Dontopedia

DebugModel

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

DebugModel has 19 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

19 facts·11 predicates·2 sources·2 in dispute

Mostly:has part(4), rdf:type(3), input dimension(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (8)

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.

partOfPart of(4)

initializedWithInitialized With(1)

isBaseForIs Base for(1)

precedesPrecedes(1)

structuralOrderStructural Order(1)

Other facts (16)

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.

16 facts
PredicateValueRef
Has PartFc1[1]
Has PartFc2[1]
Has PartFc1 Layer[2]
Has PartFc2 Layer[2]
Rdf:typeNn Module[1]
Rdf:typeClass[1]
Rdf:typeNeural Network Model[2]
Input Dimension512[1]
Output Dimension10[1]
Typemodel class[2]
Inherits Fromnn.Module[2]
PrecedesExample Usage[2]
Neural Network Architecture512-128-10[2]
Number of Layers2[2]
Designed forclassification[2]
Inherits FromNn Module[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.

typebeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
ex:nn-Module
labelbeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
DebugModel
typebeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
ex:Class
hasPartbeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
ex:fc1
hasPartbeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
ex:fc2
inputDimensionbeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
512
outputDimensionbeam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
10
namebeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
DebugModel
typebeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
model class
inherits-frombeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
nn.Module
typebeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
ex:NeuralNetworkModel
labelbeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
DebugModel
hasPartbeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
ex:fc1-layer
hasPartbeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
ex:fc2-layer
precedesbeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
ex:example-usage
neuralNetworkArchitecturebeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
512-128-10
numberOfLayersbeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
2
designedForbeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
classification
inheritsFrombeam/16ad261b-9fcf-4975-8708-5450c6d4ee02
ex:nn-module

References (2)

2 references
  1. ctx:claims/beam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3273ae1c-32c6-4028-9a0a-b07bb3d1326a
      Show excerpt
      level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("debug_training.log"), logging.StreamHandler() ] ) # Define a custom dataset class for our queries class
  2. ctx:claims/beam/16ad261b-9fcf-4975-8708-5450c6d4ee02
    • full textbeam-chunk
      text/plain1 KBdoc:beam/16ad261b-9fcf-4975-8708-5450c6d4ee02
      Show excerpt
      import json # Check if a GPU is available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(

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