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)