A denoising autoencoder trains with noisy data in order to create a model able to reduce noise in reconstructions from input data
autoencoder_denoising(network, loss = "mean_squared_error",
noise_type = "zeros", ...)Layer construct of class "ruta_network"
Loss function to be optimized
Type of data corruption which will be used to train the autoencoder, as a character string. Available types:
"zeros" Randomly set components to zero (noise_zeros)
"ones" Randomly set components to one (noise_ones)
"saltpepper" Randomly set components to zero or one (noise_saltpepper)
"gaussian" Randomly offset each component of an input as drawn from
Gaussian distributions with the same variance (additive Gaussian noise,
noise_gaussian)
"cauchy" Randomly offset each component of an input as drawn from
Cauchy distributions with the same scale (additive Cauchy noise,
noise_cauchy)
Extra parameters to customize the noisy filter:
p The probability that each instance in the input data which will be
altered by random noise (for "zeros", "ones" and "saltpepper")
var or sd The variance or standard deviation of the Gaussian
distribution from which additive noise will be drawn (for "gaussian",
only one of those parameters is necessary)
scale For the Cauchy distribution
A construct of class "ruta_autoencoder"
Other autoencoder variants: autoencoder_contractive,
autoencoder_robust,
autoencoder_sparse,
autoencoder_variational,
autoencoder