Dontopedia

val_inputs

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

val_inputs has 4 facts recorded in Dontopedia across 2 references.

4 facts·3 predicates·2 sources
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (5)

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.

containsContains(3)

pairsPairs(1)

producesProduces(1)

Other facts (3)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

3 facts
PredicateValueRef
Is Split Result ofInputs Tensor[1]
Rdf:typeTorch Tensor[2]
Derived FromInputs[2]

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.

4 undated
—
isSplitResultOfbeam/56ec773d-331c-4612-b327-318a1a96426f
ex:inputs-tensor
—
typebeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
ex:TorchTensor
—
labelbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
val_inputs
—
derivedFrombeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
ex:inputs

References (2)

2 references
  1. [1]56ec773d 331c 4612 B327 318a1a96426f1 fact
    ctx: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) …
  2. [2]2739fb08 C4fc 4bb6 B143 E05bc2133eae3 facts
    ctx:claims/beam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
      Show excerpt
      ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error class MyMod…

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