RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x22 and 480x128)
时间: 2024-01-22 10:36:21 浏览: 236
这个错误是因为在计算两个矩阵相乘时,矩阵的维度不兼容。根据你的描述,输入数据的形状是32x3000x80,而模型中的self.fc1层的输入维度是32x15。
要解决这个问题,你需要调整模型的结构以适应输入数据的形状。一种可能的方法是在self.fc1层之前添加一个池化层,将数据的维度从32x3000x80降低到32x15。下面是修改后的代码:
```python
import torch
import torch.nn as nn
class EEGNet(nn.Module):
def __init__(self, num_channels, num_classes):
super(EEGNet, self).__init__()
self.conv1 = nn.Conv1d(num_channels, 16, kernel_size=64, stride=4, padding=32)
self.bn1 = nn.BatchNorm1d(16)
self.pool1 = nn.MaxPool1d(kernel_size=8, stride=4)
self.conv2 = nn.Conv1d(16, 32, kernel_size=32, stride=2, padding=16)
self.bn2 = nn.BatchNorm1d(32)
self.pool2 = nn.MaxPool1d(kernel_size=8, stride=4)
# 添加池化层,将数据的维度从32x3000x80降低到32x15
self.pool3 = nn.MaxPool1d(kernel_size=24, stride=24)
self.fc1 = nn.Linear(32 * 15, 128)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = self.pool1(torch.relu(self.bn1(self.conv1(x))))
x = self.pool2(torch.relu(self.bn2(self.conv2(x))))
# 使用池化层降低数据维度
x = self.pool3(x)
x = x.view(-1, 32 * 15)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 创建模型实例
num_channels = 32
num_classes = 2
model = EEGNet(num_channels, num_classes)
# 其余代码不变...
```
通过添加池化层,可以将输入数据的维度从32x3000x80降低到32x15,使得矩阵相乘的维度匹配。这样就可以避免运行时错误。请注意,上述代码中的池化层参数(kernel_size和stride)需要根据你的数据和模型进行适当调整。
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