softmaxUnitDerivative

data

The weight and bias matrix for the
  calculation

weights

The function calculates the activation of the units and
returns a list, in which the first entry is the result
through the softmax transfer function and the second entry
is the derivative of the transfer function.

The darch package is build on the basis of the code from G. E.
    Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
    nets : last visit: 01.08.2013). This package is for generating neural
    networks with many layers (deep architectures) and train them with the
    method introduced by the publications "A fast learning algorithm for deep
    belief nets" (G. E. Hinton, S. Osindero, Y. W. Teh) and "Reducing the
    dimensionality of data with neural networks" (G. E. Hinton, R. R.
    Salakhutdinov). This method includes a pre training with the contrastive
    divergence method publishing by G.E Hinton (2002) and a fine tuning with
    common known training algorithms like backpropagation or conjugate
    gradient.

softmaxUnitDerivative: Softmax unit function with unit derivatives.

Description

Usage

Arguments

Value

See Also