def predict_class(text):
logging.info(f"Input text: {text}")
logging.info("cleaning input text")
text = clean_text(text)
sentence = [text]
tokenizer = load_tokenizer()
logging.info("trained tokenizer loaded")
word_index = tokenizer.word_index
vocab_size = len(word_index)
text_sequences = tokenizer.texts_to_sequences(sentence)
text_padded = pad_sequences(text_sequences,
padding=PADDING_TYPE,
truncating=TRUNC_TYPE,
maxlen=MAX_LENGTH)
logging.info("creating embedding matrix using Glove Embeddings")
embedding_matrix = embedding_matrix_glove(word_index)
logging.info(f"Embeddings Weights created {embedding_matrix.shape}")
logging.info("getting pre-trained model")
model = create_model(vocab_size, EMBEDDING_DIM, MAX_LENGTH,
embedding_matrix)
print(model.summary())
logging.info("loading model weights")
model.load_weights(MODEL)
predict = model.predict(text_padded)
predict = np.argmax(predict)
predicted_main_product = get_main_product(predict)
logging.info(f'Predicted Main Product: {predicted_main_product}')
def main():
parser = argparse.ArgumentParser(description='model')
parser.add_argument('--input_image_paths',
dest='input_image_paths',
default='inputs_128_128_1',
nargs='+',
help='input image paths, separate by space')
parser.add_argument('--max_input_count',
dest='max_input_count',
type=int,
default=10000,
help='max input image count for train')
parser.add_argument('--model_parameter_path',
dest='model_parameter_path',
default='model_parameter')
parser.add_argument('--dump_detail',
dest='dump_detail',
default=False,
action='store_true')
args = parser.parse_args()
train_x, train_y, test_x, test_y = load_image_data(args.input_image_paths,
args.max_input_count)
model = create_model(0.0001)
load_latest_model_parameter(model, args.model_parameter_path)
test_model(model, train_x, train_y, args.dump_detail)
test_model(model, test_x, test_y, args.dump_detail)
def evaluate_line():
config = utils.load_config(FLAGS.config_file) # 读取配置文件
log_path = os.path.join("evl_log", FLAGS.log_test) # ./log/train.log
logger = utils.get_logger(log_path) # log文件名及路径
# limit GPU memory
tf_config = tf.ConfigProto() # TensorFlow 会话的配置项
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f: # map_file 中存储着字与id，tag与id之间的对应关系
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
# char_to_id 每个字对应的id， id_to_char 两者是相对应的
# print('char_to_id: ', char_to_id)
# print('tag_to_id: ', tag_to_id)
with tf.Session(config=tf_config) as sess:
model = utils.create_model(sess, Model, FLAGS.ckpt_path,
data_utils.load_word2vec, config,
id_to_char, logger)
while True:
try:
line = input("请输入测试句子:")
if line == 'exit':
break
result = model.evaluate_line(
sess, data_utils.input_from_line(line, char_to_id),
id_to_tag)
print(result)
logger.debug(result)
except Exception as e:
logger.info(e)
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger, False)
# while True:
# # try:
# # line = input("请输入测试句子:")
# # result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# # print(result)
# # except Exception as e:
# # logger.info(e)
#
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
line = u"香港的房价已经到达历史巅峰,乌溪沙地铁站上盖由新鸿基地产公司开发的银湖天峰,现在的尺价已经超过一万五千港币。"
line = u"这是测试语句，国务院加入测试"
result = model.evaluate_line(sess, input_from_line(line, char_to_id),
id_to_tag)
print(result)
def evaluate_line():
config = load_config(FLAGS.config_file) #从文件config_file 中读取配置数据
#{'model_type': 'idcnn', 'num_chars': 3538, 'char_dim': 100, 'num_tags': 51, 'seg_dim': 20, 'lstm_dim': 100, 'batch_size': 20, 'emb_file': 'E:\\pythonWork3.6.2\\NERuselocal\\NERuselocal\\data\\vec.txt', 'clip': 5, 'dropout_keep': 0.5, 'optimizer': 'adam', 'lr': 0.001, 'tag_schema': 'iobes', 'pre_emb': True, 'zeros': True, 'lower': False}
logger = get_logger(FLAGS.log_file) #写日志文件名字为 train.log
# limit GPU memory
tf_config = tf.ConfigProto(
) #实例化一个设置GPU的对象 函数用在创建session的时候，用来对session进行参数配置
tf_config.gpu_options.allow_growth = True #1动态申请显存需要多少使用多少 2限制GPU使用率 config.gpu_options.per_process_gpu_memory_fraction = 0.4
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
line = input("请输入测试句子:")
result = model.evaluate_line(sess,
input_from_line(line, char_to_id),
id_to_tag)
print(result)
def main():
# load parameters for run...
parameters = yaml.load(open(PARAM_FILE_DIRECTORY))
db_defs = parameters['database']
solver_param = parameters['solver_param']
model_param = parameters['model_param']
model_specific_params = parameters[model_param['model_type']]
print('Initialize DB connection....')
