Question

我正在尝试使用简单的反向传播和单热编码将2D数据分类为多层神经网络中的3个类。在我将增量学习改为批量学习之后，我的输出收敛到0（[0,0,0]），主要是因为我使用更多数据或更高的学习速度。我不知道是否必须推导出其他东西，或者我是否在代码中犯了一些错误。

for each epoch: #pseudocode
    for each input:
        caluclate hiden neurons activations (logsig)
        calculate output neurons activations (logsig)

        #error propagation   
        for i in range(3):  
            error = (desired_out[i] - aktivations_out[i])
            error_out[i] = error * deriv_logsig(aktivations_out[i])             
        t_weights_out = zip(*weights_out)           
        for i in range(hiden_neurons):  
            sum_error = sum(e*w for e, w in zip(error_out, t_weights_out[i]))               
            error_h[i] =  sum_error * deriv_logsig(input_out[i])

        #cumulate deltas             
        for i in range(len(weights_out)):                               
            delta_out[i] = [d + x * coef * error_out[i] for d, x in zip(delta_out[i],        input_out)]               
        for i in range(len(weights_h)):
            delta_h[i] = [d + x * coef * error_h[i] for d, x in zip(delta_h[i], input)]

    #batch learning after epoch
    for i in range(len(weights_out)):                               
            weights_out[i] = [w + delta for w, delta in zip(weights_out[i], delta_out[i])]
    for i in range(len(weights_h)):
            weights_h[i] = [w + delta for w, delta in zip(weights_h[i], delta_h[i])]

Answer 1

我会尝试一些玩具示例，我确定NN会如何表现并调试我的代码。如果我确定我的代码是有效的NN并且我仍然没有得到好的结果，我会尝试更改NN的参数。但它可能非常耗时，因此我会采用一些更简单的ML技术，例如决策树不是黑盒子作为NN。使用决策树，您应该更容易，更快地找到解决方案。问题是你是否可以在NN以外实现它......

神经网络 - 输出收敛到0，python

1 个答案: