Model architecture

CVAE (Convolutional Variational Autoencoder)

• Encoder: a stack of strided Conv2D layers (downsampling) → Flatten → Dense → z_mean and z_log_var, with the reparameterization trick producing the latent sample z.

• Decoder: Dense → Reshape → stacked Conv2DTranspose layers (upsampling) → a final Conv2DTranspose with sigmoid activation reconstructing all input channels.

• Loss: per-channel binary cross-entropy reconstruction loss + beta × KL divergence.

See CVAE.

CondCVAE (Conditional CVAE)

Extends CVAE by concatenating one-hot encoded condition labels into the encoder (after the flattened features) and the decoder (with the latent vector). The number of condition columns chosen in initialize_model determines the one-hot dimension; the fitted encoder is saved alongside the model so the same encoding is reused at inference.

See CondCVAE.

Configuration

Key parameters of initialize_model():

Parameter	Default	Description
n_latent_dim	—	Dimensionality of the latent space.
n_dense_dim	—	Size of the dense layer between conv and latent layers.
conditions	—	obs columns used as conditions; [] → plain CVAE.
input_shape	(128, 128, 4)	Patch shape (height, width, channels).
conv_layers	(8, 16, 32, 64, 128)	Filters per convolutional layer.
dropout	0.25	Dropout rate.
beta	0.01	KL-divergence weight (beta-VAE).
batch_size	64	Training batch size.

Note

input_shape must be consistent with the patch_size used in generate_dataset (same height/width, plus the channel count).