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I'm playing around with Neural Nets and wanted to make a clean class implementation to handle any size net. Currently, I'm debugging my learning function to deal with 2-Layer networks.

In it's current state using logistic activation:

• It cannot learn values below 0.5
• It cannot handle matrices of input vectors (only single input vectors), this can be implemented later
• If initial weights and bias result in output less than 0.5, it will likely learn towards 0
• Assumption: In "ideal" conditions, it will learn any value between 0.5 and 1 using any combination of binary input
  • This has been tested with 2 and 3 inputs to network
• Does proper forward propagation regardless of number of layers

Here's the relevant code: