torch--模型选择-欠拟合-过拟合

"""
模型选择，欠拟合、过拟合
"""import math
import numpy as np
import torch
from d2l import torch as d2l
from IPython import display
import matplotlib.pyplot as plt
from torch import nnmax_degree = 20                                # 多项式的最大阶数
n_train, n_test = 100, 100                     # 训练和测试数据集大小
true_w = np.zeros(max_degree)                  # 分配大量的空间
true_w[0:4] = np.array([5, 1.2, -3.4, 5.6])features = np.random.normal(size=(n_train + n_test, 1))
np.random.shuffle(features)
poly_features = np.power(features, np.arange(max_degree).reshape(1, -1))
for i in range(max_degree):poly_features[:, i] /= math.gamma(i + 1)  # gamma(n)=(n-1)!
# labels的维度:(n_train+n_test,)
labels = np.dot(poly_features, true_w)
labels += np.random.normal(scale=0.1, size=labels.shape)# NumPy ndarray转换为tensor
true_w, features, poly_features, labels = [torch.tensor(x, dtype=torch.float32) for x in [true_w, features, poly_features, labels]]features[:2], poly_features[:2, :], labels[:2]class Animator:"""在动画中绘制数据"""def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,ylim=None, xscale='linear', yscale='linear',fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,figsize=(3.5, 2.5), pic_name=None):# 增量地绘制多条线if legend is None:legend = []d2l.use_svg_display()self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)if nrows * ncols == 1:self.axes = [self.axes, ]# 使用lambda函数捕获参数self.config_axes = lambda: d2l.set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)self.X, self.Y, self.fmts = None, None, fmtsself.pic_name = pic_namedef add(self, x, y):# 向图表中添加多个数据点if not hasattr(y, "__len__"):y = [y]n = len(y)if not hasattr(x, "__len__"):x = [x] * nif not self.X:self.X = [[] for _ in range(n)]if not self.Y:self.Y = [[] for _ in range(n)]for i, (a, b) in enumerate(zip(x, y)):if a is not None and b is not None:self.X[i].append(a)self.Y[i].append(b)self.axes[0].cla()for x, y, fmt in zip(self.X, self.Y, self.fmts):self.axes[0].plot(x, y, fmt)self.config_axes()plt.draw()plt.pause(0.1)plt.savefig(self.pic_name)display.display(self.fig)display.clear_output(wait=True)def show(self):display.display(self.fig)def evaluate_loss(net, data_iter, loss):"""评估给定数据集上模型的损失"""metric = d2l.Accumulator(2)  # 损失的总和,样本数量for X, y in data_iter:out = net(X)y = y.reshape(out.shape)l = loss(out, y)metric.add(l.sum(), l.numel())return metric[0] / metric[1]def train_epoch_ch3(net, train_iter, loss, updater):"""训练模型一个迭代周期（定义见第3章）"""# 将模型设置为训练模式if isinstance(net, torch.nn.Module):net.train()# 训练损失总和、训练准确度总和、样本数# metric = d2l.Accumulator(3)for X, y in train_iter:# 计算梯度并更新参数y_hat = net(X)l = loss(y_hat, y)if isinstance(updater, torch.optim.Optimizer):# 使用PyTorch内置的优化器和损失函数updater.zero_grad()l.mean().backward()updater.step()else:# 使用定制的优化器和损失函数l.sum().backward()updater(X.shape[0])def train(train_features, test_features, train_labels, test_labels,num_epochs=400, state='正常'):loss = nn.MSELoss(reduction='none')input_shape = train_features.shape[-1]# 不设置偏置，因为我们已经在多项式中实现了它net = nn.Sequential(nn.Linear(input_shape, 1, bias=False))batch_size = min(10, train_labels.shape[0])train_iter = d2l.load_array((train_features, train_labels.reshape(-1, 1)),batch_size)test_iter = d2l.load_array((test_features, test_labels.reshape(-1, 1)),batch_size, is_train=False)trainer = torch.optim.SGD(net.parameters(), lr=0.01)animator = Animator(xlabel='epoch', ylabel='loss', yscale='log',xlim=[1, num_epochs], ylim=[1e-3, 1e2],legend=['train', 'test'], pic_name=f'model_select_{state}')for epoch in range(num_epochs):train_epoch_ch3(net, train_iter, loss, trainer)if epoch == 0 or (epoch + 1) % 20 == 0:animator.add(epoch + 1, (evaluate_loss(net, train_iter, loss),evaluate_loss(net, test_iter, loss)))print('weight:', net[0].weight.data.numpy())# 三阶多项式函数拟合(正常)
# 从多项式特征中选择前4个维度，即1,x,x^2/2!,x^3/3!
train(poly_features[:n_train, :4], poly_features[n_train:, :4],labels[:n_train], labels[n_train:], state='正常拟合')# 线性函数拟合(欠拟合)
# 从多项式特征中选择前2个维度，即1和x
train(poly_features[:n_train, :2], poly_features[n_train:, :2],labels[:n_train], labels[n_train:], state='欠拟合')# 高阶多项式函数拟合(过拟合)
# 从多项式特征中选取所有维度
train(poly_features[:n_train, :], poly_features[n_train:, :],labels[:n_train], labels[n_train:], num_epochs=1500, state='过拟合')