李沐动手学深度学习代码问题求解

在欠拟合和过拟合一节中，原封不动把代码搬过来，却报错了

import math
import numpy as np
import torch
from torch import nn
from d2l import torch as d2l

max_degree = 20
n_train, n_test = 100, 100
true_w = np.zeros(max_degree)
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)
labels = np.dot(poly_features, true_w)
labels += np.random.normal(scale=0.1, size=labels.shape)


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]


def train(train_features, test_features, train_labels, test_labels,
          num_epochs=400):
    loss = nn.MSELoss()
    input_shape = train_features.shape[-1]
    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 = d2l.Animator(xlabel='epoch', ylabel='loss', yscale='log',
                            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())


train(poly_features[:n_train, :4], poly_features[n_train:, :4],
      labels[:n_train], labels[n_train:])

报错如下：

Traceback (most recent call last):
  File "D:/pycharm_workspace/other_learn/main.py", line 55, in <module>
    labels[:n_train], labels[n_train:])
  File "D:/pycharm_workspace/other_learn/main.py", line 39, in train
    batch_size)
  File "D:\Anaconda3\lib\site-packages\d2l\torch.py", line 160, in load_array
    dataset = data.TensorDataset(*data_arrays)
  File "D:\Anaconda3\lib\site-packages\torch\utils\data\dataset.py", line 158, in __init__
    assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)
  File "D:\Anaconda3\lib\site-packages\torch\utils\data\dataset.py", line 158, in <genexpr>
    assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)
TypeError: 'int' object is not callable

我哪里变量和函数重名了？？求解，重酬！