MLP_net: MLP_net function

In deepNN: Deep Learning

MLP_net function

Description

A function to define a multilayer perceptron and compute quantities for backpropagation, if needed.

Usage

MLP_net(input, weights, bias, dims, nlayers, activ, back = TRUE, regulariser)

Arguments

input	input data, a list of vectors (i.e. ragged array)
weights	a list object containing weights for the forward pass, see ?weights2list
bias	a list object containing biases for the forward pass, see ?bias2list
dims	the dimensions of the network as stored from a call to the function network, see ?network
nlayers	number of layers as stored from a call to the function network, see ?network
activ	list of activation functions as stored from a call to the function network, see ?network
back	logical, whether to compute quantities for backpropagation (set to FALSE for feed-forward use only)
regulariser	type of regularisation strategy to, see ?train, ?no_regularisation ?L1_regularisation, ?L2_regularisation

Value

a list object containing the evaluated forward pass and also, if selected, quantities for backpropagation.

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

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