loss module
DIRESA loss classes/functions
- Author:
Geert De Paepe
- Email:
- License:
MIT License
- class loss.KLLoss(kl_weight)
KL weighted loss class KL weight is annealed by KLAnnealingCallback
- __init__(kl_weight)
- Parameters:
kl_weight – tensorflow variable with initial KL loss weight
- call(_, z_mean_var)
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
z_mean_var – list with mean and ln of the variance of the distribution
- Returns:
weighted KL loss
- class loss.LatentCovLoss(cov_weight)
Latent covariance loss class Latent covariance weight is annealed by AnnealingCallback
- __init__(cov_weight)
- Parameters:
cov_weight – tensorflow variable with initial covariance loss weight
- call(_, latent)
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
latent – batch of latent vectors
- Returns:
weighted covariance loss
- loss.mae_dist_loss(_, distances)
Absolute Error between original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
batch of absolute errors
- loss.male_dist_loss(_, distances)
Absolute Error between logarithm of original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
batch of absolute logarithmic errors
- loss.mape_dist_loss(_, distances)
Absolute Percentage Error between original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
batch of absolute percentage errors
- loss.mse_dist_loss(_, distances)
Squared Error between original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
batch of squared errors
- loss.msle_dist_loss(_, distances)
Squared Error between logarithm of original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
batch of squared logarithmic errors
- loss.corr_dist_loss(_, distances)
Correlation loss between original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
1 - correlation coefficient
- loss.corr_log_dist_loss(_, distances)
Correlation loss between logarithm of original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
1 - correlation coefficient (of logarithmic distances)
- loss.loc_corr_dist_loss(_, distances)
Correlation loss between original and latent distances with location parameter
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
1 - correlation coefficient (of 50% closest distances in latent space)
- loss.spear_dist_loss(_, distances)
Spearman correlation loss between original and latent distances
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
1 - Spearman correlation coefficient
- loss.canberra_dist_loss(_, distances)
Canberra distance loss with location parameter https://github.com/richardARPANET/mlpy/blob/master/mlpy/canberra/c_canberra.c
- Parameters:
_ – not used (loss functions need 2 params: the true and predicted values)
distances – batch of original and latent distances between twins
- Returns:
Canberra distance (of 50% closest distances in latent space)