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Val Loader

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

Val Loader has 18 facts recorded in Dontopedia across 5 references, with 1 live disagreement.

18 facts·17 predicates·5 sources·1 in dispute

Mostly:rdf:type(2), has shuffle enabled(1), sets(1)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Has Shuffle EnabledhasShuffleEnabled

  • false[3]sourceall time · 23009db1 C526 4b01 963c B2c7b2736c5b

Setssets

  • Shuffle[3]sourceall time · 23009db1 C526 4b01 963c B2c7b2736c5b

Uses DatasetusesDataset

Has ShufflehasShuffle

  • false[3]sourceall time · 23009db1 C526 4b01 963c B2c7b2736c5b

Uses Batch SizeusesBatchSize

  • 64[3]sourceall time · 23009db1 C526 4b01 963c B2c7b2736c5b

Is Created UsingisCreatedUsing

Shuffleshuffle

  • false[4]sourceall time · 212294fd 6444 48ea 90be 0ccd48cb9cc3

Inverse ofinverseOf

Has Batch SizehasBatchSize

  • 64[2]sourceall time · 9344edde D6af 464f 9e96 394ef09895b9

Batch Size FormattingbatchSizeFormatting

Shuffle EnabledshuffleEnabled

  • false[2]sourceall time · 9344edde D6af 464f 9e96 394ef09895b9

Inbound mentions (12)

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.

inverseOfInverse of(3)

differsBetweenDiffers Between(1)

differsFromDiffers From(1)

includesIncludes(1)

isConfiguredForIs Configured for(1)

isUsedByIs Used by(1)

sharedByShared by(1)

sharesBatchSizeWithShares Batch Size With(1)

used_byUsed by(1)

usedByUsed by(1)

Other facts (5)

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.

5 facts
PredicateValueRef
Created byData Loader[2]
Is VariableData Loader[2]
PreservesData Order[1]
Batches10[1]
EnablesBatch Validation[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.

batchesbeam/56ec773d-331c-4612-b327-318a1a96426f
10
batchSizeFormattingbeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:space_before_64
createdBybeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:DataLoader
enablesbeam/56ec773d-331c-4612-b327-318a1a96426f
ex:batch_validation
hasBatchSizebeam/9344edde-d6af-464f-9e96-394ef09895b9
64
hasShufflebeam/23009db1-c526-4b01-963c-b2c7b2736c5b
false
hasShuffleEnabledbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
false
inverseOfbeam/212294fd-6444-48ea-90be-0ccd48cb9cc3
ex:val_dataset
isCreatedUsingbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:DataLoader
isVariablebeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:DataLoader
preservesbeam/56ec773d-331c-4612-b327-318a1a96426f
ex:data-order
typebeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:DataLoader
typebeam/25d090a4-1559-4fd2-a3aa-d752e7199607
ex:DataLoader
setsbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:shuffle
shufflebeam/212294fd-6444-48ea-90be-0ccd48cb9cc3
false
shuffleEnabledbeam/9344edde-d6af-464f-9e96-394ef09895b9
false
usesBatchSizebeam/23009db1-c526-4b01-963c-b2c7b2736c5b
64
usesDatasetbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:val_dataset

References (5)

5 references
  1. [1]beam-chunk3 facts
    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)
  2. [2]beam-chunk5 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) -
  3. [3]beam-chunk7 facts
    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
  4. [4]beam-chunk2 facts
    customctx:claims/beam/212294fd-6444-48ea-90be-0ccd48cb9cc3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/212294fd-6444-48ea-90be-0ccd48cb9cc3
      Show excerpt
      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) - train_size train_combined_inputs, val_combined_input
  5. [5]beam-chunk1 fact
    customctx:claims/beam/25d090a4-1559-4fd2-a3aa-d752e7199607
    • full textbeam-chunk
      text/plain1 KBdoc:beam/25d090a4-1559-4fd2-a3aa-d752e7199607
      Show excerpt
      train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) # Early stopping parameters best_val_loss = float('inf') patience = 5 counter = 0 # Train the model f

See also

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