optimizer_state_dict
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
optimizer_state_dict has 6 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound 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.
containsContains(3)
- Checkpoint
ex:checkpoint - Checkpoint File Format
ex:checkpoint-file-format - Tuple of Dictionaries
ex:Tuple of dictionaries
assignsAssigns(1)
- Model Load Statement
ex:model-load-statement
consistsOfConsists of(1)
- Model and Optimizer State Dicts
ex:model-and-optimizer-state-dicts
hasStateDictHas State Dict(1)
- Optimizer
ex:optimizer
loadStateDictLoad State Dict(1)
- Optimizer
ex:optimizer
requiresRequires(1)
- Optimizer.load State Dict
ex:optimizer.load_state_dict
returnsReturns(1)
- Worker Function
ex:worker-function
serializesSerializes(1)
- Save Operation
ex:save-operation
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Optimizer State | [2] |
| Rdf:type | Optimizer State | [3] |
| Rdf:type | Dictionary | [4] |
| Loaded Into | Optimizer | [1] |
| Stored in | Future | [4] |
Timeline
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References (4)
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…
ctx:claims/beam/9151b445-41b5-4d53-900d-4199adc168c1- full textbeam-chunktext/plain1 KB
doc:beam/9151b445-41b5-4d53-900d-4199adc168c1Show excerpt
model = MyModel().to(device) 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) …
ctx:claims/beam/343d7abc-9aa0-4e2b-8884-910c760bfe88- full textbeam-chunktext/plain1 KB
doc:beam/343d7abc-9aa0-4e2b-8884-910c760bfe88Show excerpt
self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x # Initialize the model and optimizer model = MyModel() opt…
ctx:claims/beam/7ad4ed2e-4b51-4d78-a76b-a1c53b9233f1
See also
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