db, dictionary, reverse_dictionary = utils.initialize_db_connection(
db_defs)
#target_words, context, dictionary, reverse_dictionary = collect_data(db_defs,5000)
#data, count, dictionary, reverse_dictionary = collect_data2(vocabulary_size=model_param['vocabulary_size'])
#target_words, context = generate_batch(data, 500, 2, 2)
#data, count, dictionary, reverse_dictionary = collect_data2(vocabulary_size=model_param['vocabulary_size'])
print('Done Collect Data.')
print('try to import model')
# build model...
model = create_model(model_param, model_specific_params)
print('Model drawn')
# Initialize the solver object.
solver = Solver(model)
# train model....
solver.train(db, db_defs, dictionary, reverse_dictionary, solver_param)
#grab embeddings for some sample data.
embedding = solver.run(np.array([1, 2, 3, 12], dtype=np.int32),
solver_param)
print('done!')
def train(self):
self.get_sentences_dict()
self.get_batch_data()
logger, config = self.get_config()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True # limit GPU memory
steps_per_epoch = self.train_batch_manager.len_data # 每一轮epoch的batch数量
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, config,
self.id_to_char, logger)
logger.info("start training")
loss = []
for i in range(FLAGS.max_epoch):
for batch in self.train_batch_manager.iter_batch(shuffle=True):
step, batch_loss = model.run_step(sess, True, batch)
loss.append(batch_loss)
if step % FLAGS.steps_check == 0:
iteration = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration, step % steps_per_epoch,
steps_per_epoch, np.mean(loss)))
loss = []
# 对验证集进行预测和评估
best = self.evaluate(sess, model, "dev",
self.dev_batch_manager, self.id_to_tag,
logger)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger)
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, NERModel, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
# txt = input("请输入文件:")
# with open(txt, encoding='u8') as test_file:
# for line in test_file.readlines():
# line = line.split(',')
# result = model.evaluate_line(sess, input_from_line(line[1], char_to_id), id_to_tag)
# print(result)
while True:
line = input("请输入测试句子:")
result = model.evaluate_line(sess,
input_from_line(line, char_to_id),
id_to_tag)
print(result)
def evaluate_file(file, target):
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, NERModel, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
with open(file, encoding='u8') as fr, open(target,
mode='w',
encoding='u8') as fw:
for line in fr:
result = model.evaluate_line(
sess, input_from_line(line.strip(), char_to_id), id_to_tag)
print(result)
fw.write(json.dumps(result, ensure_ascii=False))
fw.write("\n")
fw.flush()
while True:
line = input("请输入测试句子:")
result = model.evaluate_line(sess,
input_from_line(line, char_to_id),
id_to_tag)
print(result)
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
user_path = os.path.expanduser("~")
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
with open(
user_path +
'/share/deep_learning/data/knowledge_graph/entity_relation/corpus.yml'
) as f:
corpus = yaml.load(f)
for key in corpus:
sentences = corpus[key]
sentences = list(set(sentences))
for sen in sentences:
sen_strip = sen.replace(' ', '')
result = model.evaluate_line(
sess, input_from_line(sen_strip, char_to_id),
id_to_tag)
extract_entity(result, key)
def main(_):
if FLAGS.train:
if FLAGS.clean:
clean(FLAGS)
train()
else:
# 下面使用testdata来进行评估模型
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
log_path = os.path.join("log", FLAGS.log_file)
config = load_config(FLAGS.config_file)
logger = get_logger(log_path)
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)
test_sentences = load_sentences(FLAGS.test_file, FLAGS.lower,
FLAGS.zeros)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
FLAGS.lower)
test_manager = BatchManager(test_data, 100)
with tf.Session(config=tf_config) as sess:
sess.run(tf.global_variables_initializer())
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
def evaluate_line(sents):
global static_model
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
graph = tf.Graph()
# tf_config = tf.ConfigProto()
# tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
# with tf.Session(config=tf_config) as sess:
sess = tf.InteractiveSession(graph=graph)
print(" start create model")
static_model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
print(" end create model")
result = static_model.evaluate_line(sess,
input_from_line(sents,
char_to_id), id_to_tag)
sess.close()
return result
def predict():
"""
对一个数据集进行实体识别
:return:
"""
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True # limit GPU memory
# 从训练阶段生成的map_file中恢复各映射字典
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
test_sentences = load_sentences(FLAGS.test_file, FLAGS.lower,
FLAGS.zeros)
test_data = prepare_dataset(test_sentences,
char_to_id,
tag_to_id,
FLAGS.lower,
train=False)
test_manager = BatchManager(test_data, 1)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, config,
id_to_char, logger)
logger.info("predict data......")
