alibi_detect.models.tensorflow.losses module
- alibi_detect.models.tensorflow.losses.elbo(y_true, y_pred, cov_full=None, cov_diag=None, sim=None)[source]
Compute ELBO loss. The covariance matrix can be specified by passing the full covariance matrix, the matrix diagonal, or a scale identity multiplier. Only one of these should be specified. If none are specified, the identity matrix is used.
- Parameters:
- Return type:
Tensor- Returns:
ELBO loss value.
Example
>>> import tensorflow as tf >>> from alibi_detect.models.tensorflow.losses import elbo >>> y_true = tf.constant([[0.0, 1.0], [1.0, 0.0]]) >>> y_pred = tf.constant([[0.1, 0.9], [0.8, 0.2]]) >>> # Specifying scale identity multiplier >>> elbo(y_true, y_pred, sim=1.0) >>> # Specifying covariance matrix diagonal >>> elbo(y_true, y_pred, cov_diag=tf.ones(2)) >>> # Specifying full covariance matrix >>> elbo(y_true, y_pred, cov_full=tf.eye(2))
- alibi_detect.models.tensorflow.losses.loss_adv_ae(x_true, x_pred, model=None, model_hl=None, w_model=1.0, w_recon=0.0, w_model_hl=None, temperature=1.0)[source]
Loss function used for AdversarialAE.
- Parameters:
x_true (
Tensor) – Batch of instances.x_pred (
Tensor) – Batch of reconstructed instances by the autoencoder.model (
Optional[Model]) – A trained tf.keras model with frozen layers (layers.trainable = False).model_hl (
Optional[list]) – List with tf.keras models used to extract feature maps and make predictions on hidden layers.w_model (
float) – Weight on model prediction loss term.w_recon (
float) – Weight on MSE reconstruction error loss term.w_model_hl (
Optional[list]) – Weights assigned to the loss of each model in model_hl.temperature (
float) – Temperature used for model prediction scaling. Temperature <1 sharpens the prediction probability distribution.
- Return type:
Tensor- Returns:
Loss value.
- alibi_detect.models.tensorflow.losses.loss_aegmm(x_true, x_pred, z, gamma, w_energy=0.1, w_cov_diag=0.005)[source]
Loss function used for OutlierAEGMM.
- Parameters:
x_true (
Tensor) – Batch of instances.x_pred (
Tensor) – Batch of reconstructed instances by the autoencoder.z (
Tensor) – Latent space values.gamma (
Tensor) – Membership prediction for mixture model components.w_energy (
float) – Weight on sample energy loss term.w_cov_diag (
float) – Weight on covariance regularizing loss term.
- Return type:
Tensor- Returns:
Loss value.
- alibi_detect.models.tensorflow.losses.loss_distillation(x_true, y_pred, model=None, loss_type='kld', temperature=1.0)[source]
Loss function used for Model Distillation.
- Parameters:
x_true (
Tensor) – Batch of data points.y_pred (
Tensor) – Batch of prediction from the distilled model.model (
Optional[Model]) – tf.keras model.loss_type (
str) – Type of loss for distillation. Supported ‘kld’, ‘xent.temperature (
float) – Temperature used for model prediction scaling. Temperature <1 sharpens the prediction probability distribution.
- Return type:
Tensor- Returns:
Loss value.
- alibi_detect.models.tensorflow.losses.loss_vaegmm(x_true, x_pred, z, gamma, w_recon=1e-07, w_energy=0.1, w_cov_diag=0.005, cov_full=None, cov_diag=None, sim=0.05)[source]
Loss function used for OutlierVAEGMM.
- Parameters:
x_true (
Tensor) – Batch of instances.x_pred (
Tensor) – Batch of reconstructed instances by the variational autoencoder.z (
Tensor) – Latent space values.gamma (
Tensor) – Membership prediction for mixture model components.w_recon (
float) – Weight on elbo loss term.w_energy (
float) – Weight on sample energy loss term.w_cov_diag (
float) – Weight on covariance regularizing loss term.cov_full (
Optional[Tensor]) – Full covariance matrix.cov_diag (
Optional[Tensor]) – Diagonal (variance) of covariance matrix.sim (
float) – Scale identity multiplier.
- Return type:
Tensor- Returns:
Loss value.