encode

encode.ANN

object

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newdata

<p>Integer specifying which hidden layer is the 
compression layer. If NULL this parameter is inferred from the structure 
of the network (hidden layer with smallest number of nodes)</p>

compression.layer

<p>Compress data according to trained replicator or autoencoder.
Outputs are the activations of the nodes in the middle layer for each 
observation in <code>newdata</code></p>

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.

encode: Encoding step

Description

Usage

Arguments