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", ...)
Arguments
network 
Layer construct of class "ruta_network" 
loss 
Loss function to be optimized 
noise_type 
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

Value
A construct of class "ruta_autoencoder"
References
See also