sup3r.utilities.loss_metrics.CoarseMseLoss#

class CoarseMseLoss(gen_features='all', true_features=None)[source]#

Loss class for coarse mse on spatial average of 5D tensor

Initialize the loss with given input features

Parameters:

gen_features (list | str) – List of generator output features that the loss metric will be calculated on. If ‘all’, the loss will be calculated on all generator features. Otherwise, the loss will be calculated on the features specified in the list. The order of features in the list will be checked to determine the order of features in the generator output tensor.
true_features (list | str) – List of true features that the loss metric will be calculated on. If None, this will be the same as gen_features. The order of features in the list will be checked to determine the order of features in the ground truth tensor.

Methods

`call`(x_true, x_gen)	Exponential difference loss function
`from_config`(config)
`get_config`()

Attributes

`MSE_LOSS`
`dtype`

call(x_true, x_gen)[source]#

Exponential difference loss function

Parameters:

x_true (tf.tensor) – high resolution ground truth data (n_observations, spatial_1, spatial_2, temporal, features)
x_gen (tf.tensor) – synthetic generator output (n_observations, spatial_1, spatial_2, temporal, features)

Returns:

tf.tensor – 0D tensor with loss value

__call__(y_true, y_pred, sample_weight=None)#: Call self as a function.

sup3r.utilities.loss_metrics.CoarseMseLoss