实验不同初始化参数、正则化方式的影响
实验不同初始化参数、正则化方式的影响
data.mat 提取密码：mp2q
L2正则化的结果
dropout的结果
L2正则化是在成本函数中增加一项L2范数，会改变成本的计算，以及后向传播
Dropout是随机失活，一般是在神经元较密集的层采用，神经元较少的层不采用，通过一个超参数keep_prob进行控制。失活率为1-keep_prob。会改变前向传播和后向传播。
1.初始化矩阵D1 = np.random.rand(..., ...)和Al相同维度，其数都是0-1之间的D1 = np.random.rand(A1.shape[0], A1.shape[1])
2.将D1的值转换为0或1（使用keep_prob作为阈值 D1 = D1 < keep_prob
3.舍弃A1的一些节点（将它的值变为0或False）A1 = A1 * D1
4.缩放未舍弃的节点(不为0)的值 A1 = A1 / keep_prob
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