backprop_evaluate {deepNN}

backprop_evaluate function

Description

A function used by the train function in order to conduct backpropagation.

Usage

backprop_evaluate(parameters, dat, truth, net, loss, batchsize, dropout)

Arguments

parameters

network weights and bias parameters as a vector

dat

the input data, a list of vectors

truth

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

net

an object of class network, see ?network

loss

the loss function, see ?Qloss and ?multinomial

batchsize

optional batchsize argument for use with stochastic gradient descent

dropout

optional list of dropout probabilities ?dropoutProbs

Value

the derivative of the cost function with respect to each of the parameters

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