neural_network.py

import scipy.special
import numpy
class neuralNetwork:

    def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
        self.inodes = inputnodes
        self.hnodes = hiddennodes
        self.onodes = outputnodes
        self.wih = numpy.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes,self.inodes))
        self.who = numpy.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes))
        self.lr = learningrate
        self.activation_function = lambda x: scipy.special.expit(x)

        pass




    def train(self,inputs_list,target_lists):
        inputs = numpy.array(inputs_list,ndmin = 2).T
        targets = numpy.array(target_lists,ndmin = 2).T
        hidden_inputs = numpy.dot(self.wih,inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        final_inputs = numpy.dot(self.who,hidden_outputs)
        final_outputs = self.activation_function(final_inputs)
        output_errors = targets - final_outputs
        hidden_errors = numpy.dot(self.who.T,output_errors)
        self.who += self.lr*numpy.dot((output_errors*final_outputs*(1-final_outputs)),numpy.transpose(hidden_outputs))
        self.wih += self.lr*numpy.dot((hidden_errors*hidden_outputs*(1-hidden_outputs)),numpy.transpose(inputs))



        pass


    def query(self,inputs_list):
        inputs = numpy.array(inputs_list,ndmin = 2).T
        hidden_inputs = numpy.dot(self.wih,inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        final_inputs = numpy.dot(self.who,hidden_outputs)
        final_outputs = self.activation_function(final_inputs)
        return final_outputs