- Sort Score
- Result 10 results
- Languages All
Results 1 - 2 of 2 for unweighted (0.1 sec)
-
pkg/config/validation/validation_test.go
}}, }, valid: true}, {name: "no destination", route: &networking.HTTPRoute{ Route: []*networking.HTTPRouteDestination{{ Destination: nil, }}, }, valid: false}, {name: "weighted", route: &networking.HTTPRoute{ Route: []*networking.HTTPRouteDestination{{ Destination: &networking.Destination{Host: "foo.baz.south"}, Weight: 25, }, {
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Tue Apr 30 03:11:45 UTC 2024 - 196K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
let description = [{ The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `depth_radius`. In detail, sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) output = input / (bias + alpha * sqr_sum) ** beta
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0)