deepnet {deepdive}

Build and train an Artificial Neural Network of any size

Description

Build and train Artifical Neural Network of any depth in a single line code. Choose the hyperparameters to improve the accuracy or generalisation of model.

Usage

deepnet(
  x,
  y,
  hiddenLayerUnits = c(2, 2),
  activation = c("sigmoid", "relu"),
  reluLeak = 0,
  modelType = c("regress"),
  iterations = 500,
  eta = 10^-2,
  seed = 2,
  gradientClip = 0.8,
  regularisePar = 0,
  optimiser = "adam",
  parMomentum = 0.9,
  inputSizeImpact = 1,
  parRmsPropZeroAdjust = 10^-8,
  parRmsProp = 0.9999,
  printItrSize = 100,
  showProgress = TRUE,
  stopError = 0.01,
  miniBatchSize = NA,
  useBatchProgress = FALSE,
  ignoreNAerror = FALSE,
  normalise = TRUE
)

Arguments

x

a data frame with input variables

y

a data frame with ouptut variable

hiddenLayerUnits

a numeric vector, length of vector indicates number of hidden layers and each element in vector indicates corresponding hidden units Eg: c(6,4) for two layers, one with 6 hiiden units and other with 4 hidden units. Note: Output layer is automatically created.

activation

one of "sigmoid","relu","sin","cos","none". The default is "sigmoid". Choose a activation per hidden layer

reluLeak

numeric. Applicable when activation is "relu". Specify value between 0 any number close to zero below 1. Eg: 0.01,0.001 etc

modelType

one of "regress","binary","multiClass". "regress" for regression will create a linear single unit output layer. "binary" will create a single unit sigmoid activated layer. "multiClass" will create layer with units corresponding to number of output classes with softmax activation.

iterations

integer. This indicates number of iteratios or epochs in backpropagtion .The default value is 500.

eta

numeric.Hyperparameter,sets the Learning rate for backpropagation. Eta determines the convergence ability and speed of convergence.

seed

numeric. Set seed with this parameter. Incase of sin activation sometimes changing seed can yeild better results. Default is 2

gradientClip

numeric. Hyperparameter numeric value which limits gradient size for weight update operation in backpropagation. Default is 0.8 . It can take any postive value.

regularisePar

numeric. L2 Regularisation Parameter .

optimiser

one of "gradientDescent","momentum","rmsProp","adam". Default value "adam"

parMomentum

numeric. Applicable for optimiser "mometum" and "adam"

inputSizeImpact

numeric. Adjusts the gradient size by factor of percentage of rows in input. For very small data set setting this to 0 could yeild faster result. Default is 1.

parRmsPropZeroAdjust

numeric. Applicable for optimiser "rmsProp" and "adam"

parRmsProp

numeric.Applicable for optimiser "rmsProp" and "adam"

printItrSize

numeric. Number of iterations after which progress message should be shown. Default value 100 and for iterations below 100 atleast 5 messages will be seen

showProgress

logical. True will show progress and F will not show progress

stopError

Numeric. Rmse at which iterations can be stopped. Default is 0.01, can be set as NA in case all iterations needs to run.

miniBatchSize

integer. Set the mini batch size for mini batch gradient

useBatchProgress

logical. Applicable for miniBatch , setting T will use show rmse in Batch and F will show error on full dataset. For large dataset set T

ignoreNAerror

logical. Set T if iteration needs to be stopped when predictions become NA

normalise

logical. Set F if normalisation not required.Default T

Value

returns model object which can be passed into predict.deepnet

Examples

require(deepdive)

x <- data.frame(x1 = runif(10),x2 = runif(10))
y<- data.frame(y=20*x$x1 +30*x$x2+10)

#train
modelnet<-deepnet(x,y,c(2,2),
activation = c('relu',"sigmoid"),
reluLeak = 0.01,
modelType = "regress",
iterations =5,
eta=0.8,
optimiser="adam")

#predict
predDeepNet<-predict.deepnet(modelnet,newData=x)

#evaluate
sqrt(mean((predDeepNet$ypred-y$y)^2))