<p>From <a href="https://stats.stackexchange.com/q/232393/12359">Is there any method for choosing the number of layers and neurons?</a>:</p>

<p>There is no direct way to find the optimal number of them: people empirically try and see (e.g., using cross-validation). The most common search techniques are random, manual, and grid searches. </p>

<p>There exist more advanced techniques such as</p>

<p>1) Gaussian processes. Example:</p>

<ul>
<li>Franck Dernoncourt, Ji Young Lee <a href="http://arxiv.org/abs/1609.08703" rel="nofollow noreferrer">Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification</a>, IEEE SLT 2016.</li>
</ul>

<p>2) <a href="https://en.wikipedia.org/wiki/Neuroevolution" rel="nofollow noreferrer">Neuro-evolution</a>. Examples:</p>

<ul>
<li>Zaremba, Wojciech. Ilya Sutskever. Rafal Jozefowicz "<a href="https://scholar.google.com/scholar?cluster=9021528542952156548&amp;hl=en&amp;as_sdt=0,22" rel="nofollow noreferrer">An empirical exploration of recurrent network architectures.</a>" (2015): used evolutionary computation to find optimal RNN structures.</li>
<li>Franck Dernoncourt. "<a href="http://www.francky.me/doc/mrf2011-HEC-ISIR-ENS_en.pdf" rel="nofollow noreferrer">The medial Reticular Formation: a neural substrate for action selection? An evaluation via evolutionary computation.</a>". Master's Thesis. École Normale
Supérieure Ulm. 2011. Used evolutionary computation to find connections in the ANN.</li>
<li>Bayer, Justin, Daan Wierstra, Julian Togelius, and Jürgen Schmidhuber. "<a href="https://scholar.google.com/scholar?cluster=14945304742464379854&amp;hl=en&amp;as_sdt=0,22" rel="nofollow noreferrer">Evolving memory cell structures for sequence learning.</a>" In International Conference on Artificial Neural Networks, pp. 755-764. Springer Berlin Heidelberg, 2009.: used evolutionary computation to find optimal RNN structures.</li>
</ul>

<p>Also relevant:  <a href="https://stats.stackexchange.com/q/244975/12359">To design a Multilayer Perceptron, should I use more units per layer and less layers or more layers and less units, which is better?</a></p>
Optimum neuron and neural layer size for accuracy and efficiency
<p>I am working on Deep Neural Networks and was wondering about the following question:</p>

<p>What is the best number of layers and number of neurons per layer in general for <strong><em>optimum accuracy</em></strong>?</p>

<p>As per this picture:
<a href="https://i.stack.imgur.com/4SV9A.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/4SV9A.png" alt="Image1"></a></p>

<p>Would the optimum numbers be equal to the feature size, so that each feature's influence on each other set of features is taken into account? </p>

<p>Also, would the answer differ if we were looking for <strong><em>optimum accuracy and efficiency</em></strong>?</p>

<p>Thank you, any insights are appreciated!</p>

<p>Edit:</p>

<p>These answers are informative. I still feel like they don't address specifically the first part of my question. To clarify: Is there a maximum amount of neurons and layers that when applied would be equally granular to the data, and thus adding more neurons or layers would be redundant? I assume infinite layers to a 3 feature data set would at some point become unnecessary. Thanks again for all reads and replies!</p>