NNgrad_test: NNgrad_test function

In deepNN: Deep Learning

NNgrad_test function

Description

A function to test gradient evaluation of a neural network by comparing it with central finite differencing.

Usage

NNgrad_test(net, loss = Qloss(), eps = 1e-05)

Arguments

net	an object of class network, see ?network
loss	a loss function to compute, see ?Qloss, ?multinomial
eps	small value used in the computation of the finite differencing. Default value is 0.00001

Value

the exact (computed via backpropagation) and approximate (via central finite differencing) gradients and also a plot of one against the other.

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

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

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

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