def __init__(self, input_size, classes, routings):
super(CapsuleNet, self).__init__()
self.input_size = input_size
self.classes = classes
self.routings = routings
# Layer 1: Just a conventional Conv2D layer
self.conv1 = nn.Conv2d(input_size[0], 256, kernel_size=9, stride=1, padding=0)
# Layer 2: Conv2D layer with `squash` activation, then reshape to [None, num_caps, dim_caps]
self.primarycaps = PrimaryCapsule(256, 256, 8, kernel_size=9, stride=2, padding=0)
# Layer 3: Capsule layer. Routing algorithm works here.
self.digitcaps = DenseCapsule(in_num_caps=32*6*6, in_dim_caps=8,
out_num_caps=classes, out_dim_caps=16, routings=routings)
# Decoder network.
self.decoder = nn.Sequential(
nn.Linear(16*classes, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, input_size[0] * input_size[1] * input_size[2]),
nn.Sigmoid()
)
self.relu = nn.ReLU()
def __init__(self, input_size, classes, routings):
super(CapsuleNet, self).__init__()
self.input_size = input_size
self.classes = classes
self.routings = routings
# Layer 1: Just a conventional Conv2D layer
self.conv1 = nn.Conv2d(input_size[0],
256,
kernel_size=9,
stride=1,
padding=0)
# Layer 2: Conv2D layer with `squash` activation, then reshape to [None, num_caps, dim_caps]
self.primarycaps = PrimaryCapsule(256,
256,
8,
kernel_size=9,
stride=2,
padding=0)
# Layer 3: Capsule layer. Routing algorithm works here.
self.digitcaps = DenseCapsule(in_num_caps=32 * 6 * 6,
in_dim_caps=8,
out_num_caps=classes,
out_dim_caps=16,
routings=routings)
self.relu = nn.ReLU()