MXNET：权重衰减-gluon实现

时间：2021-12-19 12:04:56

构建数据集

# -*- coding: utf-8 -*-

from mxnet import init

from mxnet import ndarray as nd

from mxnet.gluon import loss as gloss

import gb

n_train = 20

n_test = 100

num_inputs = 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_features, test_features = features[:n_train, :], features[n_train:, :]

train_labels, test_labels = labels[:n_train], labels[n_train:]

数据迭代器

from mxnet import autograd

from mxnet.gluon import data as gdata

batch_size = 1

num_epochs = 10

learning_rate = 0.003

train_iter = gdata.DataLoader(gdata.ArrayDataset(

    train_features, train_labels), batch_size, shuffle=True)

loss = gloss.L2Loss()

训练并展示结果

gb.semilogy函数：绘制训练和测试数据的loss

from mxnet import gluon

from mxnet.gluon import nn

def fit_and_plot(weight_decay):

    net = nn.Sequential()

    net.add(nn.Dense(1))

    net.initialize(init.Normal(sigma=1))

    # 对权重参数做 L2 范数正则化，即权重衰减。

    trainer_w = gluon.Trainer(net.collect_params('.*weight'), 'sgd', {

        'learning_rate': learning_rate, 'wd': weight_decay})

    # 不对偏差参数做 L2 范数正则化。

    trainer_b = gluon.Trainer(net.collect_params('.*bias'), 'sgd', {

        'learning_rate': learning_rate})

    train_ls = []

    test_ls = []

    for _ in range(num_epochs):

        for X, y in train_iter:

            with autograd.record():

                l = loss(net(X), y)

            l.backward()

            # 对两个 Trainer 实例分别调用 step 函数。

            trainer_w.step(batch_size)

            trainer_b.step(batch_size)

        train_ls.append(loss(net(train_features),

                             train_labels).mean().asscalar())

        test_ls.append(loss(net(test_features),

                            test_labels).mean().asscalar())

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

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

    return 'w[:10]:', net[0].weight.data()[:, :10], 'b:', net[0].bias.data()

print fit_and_plot(5)
  • 使用 Gluon 的 wd 超参数可以使用权重衰减来应对过拟合问题。
  • 我们可以定义多个 Trainer 实例对不同的模型参数使用不同的迭代方法。

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