cv_data = data_util.get_k_fold_data(k=3,
data=train_data,
rand_seed=3,
)
SingleChannelBowCNN.cross_validation(
cv_data,
(test_data[u'SENTENCE'].as_matrix(), test_y),
'result/cnn_bow_%s_v2.3S_cv_detail.txt'% feature_type,
rand_seed=rand_seed,
nb_epoch=nb_epoch,
verbose=verbose,
remove_stopword = remove_stopword,
feature_type=feature_type,
layer1=layer1,
l1_conv_filter_type=l1_conv_filter_type,
layer2=layer2,
l2_conv_filter_type=l2_conv_filter_type,
k=k,
hidden1=hidden1,
hidden2=hidden2,
word2vec_to_solve_oov = word2vec_to_solve_oov,
word2vec_model_file_path = config['word2vec_model_file_path']
)
end_time = timeit.default_timer()
print 'end! Running time:%ds!' % (end_time - start_time)
logging.debug('=' * 20)
cv_data = data_util.get_k_fold_data(k=3,
data=train_data,
rand_seed=0,
)
SingleChannelBowCNN.cross_validation(
cv_data,
(test_data[u'SENTENCE'].as_matrix(), test_y),
'single_%s_bow_cv_detail.txt',
rand_seed=1337,
nb_epoch=30,
verbose=0,
feature_type='word_seg',
layer1=[3,5,8,18],
l1_conv_filter_type=[2, 3, 4],
layer2=[3, 7],
l2_conv_filter_type=[5],
k=[2, 2],
hidden1=[50, 100],
hidden2=[50, 100],
)
quit()
for layer1 in [3,5,8,18]:
for layer2 in [3,7,10,20,50]:
for hidden1 in [50,100,500,1000]:
for hidden2 in [50,100,300,450]:
print('=' * 150)
