dselsam / neurosat Goto Github PK

Hello, I notice you used PCA to get knowledge of what's happening during iterations. So I wonder if it's necessary to do PCA to the data, then use k-means to decode, or just use k-means.

I am new to this field, sorry to bother you if I propose a stupid question.

Hi
It'd be useful if you could provide an example dataset at least for the toy example. The training script for this one is referring to data/train/sr5.

Thanks
Ben.

Hi,

Is there any script which can illustrate the procedure to generate different problems, such as six different random graph distributions and graph coloring problems (3 ≤ k ≤ 5), dominating-set problems (2 ≤ k ≤ 4)), clique-detection problems (3 ≤ k ≤ 5), and vertex cover problems (4 ≤ k ≤ 6).

Thanks!

Hi --

I see the disclaimer about how this repo doesn't include the code to reproduce the experiments in the paper, but are you able to sketch out what I'd have to do to reproduce some of those experiments? (In particular, I'm most interested in reproducing the results in Table 2, where you show that the learned solver can be applied to SAT-encoded version of other NP problems, but also in the SR(U(40)) experiments)

EDIT: More specifically, a couple of things that could help get me off the ground -- For the experiments described in Table 1, how many problem instances did you train on? How many epochs of training?

Thanks
Ben

Sorry for interrupting you when you are busy with working, I'm trying to modify this network to do some experiment, however I can't get normal result. I haven changed the size of problem by modify gen_data.py,but the result are as the same. For example, the loss are always 0.6931 and the matrix are always 50% accuracy. I wonder how can I get some normal result!

Training
Loading data/train/sr5/data_dir=grp1_npb=60000_nb=8.pkl...
[0] 0.6932 (0.30, 0.20, 0.30, 0.20) [42s]
Start Trian
Loading data/train/sr5/data_dir=grp2_npb=60000_nb=10.pkl...
[1] 0.6932 (0.25, 0.25, 0.25, 0.25) [43s]
Start Trian
Loading data/train/sr5/data_dir=grp3_npb=60000_nb=6.pkl...
[2] 0.6932 (0.20, 0.30, 0.20, 0.30) [43s]
Start Trian
Loading data/train/sr5/data_dir=grp8_npb=60000_nb=7.pkl...
[3] 0.6932 (0.30, 0.20, 0.30, 0.20) [53s]
Start Trian
Loading data/train/sr5/data_dir=grp9_npb=60000_nb=7.pkl...
[4] 0.6932 (0.20, 0.30, 0.20, 0.30) [44s]

Test:
data/test/sr5/data_dir=grp8_npb=60000_nb=9.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp2_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp10_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp5_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp1_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp9_npb=60000_nb=10.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp3_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp6_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp4_npb=60000_nb=8.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)
data/test/sr5/data_dir=grp7_npb=60000_nb=9.pkl 0.6932 (0.50, 0.00, 0.50, 0.00)

I find that in the toy examples, you set this hyper-parameter as 60000, however, in the paper, you set this as 12000, which is smaller than 60000.

From my understanding, more nodes mean more expressive representation. I am wondering if my understanding correct? And why you set this hyper-parameter in the toy examples.

Thanks!

scripts/toy_test.sh references the file python/testate.py which does not exist -- is this supposed to be python/validate.py?

Thanks
Ben