reconstruct

<code>reconstruct</code> takes new data as input and reconstructs the observations using
a trained replicator or autoencoder object.

Training of neural networks for classification and regression tasks
using mini-batch gradient descent. Special features include a function for
training autoencoders, which can be used to detect anomalies, and some
related plotting functions. Multiple activation functions are supported,
including tanh, relu, step and ramp. For the use of the step and ramp
activation functions in detecting anomalies using autoencoders, see
Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore,
several loss functions are supported, including robust ones such as Huber
and pseudo-Huber loss, as well as L1 and L2 regularization. The possible
options for optimization algorithms are RMSprop, Adam and SGD with momentum.
The package contains a vectorized C++ implementation that facilitates
fast training through mini-batch learning.

Bart Lammers

ANN2

Artificial Neural Networks for Anomaly Detection

reconstruct function

<dl><dt>object</dt>
<dd>Object of class <code>ANN</code> created with <code>autoencoder()</code></dd>
<dt>X</dt>
<dd>data matrix to reconstruct</dd></dl>

Arguments

Reconstruct data using trained ANN object of type autoencoder — reconstruct

<dl>

<dt>object</dt>
<dd>Object of class <code>ANN</code> created with <code>autoencoder()</code></dd>


<dt>X</dt>
<dd>data matrix to reconstruct</dd>

</dl>

reconstruct: Reconstruct data using trained ANN object of type autoencoder

Description

Usage

Value

Arguments

Details