`trw.transforms.renormalize`¶

Module Contents¶

`renormalize_torch`(data, desired_mean, desired_std, current_mean=None, current_std=None)	Transform the data so that it has desired mean and standard deviation element wise
`renormalize_numpy`(data, desired_mean, desired_std, current_mean=None, current_std=None)
`renormalize`(data, desired_mean, desired_std, current_mean=None, current_std=None)	Transform the data so that it has desired mean and standard deviation element wise

trw.transforms.renormalize.renormalize_torch(data, desired_mean, desired_std, current_mean=None, current_std=None)¶

Transform the data so that it has desired mean and standard deviation element wise

Parameters

data – a torch.Tensor
desired_mean – the mean to transform data to
desired_std – the std to transform data to
current_mean – if the mean if known, do not recalculate it (e.g., training mean to be used in validation split)
current_std – if the std if known, do not recalculate it (e.g., training std to be used in validation split)

Returns

a torch.Tensor data with mean desired_mean and std desired_std

trw.transforms.renormalize.renormalize_numpy(data, desired_mean, desired_std, current_mean=None, current_std=None)¶

trw.transforms.renormalize.renormalize(data, desired_mean, desired_std, current_mean=None, current_std=None)¶

Transform the data so that it has desired mean and standard deviation element wise

Parameters

data – a torch or numpy array
desired_mean – the mean to transform data to
desired_std – the std to transform data to
current_mean – if the mean if known, do not recalculate it (e.g., training mean to be used in validation split)
current_std – if the std if known, do not recalculate it (e.g., training std to be used in validation split)

Returns

a data with mean desired_mean and std desired_std