Grid Search Experiment

Description

A grid search experiment enables users to execute an exhaustive search on a user-defined grid with the SigOpt Platform. This means that you'll need to assign grid (or categorical) values to every parameter as part of the experiment create call.

View a sample grid search experiment implementation in a notebook

See this notebook for a demonstration of how easy grid search is with SigOpt. For more notebook instructions and tutorials, check out our GitHub notebook tutorials repo.
Python
YAML
sigopt.create_experiment(
name="Grid search",
type="grid",
parameters=[
dict(
name="hidden_layer_size",
type="int",
grid=[
32,
64,
128,
256,
512
]
),
dict(
name="activation_function",
type="categorical",
categorical_values=[
"relu",
"tanh"
]
)
],
metrics=[
dict(
name="holdout_accuracy",
objective="maximize"
)
],
parallel_bandwidth=1,
budget=30
)
name: Grid search
type: grid
parameters:
- name: hidden_layer_size
type: int
grid:
- 32
- 64
- 128
- 256
- 512
- name: activation_function
type: categorical
categorical_values:
- relu
- tanh
metrics:
- name: holdout_accuracy
objective: maximize
parallel_bandwidth: 1
budget: 30
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