glorotxa / sme Goto Github PK

View Code? Open in Web Editor NEW

250.0 250.0 110.0 1.71 MB

License: Other

Python 100.00%

sme's People

Contributors

Stargazers

Watchers

Forkers

aneesha bordesa ibm88 wangdongfrank nhambletccri chtlp sodasandwich heshizhu fangyw henryslzhao zhichun devsinghsachan dapeng2018 minhlab rockt fanfannothing agduran qiuyuew lngvietthang while519 antoine-tran mouhidine 52nlp codeaudit skaasj lsh5207 ryulee ericalingyuandrexel junwei-pan sandy4321 springbarley jolinxql iboxty eshijia alphaprime lightsilver anukat2015 lakpatashi chan4cc bluetropic nooralahzadeh njust-wind zxlzr vishwajeetkumar93 wellwang vyraun boyangumn collawolley chriszblong itgirls zhouhoo zhenv5 dingboy bensnw shirveon mindis nausheenfatma flyscofield jiajiadf pingjietang yanyushu laxatives liuyepku avneets v-mostafapour leiloong liukangling lily1756 yaoleo mzkhan2000 xitongdashi xun-yang hatleon flyrainkey lrxzhy afcarl darvid7 olifei yshihui wei-he usmanakhtar gzupanda fengyaogong jiajie-mei bryan2chow guixiangyu1 wos1239 hongtaicao wurentidai zhiyuxzh thuydang subbulakshmisubha nwpusunyue admn410 xuerli rebaccamin zmjm4 xeverentx andglf j-bing

sme's Issues

idx2identity doesn't match after slicing for TransE

Converting the relation index into its entity just after load yields reasonable candidate for Freebase predicate (i.e. /base/activism/activist/area_of_activism) but if performed after the pre-defined slicing for TransE it seems to map to a different set of indices, and thus falls on entities rather than predicates.

state.op = 'Unstructured'
testo = load_file(state.datapath + state.dataset + '-test-rel.pkl')
testo = convert2idx(testo)
print(idx2entity[convert2idx(testo)[0]]
# >>> /award/award_nominee/award_nominations./award/award_nomination/award

state.op = 'TransE'
testo = load_file(state.datapath + state.dataset + '-test-rel.pkl')
testo = convert2idx(testo)
if state.op == 'SE' or state.op == 'TransE':
    testo = testo[-state.Nrel:, :]
print(idx2entity[convert2idx(testo)[0]])
# >>> /m/088fh

Maybe I'm missing something?

doubt on the gradients

According to the models, if we use "margincost", we should get their gradients for positive and negative triplet respectively. But in the code of model.py, take #L1208 in "TrainFn1Member" for example, you add them together before compute their gradients. Because "costl" and "costr" share some varibles, I'm wondering if it can work just like the models defined.

ValueError: dimension mismatch with FB15k training

I now fed the data with the right input format into the model, unfortunately I got another exception this time:

Traceback (most recent call last):
  File "FB15k_TransE.py", line 5, in <module>
    nbatches=100, totepochs=500, test_all=10, neval=1000, savepath='FB15k_TransE', datapath='../data/')
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 421, in launch
    FB15kexp(state, channel)
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 269, in FB15kexp
    tmpl, tmpr, tmpo, tmpnl, tmpnr)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/theano/compile/function_module.py", line 871, in __call__
    storage_map=getattr(self.fn, 'storage_map', None))
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/theano/gof/link.py", line 314, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/theano/compile/function_module.py", line 859, in __call__
    outputs = self.fn()
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/theano/gof/op.py", line 912, in rval
    r = p(n, [x[0] for x in i], o)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/theano/sparse/basic.py", line 4121, in perform
    rval = x * y
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/sparse/base.py", line 388, in __rmul__
    return (self.transpose() * tr).transpose()
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/sparse/base.py", line 355, in __mul__
    raise ValueError('dimension mismatch')
ValueError: dimension mismatch
Apply node that caused the error: SparseDot(Eemb, SparseVariable{csr,float64})
Toposort index: 9
Inputs types: [TensorType(float64, matrix), Sparse[float64, csr]]
Inputs shapes: [(50, 16296), (17844, 816)]
Inputs strides: [(130368, 8), 'No strides']
Inputs values: ['not shown', 'not shown']
Outputs clients: [[InplaceDimShuffle{1,0}(SparseDot.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
  File "FB15k_TransE.py", line 5, in <module>
    nbatches=100, totepochs=500, test_all=10, neval=1000, savepath='FB15k_TransE', datapath='../data/')
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 421, in launch
    FB15kexp(state, channel)
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 236, in FB15kexp
    marge=state.marge, rel=False)
  File "/Users/dennisulmer/Desktop/SME-master/model.py", line 1157, in TrainFn1Member
    lhsn = S.dot(embedding.E, inpln).T

HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

What could be the source of this problem?

Links mentioned in readme aren't working

Hello,
can anyone direct me to the contents of the URLs mentioned in the readme file for further help?
Thanks

problems while reproducing results on FB15k

The procedure I follow is
First git clone the repo.
Under the FB15k directory

python FB15k_TransE.py
python FB15k_evaluation.py FB15k_TransE/best_valid_model.pkl

The result I get is

Using gpu device 0: Tesla K40m
/home/cc/jxshi/env/local/lib/python2.7/site-packages/theano/tensor/subtensor.py:114: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  stop in [None, length, maxsize] or
### MICRO:
    -- left   >> mean: 2872.72939, median: 682.0, hits@10: 11.131%
    -- right  >> mean: 2362.83263, median: 399.0, hits@10: 15.427%
    -- global >> mean: 2617.78101, median: 533.0, hits@10: 13.279%
### MACRO:
    -- left   >> mean: 3811.39395, median: 3553.36108, hits@10: 6.763%
    -- right  >> mean: 3343.12054, median: 3118.40843, hits@10: 10.424%
    -- global >> mean: 3577.25725, median: 2691.94641, hits@10: 8.594%

The training log is

Using gpu device 0: Tesla K40m
DD{'ndim': 50, 'test_all': 10, 'loadmodelBi': False, 'loadmodelTri': False, 'nhid': 50, 'lremb': 0.01, 'savepath': 'FB15k_TransE', 'seed': 123, 'marge': 1.0, 'simfn': 'L1', 'neval': 1000, 'dataset': 'FB15k', 'nbatches': 100, 'lrparam': 1.0, 'loademb': False, 'datapath': '../data/', 'Nrel': 1345, 'totepochs': 500, 'rhoL': 5, 'Nent': 16296, 'Nsyn': 14951, 'loadmodel': False, 'rhoE': 1, 'op': 'TransE'}
/home/cc/jxshi/env/local/lib/python2.7/site-packages/theano/tensor/subtensor.py:114: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  stop in [None, length, maxsize] or
batchsize: 4831
nbatches: 100
BEGIN TRAINING
-- EPOCH 10 (14.0093 seconds per epoch):
COST >> 0.7421 +/- 0.2195, % updates: 32.669%
    MEAN RANK >> valid: 2618.799, train: 1588.12
        ##### NEW BEST VALID >> test: 2506.168
    (the evaluation took 62.386 seconds)
-- EPOCH 20 (14.357 seconds per epoch):
COST >> 0.5985 +/- 0.0286, % updates: 28.821%
    MEAN RANK >> valid: 2573.779, train: 1479.3145
        ##### NEW BEST VALID >> test: 2419.6415
    (the evaluation took 69.338 seconds)
-- EPOCH 30 (14.5237 seconds per epoch):
COST >> 0.59 +/- 0.0283, % updates: 28.66%
    MEAN RANK >> valid: 2596.0705, train: 1507.8925
    (the evaluation took 41.674 seconds)
-- EPOCH 40 (13.9154 seconds per epoch):
COST >> 0.5868 +/- 0.028, % updates: 28.612%
    MEAN RANK >> valid: 2525.7585, train: 1435.894
        ##### NEW BEST VALID >> test: 2480.78
    (the evaluation took 60.28 seconds)
-- EPOCH 50 (14.035 seconds per epoch):
COST >> 0.5861 +/- 0.0288, % updates: 28.612%
