sup3r.utilities.loss_metrics.StExtremesFftLoss
- class StExtremesFftLoss(spatial_weight=1.0, temporal_weight=1.0, fft_weight=1.0)[source]
Bases:
Loss
Loss class that encourages accuracy of the min/max values across both space and time as well as frequency domain accuracy.
Initialize the loss with given weight
- Parameters:
spatial_weight (float) – Weight for spatial min/max loss terms.
temporal_weight (float) – Weight for temporal min/max loss terms.
fft_weight (float) – Weight for the fft loss term.
Methods
call
(y_true, y_pred)Invokes the Loss instance.
from_config
(config)Instantiates a Loss from its config (output of get_config()).
Returns the config dictionary for a Loss instance.
- __call__(x1, x2)[source]
Custom content loss that encourages spatiotemporal min/max accuracy and fft 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()).
- Args:
config: Output of get_config().
- Returns:
A Loss instance.
- get_config()
Returns the config dictionary for a Loss instance.