def test_multilayer_perceptron_train(self): # mlnn = MultiLayerNeuralNetwork([2, 2, 1], threshold=0.5, learning_coefficient=0.5, sigmoid_alpha=5) mlnn = MultiLayerNeuralNetwork([2, 4, 1], threshold=0.5, learning_coefficient=0.5, sigmoid_alpha=5, print_error=False) #mlnn = MultiLayerNeuralNetwork([2, 4, 4, 1], threshold=0.5, learning_coefficient=0.5, sigmoid_alpha=5) train_data = [ [[0,0],[1]], [[1,0],[0]], [[0,1],[0]], [[1,1],[1]], ] # train mlnn.train(train_data) # print train_data # print mlnn.weights # print mlnn.predict([0, 0]) # print mlnn.predict([1, 0]) # print mlnn.predict([0, 1]) # print mlnn.predict([1, 1]) assert mlnn.predict([0, 0]) > mlnn.predict([1, 0]) assert mlnn.predict([0, 0]) > mlnn.predict([0, 1]) assert mlnn.predict([1, 1]) > mlnn.predict([1, 0]) assert mlnn.predict([1, 1]) > mlnn.predict([0, 1])
def multilayer_perceptron(): mlnn = MultiLayerNeuralNetwork( [2, 4, 1], threshold=0.5, learning_coefficient=0.5, sigmoid_alpha=5) x_range = [0,10] y_range = [0,10] # liner_data = TrainingData.liner_training_data(x_range, y_range) #liner_data = TrainingData.quadratic_function_data(x_range, y_range) liner_data = TrainingData.sin_function_data(x_range, y_range, 5) train_data = TrainingData.change_format(liner_data) # 教師データのプロット fig = plt.figure() scat(fig, [key for key, value in liner_data.items() if value == 0], color='g' ) scat(fig, [key for key, value in liner_data.items() if value == 1], color='b' ) # 学習 sample_border = len(train_data) random.shuffle(train_data) #mlnn.train(train_data[:20]) mlnn.train(train_data[:sample_border]) # xに対応するyを算出 data = get_predict_list(x_range,y_range, mlnn) # 学習後分離線 plot(fig, data) plt.show()