def main():
_, word_vecs = data_load.load_word_vectors('/media/dhl/Data/el/word2vec/wiki_vectors.jbin')
word_vec_len = len(word_vecs[0])
wid_idx_dict, entity_vecs = data_load.load_entities(
'/media/dhl/Data/el/vec_rep/wid_entity_rep_wiki50_cat.bin',
False)
# wid_idx_dict, entity_vecs = data_load.load_entities('/media/dhl/Data/el/vec_rep/wid_entity_rep_wiki50.bin',
# True)
# all_word_vecs =
num_val_candidates = 30
num_test_candidates = 30
skipwidth_loading = 0
img_h = sentence_len + 2 * sentence_pad_len
train_cnn_for_el('/media/dhl/Data/el/vec_rep/wiki_train_word_vec_indices_wiki50.td',
'/media/dhl/Data/el/vec_rep/tac_2014_training.bin',
# '/media/dhl/Data/el/vec_rep/wiki_val_word_vec_indices_wiki50.td',
num_val_candidates,
'/media/dhl/Data/el/vec_rep/wiki_test_word_vec_indices_wiki50.td',
num_test_candidates,
img_h, word_vec_len,
word_vecs,
wid_idx_dict,
entity_vecs,
skip_width_loading=skipwidth_loading,
n_epochs=1)
def main():
if len(sys.argv) < 2:
print 'need params file'
params = load_params(sys.argv[1])
entity_side_cnn = params['entity_side_cnn'] == '1'
word_vec_file_name = params['word_vec_file']
if entity_side_cnn:
entity_rep_file_name = params['entity_rep_indices_file']
else:
entity_rep_file_name = params['entity_rep_vec_file']
train_data_file_name = params['train_data_file']
val_data_file_name = params['val_data_file']
test_data_file_name = params['test_data_file']
training_part_size = int(params['training_part_size'])
sentence_len = int(params['context_sentence_len'])
_, word_vecs = data_load.load_word_vectors(word_vec_file_name)
word_vec_len = len(word_vecs[0])
if entity_side_cnn:
print 'entity use cnn'
global entity_rep_len
wid_idx_dict, entity_vecs, entity_rep_len = data_load.load_index_vec_of_entities_fixed_len(
entity_rep_file_name)
else:
wid_idx_dict, entity_vecs = data_load.load_entities(
entity_rep_file_name,
False)
# wid_idx_dict, entity_vecs = data_load.load_entities_indices(
# entity_rep_file_name, max_num_entity_words, entity_pad_len)
num_val_candidates = 30
num_test_candidates = 30
skipwidth_loading = 0
train_cnn_for_el(train_data_file_name,
val_data_file_name,
num_val_candidates,
test_data_file_name,
num_test_candidates,
sentence_len, word_vec_len,
word_vecs,
wid_idx_dict,
entity_vecs,
entity_side_cnn=entity_side_cnn,
gold_as_first_candidate=False,
skip_width_loading=skipwidth_loading,
n_epochs=1,
training_part_size=training_part_size)
def main():
if len(sys.argv) < 2:
print 'need params file'
params = load_params(sys.argv[1])
entity_side_cnn = params['entity_side_cnn'] == '1'
word_vec_file_name = params['word_vec_file']
if entity_side_cnn:
entity_rep_file_name = params['entity_rep_indices_file']
else:
entity_rep_file_name = params['entity_rep_vec_file']
train_data_file_name = params['train_data_file']
val_data_file_name = params['val_data_file']
test_data_file_name = params['test_data_file']
training_part_size = int(params['training_part_size'])
sentence_len = int(params['context_sentence_len'])
_, word_vecs = data_load.load_word_vectors(word_vec_file_name)
word_vec_len = len(word_vecs[0])
if entity_side_cnn:
print 'entity use cnn'
global entity_rep_len
wid_idx_dict, entity_vecs, entity_rep_len = data_load.load_index_vec_of_entities_fixed_len(
entity_rep_file_name)
else:
wid_idx_dict, entity_vecs = data_load.load_entities(
entity_rep_file_name, False)
# wid_idx_dict, entity_vecs = data_load.load_entities_indices(
# entity_rep_file_name, max_num_entity_words, entity_pad_len)
num_val_candidates = 30
num_test_candidates = 30
skipwidth_loading = 0
train_cnn_for_el(train_data_file_name,
val_data_file_name,
num_val_candidates,
test_data_file_name,
num_test_candidates,
sentence_len,
word_vec_len,
word_vecs,
wid_idx_dict,
entity_vecs,
entity_side_cnn=entity_side_cnn,
gold_as_first_candidate=False,
skip_width_loading=skipwidth_loading,
n_epochs=1,
training_part_size=training_part_size)