neural_nets.py.py

#
#Imports
#
import numpy as np

class BackPropagationNetwork:
    """A back-propagation-network"""

    #
    # Class members
    #
    layerCount = 0
    shape =None
    weights = []

    #
    #class methods
    #
    def __init__(self, layerSize):
        """initialize the network"""
        # Layer info
        self.layerCount = len(layerSize)-1
        self.shape = layerSize

        # Input/Output data from last run
        self.layerInput = []
        self._layerOutput =[]

        # create the weight arrays
        for (l1, l2) in zip(layerSize[:-1], layerSize[1:]):
            self.weights.append(np.random.normal(scale =0.1, size =(l2, l1+1)))

#
# If run as script, create a test object
#
if __name__ == "__main__":
    bpn = BackPropagationNetwork((2,2,1))
    print(bpn.shape)
    print(bpn.weights)