Dontopedia

Batch Size 64

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Batch Size 64 has 2 facts recorded in Dontopedia across 2 references.

2 facts·2 predicates·2 sources
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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providesFourTimesMoreDataPerStepProvides Four Times More Data Per Step(1)

Other facts (2)

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2 facts
PredicateValueRef
Characterized Aslarger[1]
Is Used byBatch Processing[2]

Timeline

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2 undated
—
characterizedAsbeam/98aa08f4-6776-4759-9a34-fc5897ebea4d
larger
—
isUsedBybeam/50866f1c-f63e-42f0-a70c-005f7877c981
ex:batch-processing

References (2)

2 references
  1. [1]98aa08f4 6776 4759 9a34 Fc5897ebea4d1 fact
    ctx:claims/beam/98aa08f4-6776-4759-9a34-fc5897ebea4d
    • full textbeam-chunk
      text/plain1 KBdoc:beam/98aa08f4-6776-4759-9a34-fc5897ebea4d
      Show excerpt
      data_loader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4) model = SecureTuningModel() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr= 0.01) fine_tune_model(model, data_loader, optimizer,…
  2. [2]50866f1c F63e 42f0 A70c 005f7877c9811 fact
    ctx:claims/beam/50866f1c-f63e-42f0-a70c-005f7877c981
    • full textbeam-chunk
      text/plain1 KBdoc:beam/50866f1c-f63e-42f0-a70c-005f7877c981
      Show excerpt
      2. **Model and Optimizer Initialization**: - Move the model to the GPU using `model.to(device)`. - Use `Adam` optimizer with a learning rate of `0.001`. 3. **Batch Processing**: - Process batches in the loop, ensuring efficient gr…

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