NNpredict.regression: NNpredict.regression function

In deepNN: Deep Learning

NNpredict.regression function

Description

A function to produce predictions from a trained network

Usage

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

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

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

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, 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

deepNN documentation built on Aug. 25, 2023, 5:14 p.m.