构建数据集
# -*- coding: utf-8 -*-from mxnet import initfrom mxnet import ndarray as ndfrom mxnet.gluon import loss as glossimport gbn_train = 20n_test = 100num_inputs = 200true_w = nd.ones((num_inputs, 1)) * 0.01true_b = 0.05features = nd.random.normal(shape=(n_train+n_test, num_inputs))labels = nd.dot(features, true_w) + true_blabels += 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 autogradfrom mxnet.gluon import data as gdatabatch_size = 1num_epochs = 10learning_rate = 0.003train_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 gluonfrom mxnet.gluon import nndef 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。