解决ValueError: Expected input batch_size () to match target batch_size ().问题

一般这种问题是由于输入图像的大小不匹配导致，

class Network(nn.Module):
    def __init__(self, height_size, width_size, num_classes):
        super(Network, self).__init__()
        self.height_size = height_size
        self.width_size = width_size
        self.conv2d_1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=(3, 3), padding=2)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2d_2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), padding=2)
        #self.fc_1 = nn.Linear(16 * self.height_size // 4*self.width_size // 4, 1024)
        self.fc_1 = nn.Linear(16 * 9 * 9, 1024)
        self.fc_2 = nn.Linear(1024, num_classes)

    def forward(self, input):
        x = self.conv2d_1(input)
        x = F.relu(x)
        x = self.pool(x)
        x = self.conv2d_2(x)
        x = F.relu(x)
        x = self.pool(x)
        #x = x.view(-1, 16*self.height_size//4*self.width_size//4)
        #print(x.shape)
        #torch.Tensor(64, 16, 9, 9)
        x = x.view(-1, 16 * 9 * 9) 
        x = self.fc_1(x)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.fc_2(x)
        output = F.log_softmax(x,dim=1)
        return output

作者这里是由于修改了kernal_size导致卷积之后的图像大小与修改之前的图像大小不一致报错。
可以通过print一下view之前的输入形状并根据结果来修改线性层的输入大小，记得线性层初始化时的输入大小也要修改。