fwdNN: Feed forward and back propagation for dnn Models

In dnn: Deep Neural Network Tools for Probability and Statistic Models

Feed forward and back propagation for dnn Models

Description

{fwdNN} is an R function for feed forward network.

Usage

   fwdNN(X, model)             
#
# to calculate a feed feedward model 
#

Arguments

X	For "dNNmodel", X is a design matrix of dimension n * p.
model	a model return from dNNmodel function.

Details

'cache' is the cache of each input layer, will be used in the bwdNN function.

Value

The function fwdNN return a list containing at least the following components:

cache

a list contains the values of each output layer after activation function transformation and adding the intercept term (i.e. the bias term). The intercept does not add to the output layer in the cache.

Author(s)

Bingshu E. Chen ([e-mail])

See Also

bwdNN, plot.dNNmodel, print.dNNmodel, summary.dNNmodel,

Examples

### define a dnn model, calculate the feed forward network
   model = dNNmodel(units = c(8, 6, 1), activation = c("elu", "sigmoid", "sigmoid"), 
                   input_shape = 3)
  
### feed forward with a dummy x matrix
   x = matrix(runif(15), nrow = 5, ncol = 3)
   cache = fwdNN(x, model)

dnn documentation built on May 29, 2024, 1:48 a.m.