title: How to estimate a CoreML model's maximal runtime footprint (in megabytes)
body: <p>Let's say I have a network model made in <code>TensorFlow</code>/<code>Keras</code>/<code>Caffe</code> etc.
  I can use <code>CoreML Converters</code> API to get a CoreML model file (<code>.mlmodel</code>) from it.</p>
  <p>Now, as I have a <code>.mlmodel</code> file, and know <code>input shape</code> and <code>output shape</code>, how can a maximum RAM footprint be estimated?
  I know that a model сan have a lot of layers, their size can be much bigger than input/output shape.</p>
  <p>So the questions are:</p>
  <ol>
  <li>Can be a maximal <code>mlmodel</code> memory footprint be known with some formula/API, without compiling and running an app?</li>
  <li>Is a maximal footprint closer to a memory size of the biggest intermediate layer, or is it closer to a sum of the all layer's sizes?</li>
  </ol>
  <p>Any advice is appreciated.
  As I am new to CoreML, you may give any feedback and I'll try to improve the question if needed.</p>