NeuralNetwork.py

import numpy as np
class NeuralNetwork():
    def __init__(self, input_size):
        np.random.seed(1)
        self.synaptic_weights = 2 * np.random.random((input_size, 1)) - 1

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def train(self, training_inputs, training_outputs, training_iterations):
        for iteration in range(training_iterations):
            # Pass training set through the neural network
            output = self.think(training_inputs)
            
            # Calculate the error rate
            error = training_outputs - output

            # Multiply error by input and gradient of the sigmoid function
            # Less confident weights are adjusted more through the nature of the function
            adjustments = np.dot(training_inputs.T, error * self.sigmoid_derivative(output))

            # Adjust synaptic weights
            self.synaptic_weights += adjustments

    def think(self, inputs):        
        inputs = inputs.astype(float)
        output = self.sigmoid(np.dot(inputs, self.synaptic_weights))
        return output