调试反向传播算法

Question

我试图在python中使用numpy实现反向传播算法。我一直在使用本站实现矩阵形式的反向传播.在XOR上测试此代码时，即使在多次运行数千次迭代之后，我的网络也不会收敛。我认为有某种逻辑上的错误。如果有人愿意看一看，我将非常感激。完全可运行的代码可以在github中找到

import numpy as np

def backpropagate(network, tests, iterations=50):

    #convert tests into numpy matrices
    tests = [(np.matrix(inputs, dtype=np.float64).reshape(len(inputs), 1),
            np.matrix(expected, dtype=np.float64).reshape(len(expected), 1))
            for inputs, expected in tests]

    for _ in range(iterations):

        #accumulate the weight and bias deltas
        weight_delta = [np.zeros(matrix.shape) for matrix in network.weights]
        bias_delta = [np.zeros(matrix.shape) for matrix in network.bias]

        #iterate over the tests
        for potentials, expected in tests:

            #input the potentials into the network
            #calling the network with trace == True returns a list of matrices,
            #representing the potentials of each layer 
            trace = network(potentials, trace=True)
            errors = [expected - trace[-1]]

            #iterate over the layers backwards
            for weight_matrix, layer in reversed(list(zip(network.weights, trace))):
                #compute the error vector for a layer
                errors.append(np.multiply(weight_matrix.transpose()*errors[-1],
                                          network.sigmoid.derivative(layer)))

            #remove the input layer
            errors.pop()
            errors.reverse()

            #compute the deltas for bias and weight
            for index, error in enumerate(errors):
                bias_delta[index] += error
                weight_delta[index] += error * trace[index].transpose()

        #apply the deltas
        for index, delta in enumerate(weight_delta):
            network.weights[index] += delta
        for index, delta in enumerate(bias_delta):
            network.bias[index] += delta

此外，下面是计算输出的代码和我的sigmoid函数。bug不太可能出现在这里；我能够训练一个网络来模拟XOR，使用模拟退火。

# the call function of the neural network
def __call__(self, potentials, trace=True):

    #ensure the input is properly formated
    potentials = np.matrix(potentials, dtype=np.float64).reshape(len(potentials), 1)

    #accumulate the trace
    trace = [potentials]

    #iterate over the weights
    for index, weight_matrix in enumerate(self.weights):
        potentials = weight_matrix * potentials + self.bias[index]
        potentials = self.sigmoid(potentials)
        trace.append(potentials)

    return trace

#The sigmoid function that is stored in the network
def sigmoid(x):
    return np.tanh(x)
sigmoid.derivative = lambda x : (1-np.square(x))

Answer 1

问题是缺少的步长参数。梯度应该是额外的缩放，而不是一次在权值空间中完成整个步骤。所以，它应该是：network.weights[index] += delta和network.bias[index] += delta，而不是：

def backpropagate(network, tests, stepSize = 0.01, iterations=50):

    #...

    network.weights[index] += stepSize * delta

    #...

    network.bias[index] += stepSize * delta