# File: net.py

# load in Conx support for neural networks
from ConxExtensions import *

# create a basic feedforward backpropagation network
n = BackpropNetwork()

# add layers in the order they will be connected
n.addLayer('input', 2)        # input layer has two units
n.addLayer('output', 1)       # output layer has one unit
n.connect('input', 'output')  # connect the layers together

# learning rate
n.setEpsilon(0.5)
# how often the network reports its total error during training
n.setReportRate(1)
# how close an output value has to be to the target to count as correct
n.setTolerance(0.1)

# specify the dataset to use for learning AND
n.setInputs([[0, 0], [0, 1], [1, 0], [1, 1]])
n.setTargets([[0], [0], [0], [1]])

print "Network is set up"