我在尝试提高用Python编写的前馈神经网络的准确性时遇到了问题。我不确定这是一个真正的错误还是我的数学函数的问题，但我得到了一些模糊的输出（例如0.5）。无论我如何增加迭代次数，我的代码如下：

from numpy import exp, array, random, dotclass NeuralNetwork():    def __init__(self):        random.seed(1)        self.synaptic_weights = 2 * random.random((3, 1)) - 1     # MM reuslt = 3 (3 * 1)    def Sigmoid(self, x):        return 1 / (1 + exp(-x))    def Sigmoid_Derivative(self, x):        return x * (1 - x)    def train(self, Training_inputs, Training_outputs, iterations):        output = self.think(Training_inputs)        print ("THe outputs are: -", output)        erorr = Training_outputs - output        adjustment = dot(Training_inputs.T, erorr * self.Sigmoid_Derivative(output))        print ("The adjustments are:-", adjustment)        self.synaptic_weights += output    def think(self, inputs):        Training_inputs = array(inputs)        return self.Sigmoid(dot(inputs, self.synaptic_weights))# phew! the class ends..if __name__ == "__main__":    neural_network = NeuralNetwork()    print("Random startin weights", neural_network.synaptic_weights)    Training_inputs = array([[1, 1, 1],                              [0, 0, 0],                              [1, 0, 1],])                 # 3 rows * 3 columns???    Training_outputs = array([[1, 1, 0]]).T    neural_network.train(Training_inputs, Training_outputs, 0)    print ("New synaptic weights after training: ")    print (neural_network.synaptic_weights)    # Test the neural network with a new situation.    print ("Considering new situation [1, 0, 0] -> ?: ")    print (neural_network.think(array([1, 0, 0])))

以下是我的输出结果：

[Running] python -u "/home/neel/Documents/VS-Code_Projects/Machine_Lrn(PY)/test.py"Random startin weights [[-0.16595599] [ 0.44064899] [-0.99977125]]THe outputs are: - [[0.3262757 ] [0.5       ] [0.23762817]]The adjustments are:- [[0.10504902] [0.14809799] [0.10504902]]New synaptic weights after training: [[ 0.16031971] [ 0.94064899] [-0.76214308]]Considering new situation [1, 0, 0] -> ?: [0.5399943][Done] exited with code=0 in 0.348 seconds[Running] python -u "/home/neel/Documents/VS-Code_Projects/Machine_Lrn(PY)/tempCodeRunnerFile.py"Random startin weights [[-0.16595599] [ 0.44064899] [-0.99977125]]THe outputs are: - [[0.3262757 ] [0.5       ] [0.23762817]]The adjustments are:- [[0.10504902] [0.14809799] [0.10504902]]New synaptic weights after training: [[ 0.16031971] [ 0.94064899] [-0.76214308]]Considering new situation [1, 0, 0] -> ?: [0.5399943][Done] exited with code=0 in 3.985 seconds

我尝试过更改迭代次数，但差异非常小。我认为问题可能出在我其中一个数学（Sigmoid）函数上。除此之外，我认为第20行的点乘运算可能也是问题所在，因为调整看起来不太对劲……

另外，0.5是否表明我的网络没有在学习，而只是在进行随机猜测？

附注： 我认为我的问题并不是重复的，因为它涉及到所述模型的‘准确性’，而链接的问题涉及到‘非预期输出’

回答：

你的 Sigmoid_Derivative 函数是错误的，这一点在你之前的问题中已经指出了；它应该是这样的：

def Sigmoid_Derivative(self, x):    return self.Sigmoid(x) * (1-self.Sigmoid(x))

请参阅Math.SE上的Sigmoid函数的导数讨论串，以及这里的讨论。

如果纠正这一点后仍然没有得到预期的结果，请不要更改上面的问题 – 而是开启一个新问题…