HGQ.bops package
Submodules
HGQ.bops.bops module
- class HGQ.bops.bops.CalibratedBOPs(calibration_data, bsz=None)
Bases:
Callback- on_epoch_end(epoch, logs=None)
Called at the end of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
- Parameters:
epoch – Integer, index of epoch.
logs – Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.
- class HGQ.bops.bops.FreeBOPs
Bases:
Callback- on_epoch_end(epoch, logs=None)
Called at the end of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
- Parameters:
epoch – Integer, index of epoch.
logs – Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.
- class HGQ.bops.bops.ResetMinMax
Bases:
Callback- on_epoch_end(epoch, logs=None)
Called at the end of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
- Parameters:
epoch – Integer, index of epoch.
logs – Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.
- static reset_minmax(model)
- HGQ.bops.bops.trace_minmax(model, dataset, bsz=16384, verbose=True, return_predictions=False, no_bops_computation=False, rst=True, cover_factor=1.0)