sknn multi layer perceptron classifier
Question: I am using the following neural net classifier in python

    
    
    from sknn.mlp import Layer,Classifier
    
    nn = mlp.Classifier(
                layers=[
                    mlp.Layer("Tanh", units=n_feat/8),
                    mlp.Layer("Sigmoid", units=n_feat/16),
                    mlp.Layer("Softmax", units=n_targets)],
                n_iter=50,
                n_stable=10,
                batch_size=25,
                learning_rate=0.002,
                learning_rule="momentum",
                valid_size=0.1,
                verbose=1)
    

which is working just fine.My question is that how to proceed if I require for
example 100,200 or 500 hidden layers? Do I have to specify each layer here
manually or someone has better Idea in python for MLP?


Answer: You could create some loop-based mechanism to build the list of layers I
suppose, but there's a bigger issue here. A standard MLP with hundreds of
layers is likely to be _extremely_ expensive to train - both in terms of
computational speed as well as memory usage. MLPs typically only have one or
two hidden layers, or occasionally a few more. But for problems that can truly
benefit from more hidden layers, it becomes important to incorporate some of
the lessons learned in the field of [deep
learning](https://en.wikipedia.org/wiki/Deep_learning). For example, for
object classification on images, using all fully-connected layers is
incredibly inefficient, because you're interested in identifying spatially-
local patterns, and therefore interactions between spatially-distant pixels or
regions is largely noise. (This is a perfect case for using a deep
convolutional neural net.)

Although some _very_ deep networks have been created, it's worth pointing out
that even Google's very powerful Inception-v3 model is only 42-layers deep.
Anyway, if you're interested in building deep models, I'd recommend reading
this [Deep Learning book](http://www.deeplearningbook.org/). From what I've
read of it, it seems to be a very good introduction. Hope that helps!

