feedforward.py

feedforward.py

from matrix import Matrix
import math

class RedeNeural():
    nodes_input = []
    nodes_hidden = []
    nodes_output = []
    bias_in_to_hidden = []
    bias_hidden_to_output = []
    weight_in_to_hidden = []
    weight_in_to_output = []
    learn_rate = 0.5

    def __init__(self, nodes_input, nodes_hidden, nodes_output):
        self.nodes_input = Matrixx().create_matrix(nodes_input, nodes_hidden)
        self.nodes_hidden = Matrixx().create_matrix(nodes_hidden, nodes_output)
        self.nodes_output = Matrixx().create_matrix(nodes_output, 1)
        self.bias_in_to_hidden = Matrixx().create_matrix(nodes_input, nodes_hidden)
        self.bias_hidden_to_output = Matrixx().create_matrix(nodes_hidden, nodes_output)

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

    def feedforward(self, input):
        input = Matrixx().array_to_matrix(input)
        
        hidden = input.multiply(self.nodes_hidden, input)
        hidden = input.add(hidden, self.bias_in_to_hidden, True)
        hidden = list(map(lambda i: list(map(lambda j: self.sigmoid(j), i)), hidden))
        hidden = Matrixx().array_to_matrix(hidden)
        
        output = input.multiply(self.nodes_output, hidden)
        output.data = list(output.data)[0]
        output = input.add_output(output, self.bias_hidden_to_output, True)
        output = list(output[0][0])
        output = map(lambda i: self.sigmoid(i), output)

        print(output)

r = RedeNeural(1, 2, 1)
r.feedforward([1, 2])