Optim.adam
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Optim.adam has 8 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Mostly:rdf:type(3), rdfs:label(2), has learning rate(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
Rdfs:labelrdfs:label
Has Learning RatehasLearningRate
- 0.001[1]sourceall time · 473b8b12 Bc82 4e33 85d3 1090ae8915bb
Has Learning Rate ParameterhasLearningRateParameter
- 0.001[2]sourceall time · 1431835d Ed0f 4f5e A055 310bf86b145f
Is Constructor CallisConstructorCall
- true[2]sourceall time · 1431835d Ed0f 4f5e A055 310bf86b145f
Inbound mentions (10)
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.
passedToPassed to(2)
- Lr Argument
ex:lr_argument - Parameters Argument
ex:parameters_argument
assignedFromAssigned From(1)
- Optimizer
ex:optimizer
createdCreated(1)
- Optimizer
ex:optimizer
implementedAsImplemented As(1)
- Optimizer
ex:optimizer
valueValue(1)
- Optimizer Assignment
ex:optimizer_assignment
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.
References (4)
- custom
ctx:claims/beam/473b8b12-bc82-4e33-85d3-1090ae8915bb- full textbeam-chunktext/plain1 KB
doc:beam/473b8b12-bc82-4e33-85d3-1090ae8915bbShow excerpt
return x # Example usage: queries = [...] # List of queries labels = [...] # List of labels dataset = QueryDataset(queries, labels) data_loader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4) model = Optimizat…
- custom
ctx:claims/beam/1431835d-ed0f-4f5e-a055-310bf86b145f- full textbeam-chunktext/plain1 KB
doc:beam/1431835d-ed0f-4f5e-a055-310bf86b145fShow excerpt
def worker(data_loader): local_model = MyModel() local_optimizer = optim.Adam(local_model.parameters(), lr=0.001) update_model(local_model, local_optimizer, data_loader) return local_model.state_dict(), local_optimizer.state…
- custom
ctx:claims/beam/e23941de-32cc-40aa-8fa8-2ba2a21a03db- full textbeam-chunktext/plain1 KB
doc:beam/e23941de-32cc-40aa-8fa8-2ba2a21a03dbShow excerpt
optimizer = optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data_loader): model.train() for data, _ in data_loader: data = data.to(device) optimizer.zero_grad() …
- custom
ctx:claims/beam/1ca59683-ef7c-4511-a82b-ebdf3e48113e
See also
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