Transposed convolution TensorFlow padding for FCN style networks
No, it's your decision how you calculate the kernel-map in every single convolutional layer. It's a matter of designing your model.
I am implementing some variants of FCN for Segmentation. In particular, I have implemented a U-net architecture. Within the architecture, I am applying valid convolution with a 3x3 kernel and then I apply transposed convolution for upsampling with a 2x2 kernel and stride of 2. 
My question is, if using valid or same padding for the convolution, does this determine whether we use valid or same padding for the transposed convolution?
Currently I use valid padding for convolution and same padding for transposed convolution.