python neural network

neural.py

import numpy
from scipy.special import expit as sigmoid

class neural_network:

    def __init__(self, nodes, learning_rate):

        self.nodes = nodes
        self.layers = len(nodes)
        self.lr = learning_rate
        
        self.weights = []

        for i in range(self.layers - 1):
            array = numpy.random.normal(0.0, pow(nodes[i + 1], -0.5), (nodes[i + 1], nodes[i]))
            self.weights.append(array)


    def train(self, inputs, targets):

        targets = numpy.array(targets, ndmin=2).T
        result = self.query(inputs)
        error = targets - result[-1]
        
        for i in range(1, self.layers):

            previous = result[-i - 1]
            z = result[-i]
            delta = self.lr * numpy.dot(error * z * (1.0 - z), previous.T)
            
            # next layer's error
            error = numpy.dot(self.weights[-i].T, error)

            self.weights[-i] += delta

    def query(self, inputs):

        output = numpy.array(inputs, ndmin=2).T        
        result = [output]
        
        for w in self.weights:
            output = sigmoid(numpy.dot(w, output))
            result.append(output)

        return result