Hello, i am getting the error when i am trying to train a CNN model using custom dataset.
here is my model:
class CNNMnist(nn.Module):
    def __init__(self, args):
        super(CNNMnist, self).__init__()
        self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(720, 50)
        self.fc2 = nn.Linear(128, args.num_classes)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
        print(x.shape)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)

        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

i reshape my images while loading with transforms.Resize(256),transforms.CenterCrop(224)
i initialize my model:
global_model = CNNMnist(args=args)

for my custom dataset, the number of class is 6 and the number of channel is 3.
The error is thrown at  x = F.relu(self.fc1(x)).
After spending hours trying to find out the problem, my guess is the values used in the following lines are not correct: self.fc1 = nn.Linear(720, 50)         self.fc2 = nn.Linear(128, args.num_classes)
Since i actually have no idea how these values are calculated, I would really appreciate some help on this.
Thank you