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Leaky Relu Activation

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Leaky Relu Activation has 8 facts recorded in Dontopedia across 1 reference.

8 facts·8 predicates·1 sources

Mostly:feeds into(1), has part(1), precedes(1)

Maturity scale raw canonical shape-checked rule-derived certified

Feeds IntofeedsInto

  • Layer 2[1]all time · B37d3f65 B489 4a88 Aa05 62e2c014851e

Has ParthasPart

Precedesprecedes

  • Layer 2[1]all time · B37d3f65 B489 4a88 Aa05 62e2c014851e

Is Part ofisPartOf

Has Parameter ValuehasParameterValue

  • 0.1[1]sourceall time · B37d3f65 B489 4a88 Aa05 62e2c014851e

Has ParameterhasParameter

  • negative_slope[1]sourceall time · B37d3f65 B489 4a88 Aa05 62e2c014851e

Rdfs:labelrdfs:label

  • LeakyReLU[1]all time · B37d3f65 B489 4a88 Aa05 62e2c014851e

Rdf:typerdf:type

Inbound mentions (4)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

consistsOfConsists of(1)

feedsIntoFeeds Into(1)

followedByFollowed by(1)

precedesPrecedes(1)

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

8 undated
—
feedsIntobeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:layer-2
—
hasParameterbeam/b37d3f65-b489-4a88-aa05-62e2c014851e
negative_slope
—
hasParameterValuebeam/b37d3f65-b489-4a88-aa05-62e2c014851e
0.1
—
hasPartbeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:pytorch-model
—
isPartOfbeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:pytorch-model
—
precedesbeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:layer-2
—
labelbeam/b37d3f65-b489-4a88-aa05-62e2c014851e
LeakyReLU
—
typebeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:ActivationFunction

References (1)

1 references
  1. [1]beam-chunk8 facts
    customctx:claims/beam/b37d3f65-b489-4a88-aa05-62e2c014851e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b37d3f65-b489-4a88-aa05-62e2c014851e
      Show excerpt
      import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from torch.cuda.amp import GradScaler, autocast # Initialize PyTorch model model = nn.Sequential( nn.Linear(128, 128)…

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