    MEAN RANK >> valid: 2544.1805, train: 1451.6185
    (the evaluation took 39.961 seconds)
-- EPOCH 60 (13.8575 seconds per epoch):
COST >> 0.5857 +/- 0.0278, % updates: 28.615%
    MEAN RANK >> valid: 2580.475, train: 1557.4625
    (the evaluation took 41.311 seconds)
-- EPOCH 70 (13.9798 seconds per epoch):
COST >> 0.586 +/- 0.0273, % updates: 28.638%
    MEAN RANK >> valid: 2547.1255, train: 1464.591
    (the evaluation took 41.477 seconds)
-- EPOCH 80 (13.6954 seconds per epoch):
COST >> 0.585 +/- 0.0284, % updates: 28.604%
    MEAN RANK >> valid: 2553.614, train: 1504.0965
    (the evaluation took 40.815 seconds)
-- EPOCH 90 (13.5098 seconds per epoch):
COST >> 0.5851 +/- 0.0283, % updates: 28.626%
    MEAN RANK >> valid: 2519.132, train: 1569.168
        ##### NEW BEST VALID >> test: 2399.125
    (the evaluation took 61.435 seconds)
-- EPOCH 100 (13.6294 seconds per epoch):
COST >> 0.5851 +/- 0.0271, % updates: 28.613%
    MEAN RANK >> valid: 2510.854, train: 1510.5975
        ##### NEW BEST VALID >> test: 2461.665
    (the evaluation took 65.216 seconds)
-- EPOCH 110 (13.6886 seconds per epoch):
COST >> 0.5851 +/- 0.0279, % updates: 28.605%
    MEAN RANK >> valid: 2578.334, train: 1477.527
    (the evaluation took 44.238 seconds)
-- EPOCH 120 (13.6551 seconds per epoch):
COST >> 0.5854 +/- 0.0277, % updates: 28.615%
    MEAN RANK >> valid: 2563.495, train: 1482.032
    (the evaluation took 42.621 seconds)
-- EPOCH 130 (13.7041 seconds per epoch):
COST >> 0.5843 +/- 0.0275, % updates: 28.606%
    MEAN RANK >> valid: 2549.267, train: 1493.6845
    (the evaluation took 42.67 seconds)
-- EPOCH 140 (13.9192 seconds per epoch):
COST >> 0.585 +/- 0.0281, % updates: 28.606%
    MEAN RANK >> valid: 2534.716, train: 1474.618
    (the evaluation took 40.11 seconds)
-- EPOCH 150 (14.1649 seconds per epoch):
COST >> 0.5853 +/- 0.0277, % updates: 28.616%
    MEAN RANK >> valid: 2538.495, train: 1502.6005
    (the evaluation took 43.591 seconds)
-- EPOCH 160 (13.7459 seconds per epoch):
COST >> 0.5852 +/- 0.0288, % updates: 28.628%
    MEAN RANK >> valid: 2563.7135, train: 1457.409
    (the evaluation took 47.238 seconds)
-- EPOCH 170 (13.9505 seconds per epoch):
COST >> 0.585 +/- 0.0284, % updates: 28.627%
    MEAN RANK >> valid: 2598.625, train: 1508.668
    (the evaluation took 41.62 seconds)
-- EPOCH 180 (13.7503 seconds per epoch):
COST >> 0.5846 +/- 0.028, % updates: 28.617%
    MEAN RANK >> valid: 2562.2285, train: 1514.315
    (the evaluation took 41.185 seconds)
-- EPOCH 190 (13.7498 seconds per epoch):
COST >> 0.5853 +/- 0.0276, % updates: 28.628%
    MEAN RANK >> valid: 2545.985, train: 1508.7025
    (the evaluation took 40.715 seconds)
-- EPOCH 200 (13.911 seconds per epoch):
COST >> 0.5846 +/- 0.0275, % updates: 28.616%
