% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autoenc_denoise_ed.R
\name{autoenc_denoise_ed}
\alias{autoenc_denoise_ed}
\title{Denoising Autoencoder - Encode-Decode}
\usage{
autoenc_denoise_ed(
  input_size,
  encoding_size,
  encoder_hidden_sizes = 64L,
  decoder_hidden_sizes = NULL,
  activation = c("relu", "leaky_relu", "elu", "gelu", "selu", "tanh"),
  output_activation = c("none", "relu", "sigmoid", "tanh", "softplus"),
  negative_slope = 0.2,
  batch_size = 32,
  epochs = 100L,
  num_epochs = NULL,
  learning_rate = 0.001,
  noise_factor = 0.3,
  validation_strategy = c("static", "dynamic"),
  stopping_rule = c("none", "patience", "sma", "ema", "h"),
  val_ratio = 0.3,
  patience = 100L,
  min_delta = 1e-04,
  sma_window = 5L,
  ema_alpha = 0.2,
  test_window = 30L,
  p_value = 0.05
)
}
\arguments{
\item{input_size}{Integer. Number of input features per observation.}

\item{encoding_size}{Integer. Size of the latent (bottleneck) representation.}

\item{encoder_hidden_sizes}{Integer vector. Hidden sizes used by the encoder.}

\item{decoder_hidden_sizes}{Optional integer vector. Hidden sizes used by the decoder.
If \code{NULL}, the decoder mirrors \code{encoder_hidden_sizes} in reverse order.}

\item{activation}{Character. Hidden activation function. One of
\code{"relu"}, \code{"leaky_relu"}, \code{"elu"}, \code{"gelu"}, \code{"selu"}, or \code{"tanh"}.}

\item{output_activation}{Character. Output activation of the decoder. One of
\code{"none"}, \code{"relu"}, \code{"sigmoid"}, \code{"tanh"}, or \code{"softplus"}.}

\item{negative_slope}{Numeric. Negative slope used when \code{activation = "leaky_relu"}.}

\item{batch_size}{Integer. Mini-batch size used during training. Default is 32.}

\item{epochs}{Integer. Maximum number of training epochs. Default is 100.}

\item{num_epochs}{Deprecated compatibility alias for \code{epochs}. If informed, it overrides \code{epochs}.}

\item{learning_rate}{Numeric. Optimizer learning rate. Default is 0.001.}

\item{noise_factor}{Numeric. Standard deviation (scale) of the noise added during training.}

\item{validation_strategy}{Character. One of \code{static} or \code{dynamic}.}

\item{stopping_rule}{Character. One of \code{none}, \code{patience}, \code{sma}, \code{ema}, or \code{h}.}

\item{val_ratio}{Numeric. Validation fraction used when validation is enabled. Default is 0.3.}

\item{patience}{Integer. Early stopping patience. Default is 100.}

\item{min_delta}{Numeric. Minimum improvement to reset early stopping. Default is 1e-4.}

\item{sma_window}{Integer. Window size used by \code{sma}. Default is 5.}

\item{ema_alpha}{Numeric. Smoothing factor used by \code{ema}. Default is 0.2.}

\item{test_window}{Integer. Window size used by \code{h}. Default is 30.}

\item{p_value}{Numeric. Significance threshold used by \code{h}. Default is 0.05.}
}
\value{
A \code{autoenc_denoise_ed} object.
}
\description{
Creates a denoising autoencoder that reconstructs observations
after learning from corrupted inputs through a Python/PyTorch backend.
}
\examples{
\dontrun{
X <- matrix(rnorm(1000), nrow = 50, ncol = 20)
ae <- autoenc_denoise_ed(
  input_size = 20,
  encoding_size = 5,
  encoder_hidden_sizes = c(128L, 64L),
  noise_factor = 0.2
)
ae <- daltoolbox::fit(ae, X)
X_hat <- daltoolbox::transform(ae, X)
mean((X - X_hat)^2)
}

}
\references{
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008). Extracting and Composing Robust Features with Denoising Autoencoders.
}
