<p>Well I haven't seen the answer I was looking for so I made a research myself.
<a href="https://i.stack.imgur.com/qCTxz.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/qCTxz.png" alt="enter image description here" /></a></p>
<p>In <a href="https://stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network">this</a> article this is said:</p>
<ul>
<li>Stochastic means 1 sample, mimibatch batch of few samples and batch means full train dataset = this I fould <a href="https://stats.stackexchange.com/questions/117919/what-are-the-differences-between-epoch-batch-and-minibatch">here</a></li>
<li>PROS of smaller batch: faster train, less RAM needed</li>
<li>CONS: The smaller the batch the less accurate the estimate of the gradient will be</li>
</ul>
<p>In <a href="https://arxiv.org/pdf/1803.09820.pdf" rel="nofollow noreferrer">this</a> paper, they were trying 256,512,1024 batch sizes and the performance of all models were in the standard deviation of each other. This means that the batch size didn't have any significant influence on performance.</p>
<p>Final word:</p>
<ul>
<li>If have problem with RAM = decrease batch size</li>
<li>If you need to calculate faster = decrease batch size</li>
<li>If the performace decreased after smaller batch = increase batch size</li>
</ul>
<p>If you find this post useful, please up-vote &amp; comment. Took the time to share it with you. Thanks</p>
How big should batch size and number of epochs be when fitting a model?
<p>My training set has 970 samples and validation set has 243 samples.</p>
<p>How big should batch size and number of epochs be when fitting a model to optimize the val_acc? Is there any sort of rule of thumb to use based on data input size?</p>