    MEAN RANK >> valid: 2545.276, train: 1489.0895
    (the evaluation took 41.367 seconds)
-- EPOCH 210 (13.6507 seconds per epoch):
COST >> 0.5852 +/- 0.0283, % updates: 28.63%
    MEAN RANK >> valid: 2521.616, train: 1501.777
    (the evaluation took 46.349 seconds)
-- EPOCH 220 (13.8924 seconds per epoch):
COST >> 0.5853 +/- 0.0279, % updates: 28.636%
    MEAN RANK >> valid: 2576.8105, train: 1464.5985
    (the evaluation took 40.534 seconds)
-- EPOCH 230 (14.0016 seconds per epoch):
COST >> 0.5851 +/- 0.0277, % updates: 28.633%
    MEAN RANK >> valid: 2581.774, train: 1488.3685
    (the evaluation took 40.768 seconds)
-- EPOCH 240 (13.6372 seconds per epoch):
COST >> 0.5847 +/- 0.0277, % updates: 28.605%
    MEAN RANK >> valid: 2561.3445, train: 1490.669
    (the evaluation took 41.671 seconds)
-- EPOCH 250 (13.588 seconds per epoch):
COST >> 0.5846 +/- 0.0275, % updates: 28.603%
    MEAN RANK >> valid: 2533.8615, train: 1491.4595
    (the evaluation took 40.431 seconds)
-- EPOCH 260 (14.1092 seconds per epoch):
COST >> 0.5852 +/- 0.0281, % updates: 28.629%
    MEAN RANK >> valid: 2524.907, train: 1529.538
    (the evaluation took 43.785 seconds)
-- EPOCH 270 (13.615 seconds per epoch):
COST >> 0.5856 +/- 0.0279, % updates: 28.649%
    MEAN RANK >> valid: 2594.1715, train: 1500.197
    (the evaluation took 41.121 seconds)
-- EPOCH 280 (13.6351 seconds per epoch):
COST >> 0.5848 +/- 0.0281, % updates: 28.625%
    MEAN RANK >> valid: 2478.206, train: 1473.8335
        ##### NEW BEST VALID >> test: 2431.339
    (the evaluation took 62.283 seconds)
-- EPOCH 290 (14.1048 seconds per epoch):
COST >> 0.5844 +/- 0.028, % updates: 28.604%
    MEAN RANK >> valid: 2594.1305, train: 1587.6305
    (the evaluation took 42.206 seconds)
-- EPOCH 300 (13.5902 seconds per epoch):
COST >> 0.5852 +/- 0.0278, % updates: 28.629%
    MEAN RANK >> valid: 2535.6355, train: 1474.9225
    (the evaluation took 42.145 seconds)
-- EPOCH 310 (13.7048 seconds per epoch):
COST >> 0.5853 +/- 0.0281, % updates: 28.633%
    MEAN RANK >> valid: 2518.5735, train: 1520.5495
    (the evaluation took 40.396 seconds)
-- EPOCH 320 (13.5596 seconds per epoch):
COST >> 0.5847 +/- 0.0285, % updates: 28.611%
    MEAN RANK >> valid: 2547.382, train: 1435.3145
    (the evaluation took 41.601 seconds)
-- EPOCH 330 (13.8951 seconds per epoch):
COST >> 0.5853 +/- 0.0277, % updates: 28.627%
    MEAN RANK >> valid: 2558.76, train: 1495.843
    (the evaluation took 40.745 seconds)
-- EPOCH 340 (13.7287 seconds per epoch):
COST >> 0.5851 +/- 0.028, % updates: 28.615%
    MEAN RANK >> valid: 2578.2635, train: 1464.18
    (the evaluation took 41.701 seconds)
-- EPOCH 350 (13.9722 seconds per epoch):
COST >> 0.5852 +/- 0.028, % updates: 28.644%
