错误消息表明输入中的通道数与第一个卷积层的权重之间不匹配。
如果您的输入数据形状为[3，784]（在图像中），则意味着您将整个图像视为三个通道中每个通道都有784个元素的平面向量。在这种情况下，您必须重新塑造输入数据，以获得具有3个通道的2D图像的适当形状。这里是解决您问题的更新版本

import torch
import torch.nn as nn

class Conv(nn.Module):
    def __init__(self):
        super(Conv, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=0, stride=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=0, stride=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Linear(in_features=32 * 4 * 4, out_features=128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(in_features=128, out_features=64)
        self.relu4 = nn.ReLU()
        self.fc3 = nn.Linear(in_features=64, out_features=7)
        self.logSoftmax = nn.LogSoftmax(dim=1)

    def forward(self, x):
        # Reshape the input to [batch_size, channels, height, width]
        x = x.view(-1, 3, 28, 28)
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        x = self.relu4(x)
        x = self.fc3(x)
        out = self.logSoftmax(x)
        return out

model = Conv()

input_data = torch.randn((3,784))
print(input_data.shape)

output = model(input_data)
print(output)

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