sup3r.utilities.loss_metrics.TemporalExtremesLoss

class TemporalExtremesLoss(weight=1.0)[source]

Bases: Loss

Loss class that encourages accuracy of the min/max values in the timeseries

Initialize the loss with given weight

Parameters:: weight (float) – Weight for min/max loss terms. Setting this to zero turns loss into MAE.

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

`EX_LOSS`
`MAE_LOSS`

__call__(x1, x2)[source]

Custom content loss that encourages temporal min/max accuracy

Parameters:

x1 (tf.tensor) – synthetic generator output (n_observations, spatial_1, spatial_2, temporal, features)
x2 (tf.tensor) – high resolution data (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()).