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问题所属任务编号（Task 01-05）
4.2.5. 训练
运行环境（操作系统版本、Python 版本）/ 非程序问题
windows 11,Python 3.10.7
完整的报错信息（截图或复制均可）/ 问题详细描述
代码：
import torch
from torch import nn
from d2l import torch as d2l
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
net = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))
'''利用PyTorch中的 nn 库定义了一个简单的神经网络模型 net，
它由两层组成：Flatten 层将输入的 28 x 28 的图像展平成一维的张量，
然后接一个全连接层 Linear，输入维度为 784（展平后的图像大小），
输出维度为 10（表示分类的类别数量）'''
def init_weights(m):
if type(m) == nn.Linear:
nn.init.normal_(m.weight, std=0.01)
net.apply(init_weights);
loss = nn.CrossEntropyLoss(reduction='none')
'''nn.CrossEntropyLoss是一个PyTorch中用于计算交叉熵损失的函数，
其中reduction='none'表示不对每个batch的损失进行平均处理和累加，
而是返回一个张量，该张量包含每个样本的损失值'''
trainer = torch.optim.SGD(net.parameters(), lr=0.1)
'''使用随机梯度下降算法（SGD）来更新神经网络中的所有参数（即net.parameters()），学习率为0.1'''
num_epochs = 10
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
**
报错：**
RuntimeError Traceback (most recent call last)
Cell In [32], line 3
1 num_epochs, lr = 10, 0.1
2 updater = torch.optim.SGD(params, lr=lr)
----> 3 d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\d2l\torch.py:318, in train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)
315 animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],
316 legend=['train loss', 'train acc', 'test acc'])
317 for epoch in range(num_epochs):
--> 318 train_metrics = train_epoch_ch3(net, train_iter, loss, updater)
319 test_acc = evaluate_accuracy(net, test_iter)
320 animator.add(epoch + 1, train_metrics + (test_acc,))
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\d2l\torch.py:257, in train_epoch_ch3(net, train_iter, loss, updater)
254 if isinstance(updater, torch.optim.Optimizer):
255 # Using PyTorch in-built optimizer & loss criterion
256 updater.zero_grad()
--> 257 l.backward()
258 updater.step()
259 metric.add(float(l) * len(y), accuracy(y_hat, y),
260 y.size().numel())
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch_tensor.py:396, in Tensor.backward(self, gradient, retain_graph, create_graph, inputs)
387 if has_torch_function_unary(self):
388 return handle_torch_function(
389 Tensor.backward,
390 (self,),
(...)
394 create_graph=create_graph,
395 inputs=inputs)
--> 396 torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\autograd_init_.py:166, in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
162 inputs = (inputs,) if isinstance(inputs, torch.Tensor) else
163 tuple(inputs) if inputs is not None else tuple()
165 grad_tensors_ = tensor_or_tensors_to_tuple(grad_tensors, len(tensors))
--> 166 grad_tensors = make_grads(tensors, grad_tensors, is_grads_batched=False)
167 if retain_graph is None:
168 retain_graph = create_graph
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\autograd_init_.py:67, in _make_grads(outputs, grads, is_grads_batched)
65 if out.requires_grad:
66 if out.numel() != 1:
---> 67 raise RuntimeError("grad can be implicitly created only for scalar outputs")
68 new_grads.append(torch.ones_like(out, memory_format=torch.preserve_format))
69 else:
RuntimeError: grad can be implicitly created only for scalar outputs
描述你期望看到的结果
怎么解决啊，将loss函数的reduction参数设置为"mean"或"sum"也不行
好奇怪，在我的环境里面可以平稳的运行，你要不退了重新再写一边试试？
或者是用这个命令，验证一下net可不可行
devices = d2l.try_all_gpus()
net.to(devices[0]);
X = torch.rand(size=(32, 3, 224, 224)).to(devices[0])
for layer in net:
X = layer(X)
print(layer.class.name,'output shape:\t', X.shape)