Machine learning 错误错误匹配m1和m2，我的conv层计算的错误是什么？

我有以下体系结构：

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()

        
        # first convolutional layer
        self.conv1 = torch.nn.Conv2d(1,32,5) # (32,220,220) output tensor # (W-F)/S + 1 = (224-5)/1 + 1 = 220
        
        # first Max-pooling layer
        self.pool1 = torch.nn.MaxPool2d(2,2) # (32,110,110) output tensor
        
        # second convolutional layer
        self.conv2 = torch.nn.Conv2d(32,64,5) # (64,106,106) output tensor # (W-F)/S + 1 = (110-5)/1 + 1 = 106
        
        # second Max-pooling layer
        self.pool2 = torch.nn.MaxPool2d(2,2) # (64,53,53) output tensor
        
        # Fully connected layer
        self.fc1 = torch.nn.Linear(64*53*53, 1000)   
        self.fc2 = torch.nn.Linear(1000, 500)       
        self.fc3 = torch.nn.Linear(500, 136)        
        self.drop1 = nn.Dropout(p=0.4)

图像的尺寸为224x224。我得到了一个错误：

运行时错误：大小不匹配，m1:[10 x 173056]，m2:[179776 x 1000]at /opt/conda/conda bld/pytorch_1524584710464/work/aten/src/TH/generic/THTensorMath.c:2033

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我看不出我的错误在哪里，有人能帮我吗？

体系结构很好，我将输入图像裁剪为220 x 200维，这显然是不正确的