# ...(other options not yet implemented)...
"search_strategy": "grid",
# `grid_search_space` specifies values to be searched for each hyperparameter
# each entry needs to follow the format {"param_name" : List(Any)}
"grid_search_space": {
"layer1_nodes": [16, 32, 64],
"layer2_nodes": [16, 32, 64],
"optimizer": ["adam", "sgd"],
},
# `grid_search_settings` contain other settings for grid search strategy
# `save_every_n_outputs`: how often should the trial results be saved.
# The more often we save the results, the less likely we lose data in
# the event of a crash, but it takes more time.
# `num_samples`: how many repeated trials to run for each point in search space
# [Not yet implemented]
"grid_search_settings": {
"save_every_n_outputs": 1,
"num_samples": 1
}
}
# trainer must be a function that takes an hpset as input and returns (hpset,
# metric, logs) as output
# `hpset`: hyperparameter values, see hyperopt.Experiment._generate_hpsets
# `metric`: objective for maximization, evaluated at the point specified by `hpset`
# `logs`: any other useful information that should be saved
exper = Experiment(trainer, config)
exper.search()
exper.summary()
