orcanet_contrib.custom_objects

Module Contents

Functions

get_custom_objects()	Functions that returns a dict with all relevant loss functions in this file.
loss_mean_relative_error_energy(y_true, y_pred)	Loss function that calculates the mean relative error.
loss_uncertainty_mae(y_true, y_pred)	Mean absolute error loss for the uncertainty estimation.
loss_uncertainty_mse(y_true, y_pred)	Mean squared error loss for the uncertainty estimation.
loss_uncertainty_gaussian_likelihood(y_true, y_pred)	Loss function that calculates something similar to a Gaussian Likelihood.
loss_uncertainty_gaussian_likelihood_dir(y_true, y_pred)	Loss function that calculates something similar to a Gaussian
loss_direction(y_true, y_pred)	Loss function that calculates the space angle between the predicted
loss_direction_grad()	Similar loss function as in loss_direction, but here it is just used
mean_absolute_error(y_true, y_pred)	Copy of the Keras mean absolute error function for testing purposes.

Attributes

dir_y_true

orcanet_contrib.custom_objects.get_custom_objects()

Functions that returns a dict with all relevant loss functions in this file.

orcanet_contrib.custom_objects.loss_mean_relative_error_energy(y_true, y_pred)

Loss function that calculates the mean relative error. y_true & y_pred are expected to be e_true & e_pred. L = (e_reco - e_true) / e_true

Returns

mreMean relative (energy) error loss

orcanet_contrib.custom_objects.loss_uncertainty_mae(y_true, y_pred)

Mean absolute error loss for the uncertainty estimation. L = sigma_pred / abs(label_true - label_reco).

Returns

lossMean absolute error for uncertainty estimations.

orcanet_contrib.custom_objects.loss_uncertainty_mse(y_true, y_pred)

Mean squared error loss for the uncertainty estimation. L = sigma_pred / abs(label_true - label_reco)**2.

Returns

lossMean squared error for uncertainty estimations.

orcanet_contrib.custom_objects.loss_uncertainty_gaussian_likelihood(y_true, y_pred)

Loss function that calculates something similar to a Gaussian Likelihood. Requires that y_pred contains only one predicted value (label). y_true & y_pred are expected to contain the predicted/true label and the predicted std for the label. L = ln(std ** 2) + (y_label_pred - y_label_true) / (std ** 2)

Returns

lossGaussian Likelihood loss

orcanet_contrib.custom_objects.loss_uncertainty_gaussian_likelihood_dir(y_true, y_pred)

Loss function that calculates something similar to a Gaussian Likelihood for predicted directions. Requires that y_pred contains three predicted values (labels): dir_x, dir_y, dir_z. y_true & y_pred are expected to contain the predicted/true label and the predicted std for the label. L = ln(std ** 2) + (y_label_pred - y_label_true) / (std ** 2)

Returns

lossGaussian Likelihood loss for the directional error

orcanet_contrib.custom_objects.loss_direction(y_true, y_pred)

Loss function that calculates the space angle between the predicted and the true direction. Not used anymore, can lead to inf gradients due to tf.acos(space_angle_inner_value)! Converts cartesian dirs to spherical coordinate system and then calculates the space angle between the two vectors.

Returns

total_lossSpace angle loss

orcanet_contrib.custom_objects.loss_direction_grad()

Similar loss function as in loss_direction, but here it is just used for manually calculating the gradient in the main function. Done in order to protect the loss_direction function from inf gradients.

orcanet_contrib.custom_objects.mean_absolute_error(y_true, y_pred)

Copy of the Keras mean absolute error function for testing purposes.

orcanet_contrib.custom_objects.dir_y_true