Neuroph Vs Encog

Question

I have decided to use a feed-forward NN with back-propagation training for my OCR application for Handwritten text and the input layer is going to be with 32*32 (1024) neurones and at least 8-12 out put neurones.

I found Neuroph easy to use by reading some articles at the same time Encog is few times better in performance. Considering the parameters in my scenario which API is the most suitable one. And I appreciate if u can comment on the number of input nodes i have taken, is it too large value (Although it is out of the topic)

Answer 1

First my disclaimer, I am one of the main developers on the Encog project. This means I am more familiar with Encog that Neuroph and perhaps biased towards it. In my opinion, the relative strengths of each are as follows. Encog supports quite a few interchangeable machine learning methods and training methods. Neuroph is VERY focused on neural networks and you can express a connection between just about anything. So if you are going to create very custom/non-standard (research) neural networks of different typologies than the typical Elman/Jordan, NEAT, HyperNEAT, Feedforward type networks, then Neuroph will fit the bill nicely.