Question: for dataset
zh794390558 opened this issue · comments
- dataet
'''
test_data, test_label, valid_data, valid_label, Valid_label, Test_label, pernums_test, pernums_valid = load_data()
'''
请问 Valid_label, Test_label, pernums_test, pernums_valid 这些是用来做什么的?
能给一组参数复现你paper中的结果吗?
- evaluation
样本不均衡问题: 我看代码里是每个 class 取了 300 个 sentence 吧。
但是我的 eval 中,所有样本倾向预测 class 0 , 请问你知道是什么原因吗?
----------segment metrics---------------
Best valid_UA: 0.2666
Best valid_WA: 0.09396
Valid Confusion Matrix:["ang","sad","hap","neu"]
[[ 34 0 0 0]
[107 0 0 6]
[ 70 0 0 2]
[207 0 0 10]]
----------segment metrics---------------
*****************************************************************
310
Epoch: 310
Valid cost: 1.52
Valid_UA: 0.2666
Valid_WA: 0.09396
Best valid_UA: 0.2666
Best valid_WA: 0.09396
Valid Confusion Matrix:["ang","sad","hap","neu"]
[[ 18 0 0 0]
[ 67 0 0 6]
[ 54 0 0 2]
[141 0 0 10]]
Test_UA: 0.2592
Test_WA: 0.0695
Test Confusion Matrix:["ang","sad","hap","neu"]
[[ 13 0 0 0]
[ 58 0 0 2]
[ 50 0 0 0]
[131 0 0 5]]
*****************************************************************
下面是训练时的打印:
----------segment metrics---------------
valid_UA: 0.4773
valid_WA: 0.3968
Valid Confusion Matrix:["ang","sad","hap","neu"]
[[28 0 6 0]
[ 3 59 22 29]
[28 11 18 15]
[63 34 52 68]]
----------segment metrics---------------
After epoch:9, step: 310, loss on training batch is 0.44, accuracy is 0.900.
train_UA: 0.8941
train_WA: 0.9
Confusion Matrix:["ang","sad","hap","neu"]
[[ 6 0 1 1]
[ 0 15 0 1]
[ 0 0 7 0]
[ 0 0 1 8]]
+ [[ 1 -le 2 ]]
1、Test_label是指每个Test utterance的情感标签,因为可能一句话时间大于3秒,被截成两段了,而pernums_test是指这句Test utterance包含多少个子段。
2、在IEMOCAP中生气是最难区分的情感,在论文里我是将样本数量最少的两种情感上采样了一次,这份代码不能完全复现我的论文,框架是一样的,但是数据处理有变化
Hi, I used Google translation on your answer, "但是数据处理有变化"
Does it mean this open code is not the same as the original paper code?
.. I can't find the technique for the data processing on your paper.
Can you tell us what is the changed processing?