    MEAN RANK >> valid: 2570.4365, train: 1506.5885
    (the evaluation took 40.943 seconds)
-- EPOCH 360 (13.6693 seconds per epoch):
COST >> 0.585 +/- 0.0283, % updates: 28.621%
    MEAN RANK >> valid: 2592.5005, train: 1488.669
    (the evaluation took 41.489 seconds)
-- EPOCH 370 (14.0562 seconds per epoch):
COST >> 0.5853 +/- 0.0275, % updates: 28.613%
    MEAN RANK >> valid: 2527.0815, train: 1510.029
    (the evaluation took 41.186 seconds)
-- EPOCH 380 (13.5753 seconds per epoch):
COST >> 0.5851 +/- 0.0278, % updates: 28.608%
    MEAN RANK >> valid: 2566.2895, train: 1480.227
    (the evaluation took 40.583 seconds)
-- EPOCH 390 (13.662 seconds per epoch):
COST >> 0.5849 +/- 0.0286, % updates: 28.609%
    MEAN RANK >> valid: 2506.2965, train: 1561.4965
    (the evaluation took 40.525 seconds)
-- EPOCH 400 (13.5609 seconds per epoch):
COST >> 0.5852 +/- 0.0281, % updates: 28.635%
    MEAN RANK >> valid: 2606.5325, train: 1474.29
    (the evaluation took 41.654 seconds)
-- EPOCH 410 (14.1833 seconds per epoch):
COST >> 0.5856 +/- 0.0287, % updates: 28.63%
    MEAN RANK >> valid: 2551.1865, train: 1511.1175
    (the evaluation took 41.022 seconds)
-- EPOCH 420 (13.9187 seconds per epoch):
COST >> 0.5854 +/- 0.028, % updates: 28.636%
    MEAN RANK >> valid: 2579.4155, train: 1496.488
    (the evaluation took 41.552 seconds)
-- EPOCH 430 (13.7581 seconds per epoch):
COST >> 0.5853 +/- 0.0282, % updates: 28.629%
    MEAN RANK >> valid: 2539.8345, train: 1487.854
    (the evaluation took 40.044 seconds)
-- EPOCH 440 (13.6784 seconds per epoch):
COST >> 0.5851 +/- 0.0274, % updates: 28.629%
    MEAN RANK >> valid: 2585.2645, train: 1449.604
    (the evaluation took 40.061 seconds)
-- EPOCH 450 (13.9173 seconds per epoch):
COST >> 0.5853 +/- 0.0277, % updates: 28.624%
    MEAN RANK >> valid: 2522.25, train: 1470.082
    (the evaluation took 40.182 seconds)
-- EPOCH 460 (13.8374 seconds per epoch):
COST >> 0.5849 +/- 0.0279, % updates: 28.637%
    MEAN RANK >> valid: 2555.768, train: 1478.828
    (the evaluation took 42.134 seconds)
-- EPOCH 470 (13.5488 seconds per epoch):
COST >> 0.5852 +/- 0.0282, % updates: 28.617%
    MEAN RANK >> valid: 2569.101, train: 1505.802
    (the evaluation took 40.231 seconds)
-- EPOCH 480 (13.9631 seconds per epoch):
COST >> 0.5851 +/- 0.0273, % updates: 28.632%
    MEAN RANK >> valid: 2580.9845, train: 1510.754
    (the evaluation took 41.682 seconds)
-- EPOCH 490 (13.75 seconds per epoch):
COST >> 0.5849 +/- 0.0277, % updates: 28.608%
    MEAN RANK >> valid: 2596.538, train: 1509.0875
    (the evaluation took 39.438 seconds)
-- EPOCH 500 (13.7304 seconds per epoch):
COST >> 0.5853 +/- 0.0282, % updates: 28.623%
    MEAN RANK >> valid: 2559.398, train: 1533.861
    (the evaluation took 43.392 seconds)

