pyabsa.framework.configuration_class.config_verification
Module Contents
Functions
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Check the validity of the training configuration arguments. |
Attributes
- pyabsa.framework.configuration_class.config_verification.config_check(args)[source]
Check the validity of the training configuration arguments.
- The config_check function performs a set of assertion checks on the arguments passed to it. The function checks for the validity of the following arguments:
SRD: The span range difference parameter, which should be a non-negative integer. lcf: The local context focus parameter, which should be one of “cdw”, “cdm”, or “fusion”. window: The window parameter, which should be one of “l”, “r”, or “lr”. eta: The learning rate parameter, which should be either -1 or a float between 0 and 1. similarity_threshold: The similarity threshold parameter, which should be a float between 0 and 1. evaluate_begin: The evaluation starting epoch, which should be a non-negative integer less than num_epoch. cross_validate_fold: The number of cross-validation folds, which should be either -1 or a positive integer greater than 1 and less than or equal to 10. dlcf_a: The LCF activation rate parameter, which should be greater than 1. dca_p: The dropout probability parameter, which should be greater than or equal to 1. dca_layer: The number of layers for the DCA model, which should be a positive integer. ensemble_mode: The mode for ensembling models, which should be one of “cat” or “mean”. optimizer: The optimizer for training, which should be one of “adam”, “radam”, “nadam”, or “sparseadam”. use_amp: The automatic mixed precision parameter, which should be either True or False. patience: The number of epochs to wait before early stopping, which should be a positive integer greater than 0.
- Parameters:
args – A dictionary containing the training configuration arguments.