电动车网站建设,备案 个人网站名称,网页设计模板素材美食,平湖网站改版1.通过多项式拟合交互探索概念
import math
import numpy as np
import torch
from torch import nn
from d2l import torch as d2l 2.使用三阶多项式来生成训练和测试数据的标签 max_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…1.通过多项式拟合交互探索概念
import math
import numpy as np
import torch
from torch import nn
from d2l import torch as d2l 2.使用三阶多项式来生成训练和测试数据的标签 max_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_trainn_test,)
labels np.dot(poly_features, true_w)
labels np.random.normal(scale0.1, sizelabels.shape) 3.查看样本
true_w, features, poly_features, labels [torch.tensor(x, dtypetorch.float32) for x in [true_w, features, poly_features, labels]]features[:2], poly_features[:2, :], labels[:2] 4.实现函数来评估模型在给定数据集的损失
def evaluate_loss(net, data_iter, loss):评估给定数据集上模型的损失metric d2l.Accumulator(2)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] 5.定义训练函数
def train(train_features, test_features, train_labels, test_labels,num_epochs400):loss nn.MSELoss()input_shape train_features.shape[-1]net nn.Sequential(nn.Linear(input_shape, 1, biasFalse))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_trainFalse)trainer torch.optim.SGD(net.parameters(), lr0.01)animator d2l.Animator(xlabelepoch, ylabelloss, yscalelog,xlim[1, num_epochs], ylim[1e-3, 1e2],legend[train, test])for epoch in range(num_epochs):d2l.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())
