Home / Deep learning / Weight tensor
Drawing of rank-3 network weights
Rank-3 weight tensor \(W\):
digraph {
<<dot-theme>>
rankdir = LR
splines = line
ranksep = 1
subgraph cluster_l1 {
style = dotted
l1d2 [label = "⋮" shape = none]
l1n3 [shape = circle width = 0.75 label = ""]
l1n2 [shape = circle width = 0.75 label = ""]
l1n1 [shape = circle width = 0.75 label = ""]
l1d1 [label = "⋮" shape = none]
l1 [label = <layer <i>i</i>−1<br/>neurons> shape = none]
}
subgraph cluster_l2 {
style = dotted
l2d2 [label = "⋮" shape = none]
l2n3 [shape = circle width = 0.75 label = ""]
l2n2 [shape = circle width = 0.75 label = ""]
l2n1 [shape = circle width = 0.75 label = ""]
l2d1 [label = "⋮" shape = none]
l2 [label = <layer <i>i</i><br/>neurons> shape = none]
}
subgraph cluster_l3 {
style = dotted
l3d2 [label = "⋮" shape = none]
l3n3 [shape = circle width = 0.75 label = ""]
l3n2 [shape = circle width = 0.75 label = ""]
l3n1 [shape = circle width = 0.75 label = ""]
l3d1 [label = "⋮" shape = none]
l3 [label = <layer <i>i</i>+1<br/>neurons> shape = none]
}
l1d1 -> l2d1 [style = invis]
l2d1 -> l3d1 [style = invis]
l1n1:e -> l2n1:w [dir = none]
l1n2:e -> l2n2:w [dir = none]
l1n3:e -> l2n3:w [dir = none]
l2n1:e -> l3n1:w [dir = none]
l2n2:e -> l3n2:w [dir = none]
l2n3:e -> l3n3:w [dir = none]
l1d2 -> l2d2 [style = invis]
l2d2 -> l3d2 [style = invis]
l1 -> l2 [constraint = false label = "forward"]
l2 -> l3 [constraint = false label = "forward"]
l1n1:e -> l2n2:w [constraint = false dir = none]
l1n1:e -> l2n3:w [constraint = false dir = none]
l1n2:e -> l2n1:w [constraint = false dir = none]
l1n2:e -> l2n3:w [constraint = false dir = none]
l1n3:e -> l2n1:w [constraint = false dir = none]
l1n3:e -> l2n2:w [constraint = false dir = none]
l2n1:e -> l3n2:w [constraint = false dir = none]
l2n1:e -> l3n3:w [constraint = false dir = none]
l2n2:e -> l3n1:w [constraint = false dir = none]
l2n2:e -> l3n3:w [constraint = false dir = none]
l2n3:e -> l3n1:w [constraint = false dir = none]
l2n3:e -> l3n2:w [constraint = false dir = none]
}