ANN2

Description

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.

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  installed size is 29.6Mb
  sub-directories of 1Mb or more:
    cereal   1.4Mb
    libs    28.1Mb

  installed size is 29.6Mb
  sub-directories of 1Mb or more:
    cereal   1.4Mb
    libs    28.1Mb

Check History

  installed size is 29.6Mb
  sub-directories of 1Mb or more:
    cereal   1.4Mb
    libs    28.1Mb

  installed size is 29.6Mb
  sub-directories of 1Mb or more:
    cereal   1.4Mb
    libs    28.1Mb

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