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Nn.linear

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Nn.linear has 9 facts recorded in Dontopedia across 6 references, with 3 live disagreements.

9 facts·5 predicates·6 sources·3 in dispute

Mostly:has parameters(3), rdf:type(2), has parameter(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Has Parameterin disputehasParameter

  • in_features[2]all time · Cb8cd140 2b8c 41c2 8160 68d7bc0c4c91
  • out_features[2]all time · Cb8cd140 2b8c 41c2 8160 68d7bc0c4c91

Has Parametersin disputehasParameters

  • 64[3]sourceall time · 9344edde D6af 464f 9e96 394ef09895b9
  • 1[3]sourceall time · 9344edde D6af 464f 9e96 394ef09895b9
  • 160[3]sourceall time · 9344edde D6af 464f 9e96 394ef09895b9

Framework ComponentframeworkComponent

  • Py Torch[1]all time · F44978a0 564c 4f7b Bb2b Fc44244862cf

Implementsimplements

Inbound mentions (27)

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.

rdf:typeRdf:type(8)

isInstanceIs Instance(6)

isInstanceOfIs Instance of(4)

instantiationOfInstantiation of(2)

isDefinedAsIs Defined As(2)

nn:LinearNn:linear(2)

constructorConstructor(1)

containsContains(1)

providesProvides(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.

9 undated
—
frameworkComponentbeam/f44978a0-564c-4f7b-bb2b-fc44244862cf
ex:PyTorch
—
hasParameterbeam/cb8cd140-2b8c-41c2-8160-68d7bc0c4c91
in_features
—
hasParameterbeam/cb8cd140-2b8c-41c2-8160-68d7bc0c4c91
out_features
—
hasParametersbeam/9344edde-d6af-464f-9e96-394ef09895b9
64
—
hasParametersbeam/9344edde-d6af-464f-9e96-394ef09895b9
1
—
hasParametersbeam/9344edde-d6af-464f-9e96-394ef09895b9
160
—
implementsbeam/56ec773d-331c-4612-b327-318a1a96426f
ex:fully-connected-layer
—
typebeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:LinearLayerClass
—
typebeam/4b0fb0ca-8535-46e3-955c-5f7eb8b91c01
ex:PyTorchClass

References (6)

6 references
  1. [1]beam-chunk1 fact
    customctx:claims/beam/f44978a0-564c-4f7b-bb2b-fc44244862cf
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f44978a0-564c-4f7b-bb2b-fc44244862cf
      Show excerpt
      - Applies PCA to reduce the dimensionality of the vectors. - Sends the processed vectors to another queue. 3. **Vector Storage Service**: - Consumes processed vectors from the queue. - Stores the processed vectors to a specifie…
  2. [2]Cb8cd140 2b8c 41c2 8160 68d7bc0c4c912 facts
    customctx:claims/beam/cb8cd140-2b8c-41c2-8160-68d7bc0c4c91
  3. [3]beam-chunk3 facts
    customctx:claims/beam/9344edde-d6af-464f-9e96-394ef09895b9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9344edde-d6af-464f-9e96-394ef09895b9
      Show excerpt
      # Concatenate existing inputs with user behavior data combined_inputs = torch.cat([inputs, user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) -…
  4. [4]beam-chunk1 fact
    customctx:claims/beam/56ec773d-331c-4612-b327-318a1a96426f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/56ec773d-331c-4612-b327-318a1a96426f
      Show excerpt
      ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # Example data preparation inputs = torch.randn(3000, 128) # Example input data labels = torch.randn(3000, 1) …
  5. [5]beam-chunk1 fact
    customctx:claims/beam/23009db1-c526-4b01-963c-b2c7b2736c5b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/23009db1-c526-4b01-963c-b2c7b2736c5b
      Show excerpt
      combined_inputs = torch.cat([inputs, combined_user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) - train_size train_combined_inputs, val_combi…
  6. [6]4b0fb0ca 8535 46e3 955c 5f7eb8b91c011 fact
    customctx:claims/beam/4b0fb0ca-8535-46e3-955c-5f7eb8b91c01

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