The Pytorch docs give the following definition of a 2d convolutional transpose layer:
torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1)
Tensorflow’s conv2d_transpose layer instead uses filter, which is a 4d Tensor of [height, width, output_channels, in_channels]. I’ve seen it used in networks with structures like the following:
4 × 4 × 1024 → 8 × 8 × 1024 → 16 × 16 × 512 → 32 × 32 × 256 → 64 × 64 × 128 → 128 × 128 × 64 → 256 × 256 × 3 (used for the generator of a DCGAN).
Can I use kernel_size as a substitute for filter (e.g. nn.ConvTranspose2d(256,128,64,1) in my model ?
Thanks in advance!