Usage to build CNN Network about numpy-ml HOT 4 OPEN

Unfortunately there really is no good high-level documentation at this point. This is on my TODO list, but is likely to take some time as there's a lot to document ;)

For your particular case, there are two examples of how you might go about building a full network in the models section.

In general, models using this code are going to be quite slow in comparison to any keras/tf/torch/theano implementations - the code here is optimized for readability over speed / efficiency. That said, I think it's a great idea to have some simple examples to show how the NN code corresponds to other packages.

from numpy-ml.

In general, if you want to implement a model, you'll probably want the following methods as a bare-minimum:

_build_network(self, ...):
    # initialize the network layers and store them within an 
    # OrderedDict so you can reliably iterate over them during the 
    # forward / backward passes

forward(self, X):
    # perform a forward pass. this is where the specific model architecture comes
    # into play, since you'll need to define how outputs from early layers flow to 
    # inputs of subsequent layers

backward(self, dLdy):
    # perform a backward pass. again, the route the gradients take through the network
    # will be specific to the particular model architecture

from numpy-ml.

So basically numpy-ml follows some kind of PyTorch way of building a model, right?

from numpy-ml.

Yeah, more or less. The major difference is that this code won't have a built-in backward method - you have to implement it yourself for each model

from numpy-ml.