python - 如何提高前馈神经网络的准确性？

Question

我在提高用 python 编码的前馈神经网络的准确性时遇到问题。我不确定这是一个真正的错误还是只是我的数学函数的无能，但我得到的输出不明确(例如 0.5)，无论我增加多少迭代......我的代码:-

from numpy import exp, array, random, dot

class 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])))

虽然这些是我的输出:=>

[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，但差异非常小。我认为问题可能出在我的数学(Sigmoid)函数之一上。除此之外，我认为第 20 行的点乘法可能是一个问题，因为这些调整对我来说看起来很不稳定......

此外，0.5 是否表明我的网络没有学习，因为它只是进行随机猜测？

PS:-我认为我的问题不是重复的，因为它涉及所述模型的“准确性”，而链接的问题涉及“不需要的输出”

Answer 1

您的Sigmoid_Derivative函数是错误，这已经在previous question of yours中指出了。 ;应该是:

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

请参阅Derivative of sigmoid function Math.SE 的线程以及讨论 here .

如果更正此问题仍然没有给出预期结果，请不要更改上面的问题 - 相反，打开一个新问题...