autoencoder_sparse: Sparse autoencoder

In fdavidcl/ruta: Implementation of Unsupervised Neural Architectures

Sparse autoencoder

Description

Creates a representation of a sparse autoencoder.

Usage

autoencoder_sparse(
  network,
  loss = "mean_squared_error",
  high_probability = 0.1,
  weight = 0.2
)

Arguments

network	Layer construct of class "ruta_network"
loss	Character string specifying a loss function
high_probability	Expected probability of the high value of the encoding layer. Set this to a value near zero in order to minimize activations in that layer.
weight	The weight of the sparsity regularization

Value

A construct of class "ruta_autoencoder"

References

• Sparse deep belief net model for visual area V2

• Andrew Ng, Sparse Autoencoder. CS294A Lecture Notes

See Also

sparsity, make_sparse, is_sparse

Other autoencoder variants: autoencoder_contractive(), autoencoder_denoising(), autoencoder_robust(), autoencoder_variational(), autoencoder()

fdavidcl/ruta documentation built on July 5, 2023, 6:32 p.m.