Reproduce the experiment results of TransE

With the default config in the code, I ran the FB15k_TransE.py to train a best model, and I found the evaluation result of that model was really different from what the paper said. The default epochs in the code if 500, and the paper said that the model was trained at most 1000 epochs. But current result was even much better than the result in paper. Does the paper used the code in the current repo? Below is my result.

MICRO:

-- left   >> mean: 229.41149, median: 23.0, hits@10: 37.377%
-- right  >> mean: 160.86706, median: 14.0, hits@10: 45.088%
-- global >> mean: 195.13927, median: 18.0, hits@10: 41.233%

MACRO:

-- left   >> mean: 106.30351, median: 83.18991, hits@10: 55.557%
-- right  >> mean: 84.51045, median: 63.63632, hits@10: 63.104%
-- global >> mean: 95.40698, median: 33.58689, hits@10: 59.331%

Model fails to learn

When I trained TransE on WordNet, only the first epoch updated weights and improved the results.

$ python WN_TransE.py
Using gpu device 0: GeForce GTX 480
DD{'ndim': 20, 'test_all': 10, 'loadmodel': False, 'nhid': 20, 'lremb': 0.01, 'savepath': 'WN_TransE', 'seed': 123, 'marge': 2.0, 'simfn': 'L1', 'neval': 1000, 'dataset': 'WN', 'nbatches': 100, 'lrparam': 1.0, 'loademb': False, 'datapath': '../data/', 'Nrel': 18, 'totepochs': 1000, 'Nent': 40961, 'Nsyn': 40943, 'op': 'TransE'}
/home/minhle/.local/lib/python2.6/site-packages/numpy/core/fromnumeric.py:2499: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.
  VisibleDeprecationWarning)
/home/minhle/SME/model.py:949: UserWarning: The parameter 'updates' of theano.function() expects an OrderedDict, got <class 'collections.OrderedDict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (theano.compat.python2x.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.
  updates=updates, on_unused_input='ignore')
BEGIN TRAINING
-- EPOCH 10 (4.1381 seconds per epoch):
COST >> nan +/- nan, % updates: 55.797%
    MEAN RANK >> valid: 21031.55, train: 20901.2955
        ##### NEW BEST VALID >> test: 20779.578
    (the evaluation took 31.919 seconds)
-- EPOCH 20 (4.1195 seconds per epoch):
COST >> nan +/- nan, % updates: 0.0%
    MEAN RANK >> valid: 21031.55, train: 20901.2955
    (the evaluation took 21.398 seconds)
-- EPOCH 30 (4.1711 seconds per epoch):
COST >> nan +/- nan, % updates: 0.0%
    MEAN RANK >> valid: 21031.55, train: 20901.2955
    (the evaluation took 21.084 seconds)
-- EPOCH 40 (4.1701 seconds per epoch):
COST >> nan +/- nan, % updates: 0.0%
    MEAN RANK >> valid: 21031.55, train: 20901.2955
    (the evaluation took 21.182 seconds)
-- EPOCH 50 (4.4218 seconds per epoch):
COST >> nan +/- nan, % updates: 0.0%
    MEAN RANK >> valid: 21031.55, train: 20901.2955
    (the evaluation took 21.132 seconds)
-- EPOCH 60 (4.4139 seconds per epoch):
COST >> nan +/- nan, % updates: 0.0%
    MEAN RANK >> valid: 21031.55, train: 20901.2955
    (the evaluation took 21.03 seconds)

IndexError: row index (13519) out of bounds before training

Hi,

I tried to train your FB15k TransE model on my (german) FB15k data. I put the three files freebase_mtr100_mte100-test.txt, freebase_mtr100_mte100-valid.txt and freebase_mtr100_mte100-test.txt into the FB15k folder and parsed them with FB15k_parse.py successfully (their format is entity<tab>relation<tab>entity, as far as I know the format you need?).

But when I try to run FB15k_TransE.py, I get an error

Traceback (most recent call last):
  File "FB15k_TransE.py", line 5, in <module>
    nbatches=100, totepochs=500, test_all=10, neval=1000, savepath='FB15k_TransE', datapath='../data/')
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 421, in launch
    FB15kexp(state, channel)
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 258, in FB15kexp
    trainln = create_random_mat(trainl.shape, np.arange(state.Nsyn)) # !!!!
  File "/Users/dennisulmer/Desktop/SME-master/FB15k_exp.py", line 33, in create_random_mat
    randommat[listidx[idx_term], idx_ex] = 1
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/sparse/lil.py", line 282, in __setitem__
    self.rows, self.data, i, j, x)
  File "scipy/sparse/_csparsetools.pyx", line 61, in scipy.sparse._csparsetools.lil_insert (scipy/sparse/_csparsetools.c:3292)
  File "scipy/sparse/_csparsetools.pyx", line 82, in scipy.sparse._csparsetools.lil_insert (scipy/sparse/_csparsetools.c:2745)
IndexError: row index (13519) out of bounds

I tried to find the bug myself, but so far I was unsuccessful.

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.