deepof.models module

deepof.models	deep autoencoder models for unsupervised pose detection.
deepof.models.get_TCN_decoder(input_shape, ...)	Return a Temporal Convolutional Network (TCN) decoder.
deepof.models.get_TCN_encoder(input_shape, ...)	Return a Temporal Convolutional Network (TCN) encoder.
deepof.models.get_recurrent_decoder(...[, ...])	Return a recurrent neural decoder.
deepof.models.get_recurrent_encoder(...[, ...])	Return a deep recurrent neural encoder.
deepof.models.get_transformer_decoder(...[, ...])	Build a Transformer decoder.
deepof.models.get_transformer_encoder(...[, ...])	Build a Transformer encoder.
deepof.models.get_vade(input_shape, ...[, ...])	Build a Gaussian mixture variational autoencoder (VaDE) model, adapted to the DeepOF setting.
deepof.models.get_vqvae(input_shape, ...[, ...])	Build a Vector-Quantization variational autoencoder (VQ-VAE) model, adapted to the DeepOF setting.
deepof.models.Classifier(*args, **kwargs)	Classifier for supervised pose motif elucidation.
deepof.models.Contrastive(*args, **kwargs)	Self-supervised contrastive embeddings.
deepof.models.GaussianMixtureLatent(*args, ...)	Gaussian Mixture probabilistic latent space model.
deepof.models.VQVAE(*args, **kwargs)	VQ-VAE model adapted to the DeepOF setting.
deepof.models.VaDE(*args, **kwargs)	Gaussian Mixture Variational Autoencoder for pose motif elucidation.
deepof.models.VectorQuantizer(*args, **kwargs)	Vector quantizer layer.