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gh-jeremylongshore-claude-c…/skills/hyperparameter-tuner/assets/hyperparameter_space_template.json
2025-11-29 18:51:22 +08:00

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{
"_comment": "Template for defining the hyperparameter search space. This file should be used as a guide for creating your own hyperparameter configuration.",
"algorithm": {
"_comment": "The search algorithm to use. Options: 'grid', 'random', 'bayesian'.",
"type": "string",
"default": "random",
"enum": ["grid", "random", "bayesian"]
},
"objective": {
"_comment": "The metric to optimize. The plugin will attempt to maximize this metric.",
"type": "string",
"default": "val_loss"
},
"max_trials": {
"_comment": "The maximum number of trials to run. Each trial will explore a different set of hyperparameters.",
"type": "integer",
"default": 10
},
"hyperparameters": {
"_comment": "A dictionary of hyperparameters to search. Each key is the name of the hyperparameter, and the value is a dictionary defining the search space for that hyperparameter.",
"type": "object",
"properties": {
"learning_rate": {
"_comment": "Example: Learning rate for a neural network.",
"type": "number",
"distribution": "loguniform",
"min": 0.0001,
"max": 0.1
},
"num_layers": {
"_comment": "Example: Number of layers in a neural network.",
"type": "integer",
"distribution": "uniform",
"min": 2,
"max": 6
},
"dropout_rate": {
"_comment": "Example: Dropout rate for regularization.",
"type": "number",
"distribution": "uniform",
"min": 0.0,
"max": 0.5
},
"batch_size": {
"_comment": "Example: Batch size for training.",
"type": "integer",
"distribution": "categorical",
"values": [32, 64, 128, 256]
},
"optimizer": {
"_comment": "Example: Optimization algorithm to use",
"type": "string",
"distribution": "categorical",
"values": ["adam", "sgd", "rmsprop"]
}
},
"required": ["learning_rate", "num_layers"]
},
"early_stopping": {
"_comment": "Parameters for early stopping. If enabled, the tuning process will stop if the objective metric does not improve for a specified number of epochs.",
"type": "object",
"properties": {
"monitor": {
"_comment": "The metric to monitor for early stopping.",
"type": "string",
"default": "val_loss"
},
"patience": {
"_comment": "The number of epochs with no improvement after which training will be stopped.",
"type": "integer",
"default": 3
},
"enabled": {
"_comment": "Whether early stopping is enabled.",
"type": "boolean",
"default": true
}
},
"required": ["monitor", "patience", "enabled"]
}
}