sup3r.utilities.loss_metrics.TemporalDerivativeLoss#

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

Loss class to encourage accurary of temporal derivative.

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`(y_true, y_pred)	Invokes the Loss instance.
`from_config`(config)	Instantiates a Loss from its config (output of get_config()).
`get_config`()	Returns the config dictionary for a Loss instance.

Attributes

LOSS_METRIC

__call__(x_true, x_gen)[source]#

Custom content loss that encourages accuracy of temporal derivative

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

abstract call(y_true, y_pred)#

Invokes the Loss instance.

Args:

y_true: Ground truth values. shape = [batch_size, d0, .. dN],: except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]

y_pred: The predicted values. shape = [batch_size, d0, .. dN]

Returns:

Loss values with the shape [batch_size, d0, .. dN-1].

classmethod from_config(config)#

Instantiates a Loss from its config (output of get_config()).

sup3r.utilities.loss_metrics.TemporalDerivativeLoss