bp.zip_BP Python实现_BP神经网络_python%BP_python预测_神经网络预测

import math import random random.seed(0) def rand(a, b): return (b - a) * random.random() + a def make_matrix(m, n, fill=0.0): mat = [] for i in range(m): mat.append([fill] * n) return mat def sigmoid(x): return 1.0 / (1.0 + math.exp(-x)) def sigmoid_derivative(x): return x * (1 - x) class BPNeuralNetwork: def __init__(self): self.input_n = 0 self.hidden_n = 0 self.output_n = 0 self.input_cells = [] self.hidden_cells = [] self.output_cells = [] self.input_weights = [] self.output_weights = [] self.input_correction = [] self.output_correction = [] def setup(self, ni, nh, no): self.input_n = ni + 1 self.hidden_n = nh self.output_n = no # init cells self.input_cells = [1.0] * self.input_n self.hidden_cells = [1.0] * self.hidden_n self.output_cells = [1.0] * self.output_n # init weights self.input_weights = make_matrix(self.input_n, self.hidden_n) self.output_weights = make_matrix(self.hidden_n, self.output_n) # random activate for i in range(self.input_n): for h in range(self.hidden_n): self.input_weights[i][h] = rand(-0.2, 0.2) for h in range(self.hidden_n): for o in range(self.output_n): self.output_weights[h][o] = rand(-2.0, 2.0) # init correction matrix self.input_correction = make_matrix(self.input_n, self.hidden_n) self.output_correction = make_matrix(self.hidden_n, self.output_n) def predict(self, inputs): # activate input layer for i in range(self.input_n - 1): self.input_cells[i] = inputs[i] # activate hidden layer for j in range(self.hidden_n): total = 0.0 for i in range(self.input_n): total += self.input_cells[i] * self.input_weights[i][j] self.hidden_cells[j] = sigmoid(total) # activate output layer for k in range(self.output_n): total = 0.0 for j in range(self.hidden_n): total += self.hidden_cells[j] * self.output_weights[j][k] self.output_cells[k] = sigmoid(total) return self.output_cells[:] def back_propagate(self, case, label, learn, correct): # feed forward self.predict(case) # get output layer error output_deltas = [0.0] * self.output_n for o in range(self.output_n): error = label[o] - self.output_cells[o] output_deltas[o] = sigmoid_derivative(self.output_cells[o]) * error # get hidden layer error hidden_deltas = [0.0] * self.hidden_n for h in range(self.hidden_n): error = 0.0 for o in range(self.output_n): error += output_deltas[o] * self.output_weights[h][o] hidden_deltas[h] = sigmoid_derivative(self.hidden_cells[h]) * error # update output weights for h in range(self.hidden_n): for o in range(self.output_n): change = output_deltas[o] * self.hidden_cells[h] self.output_weights[h][o] += learn * change + correct * self.output_correction[h][o] self.output_correction[h][o] = change # update input weights for i in range(self.input_n): for h in range(self.hidden_n): change = hidden_deltas[h] * self.input_cells[i] self.input_weights[i][h] += learn * change + correct * self.input_correction[i][h] self.input_correction[i][h] = change # get global error error = 0.0 for o in range(len(label)): error += 0.5 * (label[o] - self.output_cells[o]) ** 2 return error def train(self, cases, labels, limit=10000, learn=0.05, correct=0.1): for j in range(limit): error = 0.0 for i in range(len(cases)): label = labels[i] case = cases[i] error += self.back_propagate(case, label, learn, correct) def test(self): cases = [ [0, 0], [0, 1], [1, 0], [1, 1], ] labels = [[0], [1], [1], [0]] self.setup(2, 5, 1) self.train(cases, labels, 10000, 0.05, 0.1) for case in cases: print(self.predict(case)) if __name__ == '__main__': nn = BPNeuralNetwork() nn.test()