<p>There is no general answer to your question. Such quantities are called hyper-parameters and their choosing is an open problem, and a big part of the art of machine learning. <a href="https://www.quora.com/What-are-hyperparameters-in-machine-learning" rel="nofollow noreferrer">Here</a> is a discussion on the topic on Quora.</p>

<p>For a good introduction into neural networks and their inner-workings, see <a href="http://neuralnetworksanddeeplearning.com/chap3.html" rel="nofollow noreferrer">improving the way neural networks learn</a>.</p>

<p>To gain intuition on choosing such hyper-parameters, and constructing networks architecture, one would be wise to study known successful models:</p>

<p><a href="http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf" rel="nofollow noreferrer">LeNet</a> : The first successful applications of Convolutional Networks were developed by Yann LeCun in 1990’s. Of these, the best known is the LeNet architecture that was used to read zip codes, digits, etc.</p>

<p><a href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks" rel="nofollow noreferrer">AlexNet</a> : The first work that popularized Convolutional Networks in Computer Vision</p>

<p><a href="http://arxiv.org/abs/1409.4842" rel="nofollow noreferrer">GoogleNet</a> : The ILSVRC 2014 winner</p>

<p>Study how they are designed for the particulars of the problem being solved.</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>