Hey Folks,
My problem is this.
Currently I have an almost arbitrarily large dataset with which I want to train a common network architecture e.g. Resnet18.
My problem now is that this data set does not consist of images but only of single variables. So I have to get from a 10x1 to 3x224x224 somehow.
should i create a first new Layer like this:
class Resnet_Input(nn.Module):
    def __init__(self):
        super(Resnet_Input, self).__init__()
        self.fc1 = nn.Linear(10, 224*224*3)
    def forward(self, data):
        x = F.relu(self.fc1(data))
        x = x.view(-1, 3, 224, 224)
        return x

and stack it with nn.Sequential( Resnet_Input, ResNet18), should i modify the first layer or should i add a complete new “Residual Block”? What du you think is the best way? Do you have better ideas?