使用最新版d2l【1.0.3】的同学，由于新版d2l包不包含第三章训练代码，需要手动在【环境目录\d2l\Lib\site-packages\d2l\torch.py】文件中添加以下代码：
py
def evaluate_accuracy(net, data_iter: torch.utils.data.DataLoader):
if isinstance(net, torch.nn.Module):
net.eval()
metric = Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
metric.add(accuracy(net(X), y), y.numel())
return metric[0] / metric[1]
def train_epoch_ch3(net, train_iter, loss, updater):
metrics = Accumulator(3)
if isinstance(net, torch.nn.Module):
net.train()
for X, y in train_iter:
y_hat = net(X)
l = loss(y_hat, y)
if isinstance(updater, torch.optim.Optimizer):
updater.zero_grad()
l.mean().backward()
updater.step()
else:
l.sum().backward()
updater(X.shape[0]) # number of X's samples
metrics.add(float(l.detach().sum()), accuracy(y_hat, y), y.numel())
return metrics[0] / metrics[2], metrics[1] / metrics[2]
def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):
"""训练模型（定义见第3章）"""
animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],
legend=['train loss', 'train acc', 'test acc'])
for i in range(num_epochs):
train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
test_acc = evaluate_accuracy(net, test_iter)
animator.add(i + 1, train_metrics + (test_acc,))
train_loss, train_acc = train_metrics
assert train_loss < 0.5, train_loss
assert train_acc <= 1 and train_acc > 0.7, train_acc
assert test_acc <= 1 and test_acc > 0.7, test_acc改动点：将本书中的：
metrics.add(float(l.sum()), accuracy(y_hat, y), y.numel())
改为：
metrics.add(float(l.detach().sum()), accuracy(y_hat, y), y.numel())
同时，针对使用在Pycharm专业版中，使用Jupyter时，没有图像输出，或者图像一闪而过的情况，需修改以下代码：
在【环境目录\d2l\Lib\site-packages\d2l\torch.py】文件中，将【Animator】类中的：
py
display.display(self.fig)
display.clear_output(wait=True)注释掉即可。
新的【Animator】类如下：【注：3.6中存在这样的问题也直接去掉就好了，3.7中需要在上面提到的torch文件中手动注释】
py
class Animator:
"""For plotting data in animation."""
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)):
"""Defined in :numref:`sec_utils`"""
# Incrementally plot multiple lines
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, ]
# Use a lambda function to capture arguments
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, fmts
def add(self, x, y):
# Add multiple data points into the figure
if not hasattr(y, "__len__"):
y = [y]
n = len(y)
if not hasattr(x, "__len__"):
x = [x] * n
if 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()
# display.display(self.fig)
# display.clear_output(wait=True)如果还是没有显示的话（一般来说之前显示图没问题的话这一步是不用设置的，并且如果全部都设置了还是无法显示就要考虑matplotlib和d2l的冲突了），就在以下设置需要打开：【不同的版本可能没有pillow那个，无需理会全部勾选就ok】
成功结果如下所示：
注：本文参考d2l参考书中Zhang_Xuhui同学的评论，并且贴主进行了一定的修改和优化