MMMMMua.py 文件源码

def Back_Propagation():
    global In_param
    global Out_param
    global thres_in
    global thres_out

    rate = 0.1

    for epoch in range(50000):
        for id, item in enumerate(traits):

            hid_In    = np.array(np.mat(item) * np.mat(In_param))
            hid_Out   = sigmoid(hid_In - thres_in)

            fin_In    = np.array(np.mat(hid_Out) * np.mat(Out_param))
            fin_Out   = sigmoid(fin_In - thres_out)

            g         = fin_Out * (1.0 - fin_Out) * (judge[id] - fin_Out)
            e         = hid_Out * (1.0 - hid_Out) * np.array([np.dot(x, g) for x in Out_param])

            In_param  += np.array(rate * np.matrix(item).T * np.matrix(e))
            Out_param += np.array(rate * np.matrix(hid_Out).T * np.matrix(g))
            thres_in  -= rate * e
            thres_out -= rate * g