dnn (version 0.0.6)

dNNmodel: Specify a deep neural network model

Description

{dNNmodel} is an R function to create a deep neural network model that is to be used in the feed forward network { fwdNN } and back propagation { bwdNN }.

Usage

dNNmodel(units, activation=NULL, input_shape = NULL, type = NULL, 
           N = NULL, Rcpp=TRUE, optimizer = c("momentum", "nag", "adam"))

Value

An object of class "dNNmodel" is a list containing at least the following components:

units

number of nodes for each layer

activation

activation function

drvfun

derivative of the activation function

params

the initial values of the parameters, to be updated in model training.

input_shape

the number of columns of input X, default is NULL.

N

the number of training sample, default is NULL.

type

default is "dense", currently only support dense layer.

Arguments

units

number of nodes for each layer

activation

activation function

input_shape

the number of columns of input X, default is NULL.

N

the number of training sample, default is NULL.

type

default is "dense", currently only support dense layer.

Rcpp

use Rcpp (C++ for R) to speed up the fwdNN and bwdNN, default is "TRUE".

optimizer

optimizer used in SGD, default is "momentum".

Author

Bingshu E. Chen ([e-mail])

Details

dNNmodel returns an object of class "dNNmodel".

The function "print" (i.e., "print.dNNmodel") can be used to print a summary of the dnn model,

The function "summary" (i.e., "summary.dNNmodel") can be used to print a summary of the dnn model,

See Also

plot.dNNmodel, print.dNNmodel, summary.dNNmodel, fwdNN, bwdNN, optimizerSGD, optimizerNAG,

Examples

### To define a dnn model
 model = dNNmodel(units = c(8, 6, 1), activation = c("relu", "sigmoid", "sigmoid"), 
         input_shape = c(3))