stopping.maxit

A function to halt computation when the number of iterations reaches a given threshold, tol

Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.

Benjamin Taylor

deepNN

Deep Learning

stopping.maxit function

<dl><dt>cost</dt>
<dd>the value of the loss function passed in</dd>
<dt>curcost</dt>
<dd>current measure of cost, can be different to the parameter 'cost' above e.g. may consider smoothed cost over the last k iterations</dd>
<dt>count</dt>
<dd>iteration count</dd>
<dt>tol</dt>
<dd>tolerance, or limit</dd>
<dt>...</dt>
<dd>additional arguments</dd></dl>

Arguments

stopping.maxit function — stopping.maxit

<dl>

<dt>cost</dt>
<dd>the value of the loss function passed in</dd>


<dt>curcost</dt>
<dd>current measure of cost, can be different to the parameter 'cost' above e.g. may consider smoothed cost over the last k iterations</dd>


<dt>count</dt>
<dd>iteration count</dd>


<dt>tol</dt>
<dd>tolerance, or limit</dd>


<dt>...</dt>
<dd>additional arguments</dd>

</dl>

stopping.maxit: stopping.maxit function

Description

Usage

Value

Arguments