ner_results = model.predict(sess, test_manager, id_to_tag)
result_write_evaluate(ner_results, FLAGS.result_path, "test")
def evaluate_line():
# 写死
FLAGS.config_file = 'forum_config/config_file'
FLAGS.log_file = 'forum_config/log/train.log'
FLAGS.ckpt_path = 'forum_ckpt/'
# FLAGS.ckpt_path = 'ckpt/'
FLAGS.map_file = 'forum_config/maps.pkl'
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger)
while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
line = input("请输入测试句子:")
result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
print(result)
def __test():
"""
test interface
:return:
"""
log_path = os.path.join(".", config['log_path'])
config['ckpt_path'] = "Pretrain/" + domain + "/"
logger = get_logger(log_path)
id2mid, id2p, entity_entity_sim_Matrix, entity_relation_Adj, truth_label, train_entity_list, \
test_entity_list, valid_entity_list = data_reader(logger, config=config, domain=domain,
entity_entity_topk=entity_knn_number)
test_data = (entity_relation_Adj, entity_entity_sim_Matrix,
test_entity_list, truth_label)
test_manager = BatchManager(test_data, len(test_entity_list), "test")
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, config['ckpt_path'], config, logger)
logger.info("start test")
test_precision, test_ndcg, test_map, test_ndcg_topall = evaluate(
sess, model, "test", test_manager, logger, id2mid, id2p)
print("test precision at {} :{:>.5f}".format(k, test_precision))
print("test ndcg at {} :{:>.5f}".format(k, test_ndcg))
print("test map :{:>.5f}".format(test_map))
print("test ndcg_topall :{:>.5f}".format(test_ndcg_topall))
def evaluate_ht():
submit_path_ht = 'submit_sample/hetong.csv'
submit_path_file = open(submit_path_ht, 'a+', encoding='gbk')
submit_path_file.write('公告id,甲方,乙方,项目名称,合同名称,合同金额上限,合同金额下限,联合体成员\n')
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
rootdir = '/home/utopia/corpus/FDDC_part2_data/FDDC_announcements_round1_test_a_20180605/重大合同/html/'
list = os.listdir(rootdir) # 列出文件夹下所有的目录与文件
for i in range(0, len(list)):
htmlpath = os.path.join(rootdir, list[i])
if os.path.isfile(htmlpath):
print(htmlpath)
s_arr = levelText_withtable(htmlpath)
candidates = []
for j in range(len(s_arr)):
sen = s_arr[j]
result = model.evaluate_line(
sess, input_from_line(sen, char_to_id), id_to_tag)
entities = result.get('entities')
if len(entities) > 0:
for en in entities:
en['sid'] = j
en['pid'] = list[i]
candidates.append(en)
org_ht(candidates, submit_path_file)
print('-------------------------------------------------')
def predict():
batcher = get_batcher()
config = load_config(FLAGS.config_file)
logger = get_logger(os.path.join('log', FLAGS.log_file))
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, tag_to_id, id_to_tag = pickle.load(f)
def get_test_data(char2id):
sentences = []
with open('data/test.txt', 'r', encoding='utf-8') as f:
for line in f:
words = line.strip().split('_')
ids = [
char2id[char if char in char2id else '<UNK>']
for char in words
]
sentences.append([words, ids])
return sentences
test_data = get_test_data(char_to_id)
with tf.Session(config=tf_config) as sess:
elmo_model = load_elmo()
model = create_model(sess, Model, FLAGS.ckpt_path, elmo_model, config,
logger)
results = model.predict_batch(sess,
data=test_data,
id_to_tag=id_to_tag,
batcher=batcher,
batch_size=FLAGS.batch_size)
result_to_file(results)
def semisupervised_training(unsup, layers, data, epochs):
X_train, y_train, X_test, y_test = data[0], data[1], data[2], data[3]
sup = create_model(layers, 0, 0, 0, 'relu', 'softmax',
'binary_crossentropy', 'adam')
for i in range(3):
sup.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=1,
batch_size=200,
verbose=2)
unsup = change_weights(sup, unsup)
unsup.fit(X_train,
X_train,
