title: How do a model's size, depth and number of parameters affect training time?
body: <p>I have found a table describing the size, depth and number of parameters for every model supported by the Keras.Applications module (<a href="https://keras.io/applications/" rel="nofollow noreferrer">https://keras.io/applications/</a> under "Documentation for individual models"). I want to choose a model based on it's accuracy compared to how long it might take to train</p>
  
  <p>I assume the number of parameters directly influences training time, because the more parameters you have the more computationally difficult the gradient descent will be. But some Models, especially VGG16, have a lot of parameters and a very low depth. Or DenseNet121 and Xception, both have very similary depth but very different number of parameters and size in MB.</p>
  
  <p>Do the size and depth also influence how long it takes to train? And if so, in what way? Does training time scale linearly with number of parameters?</p>