import torch from torch import nn import d2l net = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Flatten(), nn.Linear(9216, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 10))，RuntimeError: mat1 and mat2 shapes cannot be multiplied (2x4096 and 9216x4096)，如何调整参数

这个错误是由于 nn.Linear(9216, 4096) 和前面一些卷积层的输出形状不兼容，导致无法进行矩阵乘法运算。具体来说，nn.Linear(9216, 4096) 的输入形状是 (batch_size, 9216)，而前面一些卷积层的输出形状可能是 (batch_size, h, w, c)，其中 h, w, c 分别表示高度、宽度和通道数，这样的形状无法直接与 nn.Linear(9216, 4096) 进行矩阵乘法运算。

要解决这个问题，你可以在 nn.Linear(9216, 4096) 之前添加一层 nn.Flatten()，将前面卷积层的输出形状展平成二维形状。具体来说，你可以将代码修改为如下所示：

import torch 
from torch import nn 
import d2l 

net = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1), nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(),
    nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(),
    nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Flatten(),
    nn.Linear(9216, 4096), nn.ReLU(),
    nn.Dropout(p=0.5),
    nn.Linear(4096, 4096), nn.ReLU(),
    nn.Dropout(p=0.5),
    nn.Linear(4096, 10)
)

希望这可以帮助你解决问题！