cv_data = data_util.get_k_fold_data(k=3,
data=train_data,
rand_seed=0,
)
WordEmbeddingCNN.cross_validation(
cv_data,
(test_data[u'SENTENCE'].as_matrix(), test_y),
'result/static_W2V_%s_cv_detail.txt'%feature_type,
rand_seed=rand_seed,
nb_epoch=nb_epoch,
verbose=verbose,
feature_type=feature_type,
full_mode=False,
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,
word_embedding_dim = word_embedding_dim,
sentence_padding_length = sentence_padding_length,
word2vec_model_file_path=data_util.transform_word2vec_model_name('%dd_weibo_100w'%word_embedding_dim),
embedding_weight_trainable = True,
)
end_time = timeit.default_timer()
print 'end! Running time:%ds!' % (end_time - start_time)
logging.debug('=' * 20)
