attentional_factorization_machine
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hexiangnan
AFM训练中出现nan
nan应该是因为我们自己用exp函数实现了softmax, 导致容易overflow。换用tensorflow实现的softmax应该就不会nan了。
On Sat, Feb 10, 2018 at 11:48 PM, KevinKune [email protected] wrote:
AFM训练起来很容易nan，请问您遇到过这种情况吗？对于调参数有什么建议？哪些参数比较敏感？
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感谢回答，最近发现调节softmax的温度会比较容易训练，在我的数据集上调节温度比attention_lambda更有效，能显著控制过拟合，建议代码中增加调节温度的参数
谢谢告知！
On Thu, Feb 15, 2018 at 2:50 PM, KevinKune [email protected] wrote:
感谢回答，最近发现调节softmax的温度会比较容易训练，在我的数据集上调节温度比attention_lambda更有效，能显著控制过拟合， 建议代码中增加调节温度的参数
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-- Best Regards, Xiangnan He