Thanks for making this repository, really useful :)
Let me start with the questions:
Is this network specifically done for 28x28 images or would it work for other sizes? The same goes for the number of channels, if self.conv1 = nn.Conv2d(in_channels=3, out_channels=256, kernel_size=9, stride=1) is used instead, will it work for images with 3 channels or is it necessary to make other changes?
In self.digit_capsules = CapsuleLayer(num_capsules=NUM_CLASSES, num_route_nodes=32 * 6 * 6, in_channels=8, out_channels=16), why did you choose 32x6x6 num_route_nodes? I am trying this code with different data and stuck in this step as the image dimensions are different and idk how they relate with this hyperparameter.
By increasing num_iterations will the model's performance increase as well?
Thanks for making this repository, really useful :)
Let me start with the questions:
Is this network specifically done for 28x28 images or would it work for other sizes? The same goes for the number of channels, if
self.conv1 = nn.Conv2d(in_channels=3, out_channels=256, kernel_size=9, stride=1)is used instead, will it work for images with 3 channels or is it necessary to make other changes?In
self.digit_capsules = CapsuleLayer(num_capsules=NUM_CLASSES, num_route_nodes=32 * 6 * 6, in_channels=8, out_channels=16), why did you choose 32x6x6 num_route_nodes? I am trying this code with different data and stuck in this step as the image dimensions are different and idk how they relate with this hyperparameter.By increasing
num_iterationswill the model's performance increase as well?