在最新版本1.0.3,上 遇到d2l.torch库里面缺失train_ch3函数，下面是个人写的替代补充函数可以完全平替。
所有函数都放在util.py文件中
import torch
.nn
from d2l
import torch
as d2l
from IPython
import display
class Accumulator:
"""
在n个变量上累加
"""
def __init__(self
, n
):
self
.data
= [0.0] * n
# 创建一个长度为 n 的列表，初始化所有元素为0.0。
def add(self
, *args
): # 累加
self
.data
= [a
+ float(b
) for a
, b
in zip(self
.data
, args
)]
def reset(self
): # 重置累加器的状态，将所有元素重置为0.0
self
.data
= [0.0] * len(self
.data
)
def __getitem__(self
, idx
): # 获取所有数据
return self
.data
[idx
]
def accuracy(y_hat
, y
):
"""
计算正确的数量
:param y_hat:
:param y:
:return:
"""
if len(y_hat
.shape
) > 1 and y_hat
.shape
[1] > 1:
y_hat
= y_hat
.argmax
(axis
=1) # 在每行中找到最大值的索引，以确定每个样本的预测类别
cmp = y_hat
.type(y
.dtype
) == y
return float(cmp.type(y
.dtype
).sum())
def evaluate_accuracy(net
, data_iter
):
"""
计算指定数据集的精度
:param net:
:param data_iter:
:return:
"""
if isinstance(net
, torch
.nn
.Module
):
net
.eval() # 通常会关闭一些在训练时启用的行为
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]
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)):
# 增量的绘制多条线
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, fmts
def add(self
, x
, y
):
"""
向图表中添加多个数据点
:param x:
:param y:
:return:
"""
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)
def train_epoch_ch3(net
, train_iter
, loss
, updater
):
"""
训练模型一轮
:param net:是要训练的神经网络模型
:param train_iter:是训练数据的数据迭代器，用于遍历训练数据集
:param loss:是用于计算损失的损失函数
:param updater:是用于更新模型参数的优化器
:return:
"""
if isinstance(net
, torch
.nn
.Module
): # 用于检查一个对象是否属于指定的类（或类的子类）或数据类型。
net
.train
()
# 训练损失总和， 训练准确总和， 样本数
metric
= 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
): # 用于检查一个对象是否属于指定的类（或类的子类）或数据类型。
# 使用pytorch内置的优化器和损失函数
updater
.zero_grad
()
l
.mean
().backward
() # 方法用于计算损失的平均值
updater
.step
()
else:
# 使用定制（自定义）的优化器和损失函数
l
.sum().backward
()
updater
(X
.shape
())
metric
.add
(float(l
.sum()), accuracy
(y_hat
, y
), y
.numel
())
# 返回训练损失和训练精度
return metric
[0] / metric
[2], metric
[1] / metric
[2]
def train_ch3(net
, train_iter
, test_iter
, loss
, num_epochs
, updater
):
"""
训练模型（）
:param net:
:param train_iter:
:param test_iter:
:param loss:
:param num_epochs:
:param updater:
:return:
"""
animator
= Animator
(xlabel
='epoch', xlim
=[1, num_epochs
], ylim
=[0.3, 0.9],
legend
=['train loss', 'train acc', 'test acc'])
for epoch
in range(num_epochs
):
trans_metrics
= train_epoch_ch3
(net
, train_iter
, loss
, updater
)
test_acc
= evaluate_accuracy
(net
, test_iter
)
animator
.add
(epoch
+ 1, trans_metrics
+ (test_acc
,))
train_loss
, train_acc
= trans_metrics
print(trans_metrics
)
def predict_ch3(net
, test_iter
, n
=6):
"""
进行预测
:param net:
:param test_iter:
:param n:
:return:
"""
global X
, y
for X
, y
in test_iter
:
break
trues
= d2l
.get_fashion_mnist_labels
(y
)
preds
= d2l
.get_fashion_mnist_labels
(net
(X
).argmax
(axis
=1))
titles
= [true
+ "\n" + pred
for true
, pred
in zip(trues
, preds
)]
d2l
.show_images
(
X
[0:n
].reshape
((n
, 28, 28)), 1, n
, titles
=titles
[0:n
]
)
d2l
.plt
.show
()
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直接调用即可
找个位置放就行
