NeuralNetwork.py

import numpy as np
# the sigmoid function scipy.special.expit()
from scipy.special import expit


# neural network class definition
class NeuralNetwork:

    # initialise the neural network
    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
        # set number of nodes in each input, hidden, output layer
        self.inodes = inputnodes
        self.hnodes = hiddennodes
        self.onodes = outputnodes

        # link weight matrices, wih and who
        # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
        # w11 w21
        # w12 w22 etc
        self.wih = np.random.rand(self.hnodes, self.inodes) - 0.5
        self.who = np.random.rand(self.onodes, self.hnodes) - 0.5
        # normal distribution
        self.wih = np.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
        self.who = np.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))

        # learning rate
        self.lr = learningrate

        # activation function is the sigmoid function
        self.activation_function = lambda x: expit(x)

        pass

    # train the neural network
    def train(self, inputs_list, targets_list):
        # convert inputs list to 2d array
        inputs = np.array(inputs_list, ndmin=2).T
        targets = np.array(targets_list, ndmin=2).T

        # calculate signals into hidden layer
        hidden_inputs = np.dot(self.wih, inputs)
        # calculate the signals emerging from hidden layer
        hidden_outputs = self.activation_function(hidden_inputs)

        # calculate signals into final output layer
        final_inputs = np.dot(self.who, hidden_outputs)
        # calculate the signals emerging from final output layer
        final_outputs = self.activation_function(final_inputs)

        # output layer error is the (target - actual)
        output_errors = targets - final_outputs
        # hidden layer error is the output_errors, split by weights, recombined at hidden nodes
        hidden_errors = np.dot(self.who.T, output_errors)

        # update the weights for the links between the hidden and output layers
        self.who += self.lr * np.dot((output_errors * final_outputs * (1.0 - final_outputs)),
                                     np.transpose(hidden_outputs))

        # update the weights for the links between the input and hidden layers
        self.wih += self.lr * np.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
                                     np.transpose(inputs))

        pass

    # query the neural network
    def query(self, inputs_list):
        # convert inputs list to 2d array
        inputs = np.array(inputs_list, ndmin=2).T

        # calculate signals into hidden layer
        hidden_inputs = np.dot(self.wih, inputs)
        # calculate the signals emerging from hidden layer
        hidden_outputs = self.activation_function(hidden_inputs)

        # calculate signals into final output layer
        final_inputs = np.dot(self.who, hidden_outputs)
        # calculate the signals emerging from final output layer
        final_outputs = self.activation_function(final_inputs)

        return final_outputs


if __name__ == '__main__':
    # number of input, hidden and output nodes
    input_nodes = 3
    hidden_nodes = 3
    output_nodes = 3

    # learning rate is 0.5
    learning_rate = 0.5

    # create instance of neural network
    n = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
    print(n.query([1.0, 0.5, -1.5]))