Dontopedia

Training Loop Code

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

Training Loop Code has 41 facts recorded in Dontopedia across 2 references, with 7 live disagreements.

41 facts·25 predicates·2 sources·7 in dispute

Mostly:sequence(5), defines variable(5), imports(3)

Maturity scale raw canonical shape-checked rule-derived certified

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.

affectsAffects(1)

describesDescribes(1)

explainsExplains(1)

providesProvides(1)

Other facts (41)

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.

41 facts
PredicateValueRef
SequenceForward Pass Step[1]
SequenceLoss Computation Step[1]
SequenceBackward Pass Step[1]
SequenceParameter Update Step[1]
SequenceGradient Zeroing Step[1]
Defines VariableInputs[1]
Defines VariableTargets[1]
Defines VariableOutputs[1]
Defines VariableLoss[1]
Defines VariableEpoch[1]
ImportsTorch[1]
ImportsTorch.nn[1]
ImportsTorch.optim[1]
InitializesModel[1]
InitializesOptimizer[1]
InitializesLoss Function[1]
Uses ModuleJson Module[2]
Uses ModuleLogging Module[2]
Uses ModuleOptimizer Module[2]
Rdf:typePython Code Snippet[1]
Rdf:typeCode Snippet[2]
Code DelimiterPython Fence Start[1]
Code DelimiterPython Fence End[1]
DefinesMy Model[1]
Contains Training Looptrue[1]
Training Iterations3000[1]
Includes Forward Passtrue[1]
Includes Loss Computationtrue[1]
Includes Backward Passtrue[1]
Includes Parameter Updatetrue[1]
Includes Gradient Zeroingtrue[1]
LanguagePython[1]
Inverse SequenceGradient Zeroing Step[1]
Uses FunctionRange[1]
Loop TypeFor Loop[1]
Learning Rate0.00001[1]
Model ArchitectureTwo Layer Mlp[1]
Contains FunctionTraining Loop[2]
Has StructureTry Except Block[2]
Is Written inPython[2]
Has DocumentationExplanation[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.

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ex:PythonCodeSnippet
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importsbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:torch.nn
importsbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:torch.optim
definesbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:MyModel
initializesbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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initializesbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:loss-function
containsTrainingLoopbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
true
trainingIterationsbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
3000
includesForwardPassbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
true
includesLossComputationbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
true
includesBackwardPassbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
true
includesParameterUpdatebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
true
includesGradientZeroingbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
true
languagebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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sequencebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:loss-computation-step
sequencebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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sequencebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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sequencebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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inverseSequencebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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definesVariablebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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definesVariablebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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definesVariablebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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codeDelimiterbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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codeDelimiterbeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
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definesVariablebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:epoch
loopTypebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:for-loop
learningRatebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
0.00001
modelArchitecturebeam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
ex:two-layer-mlp
typebeam/3773704e-4ce1-4051-be2f-36f352957c07
ex:CodeSnippet
containsFunctionbeam/3773704e-4ce1-4051-be2f-36f352957c07
ex:trainingLoop
hasStructurebeam/3773704e-4ce1-4051-be2f-36f352957c07
ex:tryExceptBlock
isWrittenInbeam/3773704e-4ce1-4051-be2f-36f352957c07
ex:Python
usesModulebeam/3773704e-4ce1-4051-be2f-36f352957c07
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hasDocumentationbeam/3773704e-4ce1-4051-be2f-36f352957c07
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References (2)

2 references
  1. ctx:claims/beam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/ded8141d-c7c0-46aa-b358-5e1e230d16f9
      Show excerpt
      [Turn 8428] User: I'm using PyTorch 2.1.3 for model training and have achieved 99.9% stability across 3,000 epochs. Here's my training loop: ```python import torch import torch.nn as nn import torch.optim as optim class MyModel(nn.Module):
  2. ctx:claims/beam/3773704e-4ce1-4051-be2f-36f352957c07
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3773704e-4ce1-4051-be2f-36f352957c07
      Show excerpt
      'learning_rate': optimizer.param_groups[0]['lr'] } log_json = json.dumps(log_entry) logging.info(log_json) except Exception as e: logging.error(f"Error during training: {str(e)}") ```

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