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rzai@rzai00:~/prj/DeepIE/rnnRE$ CUDA_VISIBLE_DEVICES=1 python evaluate.py
Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled)
{'useHeadEmbedding': False, 'conv_win_feature_map1': [2, 3, 4, 5], 'optimizer': 'adadelta', 'verbose': 1, 'regularizer': 0.0, 'nhidden2': 300, 'collapsed': False, 'nhidden1': 300, 'withEmbs': True, 'fold': 'all', 'expected_features2': OrderedDict([('dist1', -1), ('dist2', -1), ('type', -1), ('subtype', -1), ('order', -1), ('constit1', -1), ('constit2', -1), ('preter', -1), ('prepreter', -1), ('grammar', -1), ('gov', -1), ('indep', -1)]), 'seed': 3435, 'norm_lim': 9.0, 'kernelFets': OrderedDict([('kernelPred', 0), ('kernelScore', 0)]), 'updateEmbs': True, 'nepochs': 20, 'binaryCutoff': 2, 'seqType': '-dep', 'multilayerNN1': [300], 'outer': False, 'conv_feature_map2': 150, 'conv_feature_map1': 150, 'expected_features1': OrderedDict([('dist1', -1), ('dist2', -1), ('type', 0), ('subtype', -1), ('order', -1), ('constit1', -1), ('constit2', -1), ('preter', -1), ('prepreter', 0), ('grammar', 1), ('gov', -1), ('indep', 1)]), 'conv_win_feature_map2': [2, 3, 4, 5], 'decay': False, 'batch': 50, 'multilayerNN2': [], 'conv_winre1': 20, 'folder': 'fold_all.model_#MultiNN.st_-dep.cl_0.h1_300.h2_300.outer_False.embs_True.upd_True.batch_50.cut_2.he_0.mul1_300.mul2_.opt_adadelta.drop_0.5.reg_0.0.fet1_-1-10-1-1-1-1-101-11.ke_00.cvw1_20.cvw2_20.cvft1_150.cvft2_150.cvfm1_2345.nm_9.0', 'conv_winre2': 20, 'lr': 0.01, 'model': '#MultiNN', 'dropout': 0.5, 'sharedEmbs': OrderedDict([('word', 1), ('dist1', 0), ('dist2', 0), ('type', 0), ('subtype', 0), ('order', 0), ('constit1', 0), ('constit2', 0), ('preter', 0), ('prepreter', 0), ('grammar', 0), ('gov', 0), ('indep', 0)])}
loading dataset: ./word2vec.full_rnnRE_Plank.pkl ...
using word embeddings to initialize the network ...
sequence types: -dep
using feature1: type : embeddings
using feature1: prepreter : embeddings
using feature1: grammar : binary
using feature1: indep : binary
maximum of length in the dataset: 43 10
-------creating binary feature dictionary on the training data--------
binary feature cutoff: 2
size of dictionary: 138
maximum number of binary features: 80
converting binary features to vectors ...
vocabsize = 274 , nclasses = 8 , nsentences = 10 , word embeddings dim = 300
------- length of the instances: 43 20
... number of batches: 0
building model ...
FEATURES 1
REPRESENTATION DIMENSION 1 = 0
REPRESENTATION DIMENSION -1 = 0
FEATURES 2

done
-------------------training in epoch: 0 -------------------------------------
evaluating in epoch: 0
Traceback (most recent call last):
File "evaluate.py", line 153, in
main(pars)
File "evaluate.py", line 40, in main
folder = params['folder'])
File "/home/rzai/prj/DeepIE/rnnRE/rnnRE.py", line 971, in train
_predictions[elu], _probs[elu], _groundtruth[elu] = predict(evaluatingDataset[elu], batch, reModel, idx2word, idx2label, features1, features2, model, seqRep, kernelFets)
File "/home/rzai/prj/DeepIE/rnnRE/rnnRE.py", line 548, in predict
probs_corpus = numpy.concatenate(probs_corpus, axis=0)
ValueError: need at least one array to concatenate
rzai@rzai00:~/prj/DeepIE/rnnRE$