """Squared loss."""

    return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2

# L2 范数惩罚

def l2_penalty(w):

    return (w**2).sum() / 2

def sgd(params, lr, batch_size):

    for param in params:

        param[:] = param - lr * param.grad / batch_size

def fit_and_plot(lambd):

    w, b = init_params()

    train_ls, test_ls = [], []

    for _ in range(num_epochs):

        for X, y in train_iter:

            with autograd.record():

                # 添加了 L2 范数惩罚项。

                l = squared_loss(linreg(X, w, b), y) + lambd * l2_penalty(w)

            l.backward()

            sgd([w, b], lr, batch_size)

        train_ls.append(squared_loss(linreg(train_feature, w, b),

                             train_labels).mean().asscalar())

        test_ls.append(squared_loss(linreg(test_feature, w, b),

                            test_labels).mean().asscalar())

    gb.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss',

                range(1, num_epochs + 1), test_ls, ['train', 'test'])

    print('L2 norm of w:', w.norm().asscalar())

fit_and_plot(0)
fit_and_plot(3)