Comments (5)
pretrain之后也没打到论文上的0.4325,望告知
from attentional_factorization_machine.
pretrain之后也没打到论文上的0.4325,望告知
你好,很抱歉目前当时实验所用的环境已丢失,暂时无法完全复现。我让当时负责实验的同学稍微重新tune了一下参数, batch_size改为2048,lambda_attention改为32时,启用pretrain后ml-tag上RSME可以达到0.4375,不过和当时实验出的0.4325左右还是有一定差距。推测原因可能和tensorflow版本以及pretrain模型的质量有关系。我们这边尽可能找回当时实验所用的pretrain模型再试试。
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何老师,您好!我在使用这份代码的时候,对于FM可以复现出论文的结果;但对于AFM,目前在val上取得的最好RMSE为 0.4639,对应参数为:--epoch 100 --pretrain 1 --batch_size 4096 --hidden_factor [8,256] --keep [1.0,0.5] --lamda_attention 2.0 --lr 0.01 --batch_norm 0,而论文中得到的最好RMSE为0.433左右,请问我有哪些地方处理得有问题吗?
你好,很抱歉目前当时实验所用的环境已丢失,暂时无法完全复现。我让当时负责实验的同学稍微重新tune了一下参数, batch_size改为2048,lambda_attention改为32时,启用pretrain后ml-tag上RSME可以达到0.4375,不过和当时实验出的0.4325左右还是有一定差距。推测原因可能和tensorflow版本以及pretrain模型的质量有关系。我们这边尽可能找回当时实验所用的pretrain模型再试试。
from attentional_factorization_machine.
pretrain之后也没打到论文上的0.4325,望告知
你好,很抱歉目前当时实验所用的环境已丢失,暂时无法完全复现。我让当时负责实验的同学稍微重新tune了一下参数, batch_size改为1024,lambda_attention改为32时,启用pretrain后ml-tag上RSME可以达到0.4391,不过和当时实验出的0.4325左右还是有一定差距。推测原因可能和tensorflow版本以及pretrain模型的质量有关系。我们这边尽可能找回当时实验所用的pretrain模型再试试。
好,谢谢老师
from attentional_factorization_machine.
pretrain之后也没打到论文上的0.4325,望告知
你好,很抱歉目前当时实验所用的环境已丢失,暂时无法完全复现。我让当时负责实验的同学稍微重新tune了一下参数, batch_size改为1024,lambda_attention改为32时,启用pretrain后ml-tag上RSME可以达到0.4391,不过和当时实验出的0.4325左右还是有一定差距。推测原因可能和tensorflow版本以及pretrain模型的质量有关系。我们这边尽可能找回当时实验所用的pretrain模型再试试。
我想再请问下,这个AFM中的二次交叉项与FFM中的二次交叉有什么区别吗
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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?
- 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
- AFM训练中出现nan HOT 3
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