Random Search Experiment

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Description
A random search experiment enables you to randomly search over your hyperparameter space with the SigOpt Platform.
View a sample random search experiment implementation in a notebook
See this notebook for a demonstration of how easy random search is with SigOpt. For more notebook instructions and tutorials, check out our GitHub notebook tutorials repo.​
Python
YAML
sigopt.create_experiment(
  name="Random search",
  type="random",
  parameters=[
    dict(
      name="hidden_layer_size",
      type="int",
      bounds=dict(
        min=32,
        max=512
      )
    ),
    dict(
      name="activation_function",
      type="categorical",
      categorical_values=[
        "relu",
        "tanh"
      ]
    )
  ],
  metrics=[
    dict(
      name="holdout_accuracy",
      objective="maximize"
    )
  ],
  parallel_bandwidth=1,
  budget=30
)
name: Random search
type: random
parameters:
  - name: hidden_layer_size
    type: int
    bounds:
      min: 32
      max: 512
  - name: activation_function
    type: categorical
    categorical_values:
      - relu
      - tanh
metrics:
  - name: holdout_accuracy
    objective: maximize
parallel_bandwidth: 1
budget: 30

All Constraints Experiment

Grid Search Experiment

Last modified 3mo ago

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