Comments (3)
from attentional_factorization_machine.
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
from attentional_factorization_machine.
from attentional_factorization_machine.
Related Issues (20)
- glorot初始化 HOT 5
- can share the code? HOT 4
- Could you please share the code? HOT 1
- Could you share your code?
- 请问预训练FM factor256需要多大内存?
- 关于AFM不能复现论文实验结果 HOT 5
- No module named LoadData
- valid_dimension的含义?
- Why there is little improvement of attenion model in my experiment ? HOT 1
- reduce_sum和softmax有bug? HOT 2
- 为什么ml-tag和frappe数据的label值是1和-1?为什么不是一个0-1之间的评分呢?求解答
- 何老师您好,这个RMSE是评分预测的损失吗
- 关于attention_score 和 interaction_score 的问题??
- 请问batch_norm_layer这个函数似乎源码中并没有用上??
- 实数特征支持问题 HOT 1
- Difference between FM and AFM is not as pronounced as in the original paper HOT 3
- 数据处理代码 HOT 1
- 您好,frappe最优结果的最优参数是? HOT 8
- dropout in validation/evaluation HOT 5
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