deepNN (version 1.2)

NNpredict: NNpredict function

Description

A function to produce predictions from a trained network

Usage

NNpredict(
  net,
  param,
  newdata,
  newtruth = NULL,
  freq = 1000,
  record = FALSE,
  plot = FALSE
)

Value

if record is FALSE, the output of the neural network is returned. Otherwise a list of objects is returned including: rec, the predicted probabilities; err, the L1 error between truth and prediction; pred, the predicted categories based on maximum probability; pred_MC, the predicted categories based on maximum probability; truth, the object newtruth, turned into an integer class number

Arguments

net

an object of class network, see ?network

param

vector of trained parameters from the network, see ?train

newdata

input data to be predicted, a list of vectors (i.e. ragged array)

newtruth

the truth, a list of vectors to compare with output from the feed-forward network

freq

frequency to print progress updates to the console, default is every 1000th training point

record

logical, whether to record details of the prediction. Default is FALSE

plot

locical, whether to produce diagnostic plots. Default is FALSE

References

1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach. Deep Learning. (2016)

2. Terrence J. Sejnowski. The Deep Learning Revolution (The MIT Press). (2018)

3. Neural Networks YouTube playlist by 3brown1blue: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

4. http://neuralnetworksanddeeplearning.com/

See Also

NNpredict.regression, network, train, backprop_evaluate, MLP_net, backpropagation_MLP, logistic, ReLU, smoothReLU, ident, softmax, Qloss, multinomial, NNgrad_test, weights2list, bias2list, biasInit, memInit, gradInit, addGrad, nnetpar, nbiaspar, addList, no_regularisation, L1_regularisation, L2_regularisation

Examples

# Example 1 - mnist data

# See example at mnist repository under user bentaylor1 on githib

# Example 2

N <- 1000
d <- matrix(rnorm(5*N),ncol=5)

fun <- function(x){
    lp <- 2*x[2]
    pr <- exp(lp) / (1 + exp(lp))
    ret <- c(0,0)
    ret[1+rbinom(1,1,pr)] <- 1
    return(ret)
}

d <- lapply(1:N,function(i){return(d[i,])})

truth <- lapply(d,fun)

net <- network( dims = c(5,10,2),
                activ=list(ReLU(),softmax()))

netwts <- train( dat=d,
                 truth=truth,
                 net=net,
                 eps=0.01,
                 tol=100,            # run for 100 iterations
                 batchsize=10,       # note this is not enough
                 loss=multinomial(), # for convergence
                 stopping="maxit")

pred <- NNpredict(  net=net,
                    param=netwts$opt,
                    newdata=d,
                    newtruth=truth,
                    record=TRUE,
                    plot=TRUE)