def sample(): X = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) y = np.array([[0], [1], [1], [0]]) nn = NeuralNetwork(X, y) for i in range(1500): nn.feedforward() nn.backprop() print(nn.output)
error_count += error if error_count == 1: network.train(data, resp) generation += 1 score = 0 error_count = 0 ball_pos = ball.position[0] - SCREEN_SIZE[0] / 2 bar_pos = bar.position[0] - SCREEN_SIZE[0] / 2 bar_pos += bar.size[0] / 2 ball_pos = ball_pos / 100 bar_pos = bar_pos / 100 direction = network.feedforward(np.array([ball_pos, bar_pos])) direction = 1 if direction > 0.5 else 0 ''' df.loc[index] = [ball_pos, bar_pos, direction] index += 1 ''' bar.move(direction) text = font.render('Score: ' + str(score), 1, (255, 255, 255)) text2 = font.render('Geração: ' + str(generation), 1, (255, 255, 255)) screen.fill((0, 0, 0)) screen.blit(text, (10, 10)) screen.blit(text2, (10, 40))