dataeval.utils.models.GMMDensityNet¶

class dataeval.utils.models.GMMDensityNet(latent_dim, n_gmm=2, hidden_dim=10)¶

Gaussian Mixture Model (GMM) density network for converting latent representations.

Converts latent representations to mixture assignment probabilities.

This network can be appended to AE or VAE models to enable GMM-based OOD detection by producing gamma (mixture assignment probabilities) from the latent representation.

Parameters:¶

latent_dim : int¶

Dimensionality of the latent space (encoding dimension from AE/VAE).

n_gmm : int, default 2¶

Number of Gaussian mixture components.

hidden_dim : int, default 10¶

Number of hidden units in the dense layer.

Example

Creating a VAE with GMM density estimation:

>>> from dataeval.shift import OODReconstruction
>>> from dataeval.utils.models import VAE, GMMDensityNet
>>>
>>> # Use with OODReconstruction
>>> gmm_density_net = GMMDensityNet(latent_dim=256, n_gmm=3)
>>> vae_gmm_model = VAE(input_shape=(1, 28, 28), gmm_density_net=gmm_density_net)
>>> ood = OODReconstruction(vae_gmm_model, model_type="vae", use_gmm=True)

Notes

The network architecture is based on the GMM density network from Alibi-Detect, adapted from TensorFlow to PyTorch. It consists of: - A hidden layer with tanh activation - An output layer with softmax activation to produce valid probability distributions

forward(z)¶

Convert latent representation to mixture assignment probabilities.

Parameters:¶

z : torch.Tensor¶

Latent representation with shape (batch_size, latent_dim).

Returns:¶

Mixture assignment probabilities (gamma) with shape (batch_size, n_gmm). Each row sums to 1.0 and represents the probability distribution over mixture components for that sample.

Return type:¶

torch.Tensor