network: network function

In deepNN: Deep Learning

network function

Description

A function to set up a neural network structure.

Usage

network(dims, activ = logistic(), regulariser = NULL)

Arguments

dims	a vector giving the dimensions of the network. The first and last elements are respectively the input and output lengths and the intermediate elements are the dimensions of the hidden layers
activ	either a single function or a list of activation functions, one each for the hidden layers and one for the output layer. See for example ?ReLU, ?softmax etc.
regulariser	optional regularisation strategy, see for example ?no_regularisation (the default) ?L1_regularisation, ?L2_regularisation

Value

a list object with all information to train the network

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()))

net <- network( dims = c(100,50,50,20),
                activ=list(ReLU(),ReLU(),softmax()),
                regulariser=L1_regularisation())

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