AutoModel
provides a meaningful way to test several network architectures in an automated manner. Currently there are five supported architectures:
- conv1d
- lstm
- bidirectional_lstm
- simplernn
- dense
AutoModel
creates an input model for Scan(). Optimized for being used together with AutoParams()
and expects one or more of the above architectures to be included in params dictionary, for example:
p = {...
'networks': ['dense', 'conv1d', 'lstm']
...}
Argument | Input | Description |
---|---|---|
task |
str or None | binary , multi_label , multi_class , or continuous |
metric |
None or list | One or more Keras metric (functions) to be used in the model |
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.