sup3r.utilities.loss_metrics.ExpLoss

class ExpLoss(reduction='auto', name=None)[source]

Bases: Loss

Loss class for squared exponential difference

Initializes Loss class.

Args:

reduction: Type of tf.keras.losses.Reduction to apply to: loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used under a tf.distribute.Strategy, except via Model.compile() and Model.fit(), using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details.

name: Optional name for the instance.

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.

__call__(x1, x2)[source]

Exponential difference loss function

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()).