|
Oh God, the horror. Almost every time neural network library I've seen manages to obscure a very simple idea in piles of useless objects. "Neural networks" are just the composition of several nonlinear regressions. There's nothing particularly "neural" about them. Here's a typical 3-layer network: f(x,Wh,Wo) = tanh(Wo * tanh(Wh * X)) Wh, and Wo are the hidden and output weight matrices respectively. Fix some loss function (ie, L(x,Wh,Wo,y) = || f(x,Wh,Wo) - y||^2), get the gradient of this function, and a take step down the gradient. There's your learning rule. Now, I understand the desire for flexibility/modularity, but (1) what's the sense of trying to house supervised and unsupervised methods in the same hierarchy?
and
(2) what could possibly justify Connection and Weight objects? |