AutoScan() provides a streamlined way for conducting a hyperparameter search experiment with any dataset. It is particularly useful for early exploration as with default settings AutoScan() casts a very broad parameter space including all common hyperparameters, network shapes, sizes, as well as architectures
Configure the AutoScan() experiment and then use the property start in the returned class object to start the actual experiment.
auto = talos.autom8.AutoScan(task='binary', max_param_values=2)
auto.start(x, y, experiment_name='testing.new', fraction_limit=0.001)NOTE: auto.start() accepts all Scan() arguments.
| Argument | Input | Description |
|---|---|---|
task |
str or None | binary, multi_label, multi_class, or continuous |
max_param_values |
int | Number of parameter values to be included |
Setting task effects which various aspects of the model and should be set according to the specific prediction task, or set to None in which case metric input is required.
The only property start starts the actual experiment. AutoScan.start() accepts the following arguments:
| Argument | Input | Description |
|---|---|---|
x |
array or list of arrays | prediction features |
y |
array or list of arrays | prediction outcome variable |
kwargs |
arguments | any Scan() argument can be passed into AutoScan.start() |