validation_data=(X_test, X_test),
epochs=1,
batch_size=200,
verbose=2)
sup = change_weights(unsup, sup)
for j in range(epochs):
sup.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=1,
batch_size=200,
verbose=2)
return sup
def content_ner():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
for i in range(302):
newslist = []
m = i + 1
f = open('newsLists/%d.txt' % m, 'r')
for line in f.readlines():
newslist.append(int(line))
fout = codecs.open('content_ner/%d.json' % m,
'w',
encoding='utf-8')
for i in range(len(newslist)):
day = newslist[i]
f = codecs.open('D:/PycharmProjects/news_data/%d.json' % day,
encoding='utf-8')
f_d = json.load(f)
content = f_d["content"]
content = strip_tags(content)
result = model.evaluate_line(
sess, input_from_line(content, char_to_id), id_to_tag)
dicObj = json.dumps(result)
fout.write(dicObj)
fout.write("\n")
fout.close()
def ensemble_val_data():
preds_raw = []
labels = []
for match_str in w_file_matcher:
os.chdir(MODEL_DIR)
w_files = glob.glob(match_str)
for w_file in w_files:
full_w_file = MODEL_DIR + '/' + w_file
mname = w_file.split('_')[0]
print(full_w_file)
model = create_model(mname)
model.load_state_dict(torch.load(full_w_file))
pred, y = make_preds_val(model)
#pred = np.array(pred)
preds_raw.append(pred)
labels.append(y)
del model
save_array(PRED_VAL_RAW, preds_raw)
preds = np.mean(preds_raw, axis=0)
save_array(PRED_VAL, preds)
save_array(VAL_LABELS, labels)
return preds, labels
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
# while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
# line = input("请输入测试句子:")
line = "哮喘古代文献也称“鼻息”、“肩息”、“上气”等。"
result = model.evaluate_line(sess, input_from_line(line, char_to_id),
id_to_tag)
print(line)
print([(x["word"], x["type"]) for x in result["entities"]])
line = "喘病是指由于外感或内伤，导致肺失宣降，肺气上逆或气无所主，肾失摄纳，以致呼吸困难，甚则张口抬肩，鼻翼煽动，不能平卧等为主要临床特征的一种病证。 "
result = model.evaluate_line(sess, input_from_line(line, char_to_id),
id_to_tag)
print(line)
print([(x["word"], x["type"]) for x in result["entities"]])
def main():
# For GPU memory efficiency
gpu_options = tf.GPUOptions(allow_growth=True)
# create log dir
if not os.path.exists(FLAGS.logdir):
os.makedirs(FLAGS.logdir)
model_path = os.path.join(FLAGS.logdir, FLAGS.task_name)
# Train or Inference
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if FLAGS.is_train:
# load traning dataset
print ("Start Loding Dataset...")
mnist = input_data.read_data_sets(FLAGS.data_path ,one_hot=False, validation_size=5000)
train_data = mnist.train
val_data = mnist.validation
print ("Loading done.")
print ("Initialize the network")
if not os.path.exists(model_path) or FLAGS.reset:
# Create model if not exist or reset the model.
model = create_model(sess, FLAGS, mode="train")
else:
model = load_model(sess, model_path, mode="train")
# then we restore the trained model.
train(sess, train_data, val_data, model, FLAGS)
print ("Training Done.")
else:
print ("The inference mode is removed from inference.py")
def get_slot_dl(text):
"""
获得一个句子的slot_table
:param text: 用户输入的句子
# :param tf_sess: tensorflow 的sessiong
:return:
"""
tf.reset_default_graph() # FIXME: 为了可以保证重复多次输入(不过会很慢)整合代码时，可以尝试将NLU和DM分离，使用统一的文件管理。
FLAGS.config_file = '/forum_config/config_file'
FLAGS.log_file = '/forum_config/log/train.log'
FLAGS.ckpt_path = '/forum_ckpt/'
FLAGS.map_file = '/forum_config/maps.pkl'
file_path = os.path.dirname(__file__)
config = load_config(file_path+FLAGS.config_file)
logger = get_logger(file_path+FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(file_path+FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger)
result = model.evaluate_line(sess, input_from_line(text, char_to_id), id_to_tag)
return result
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
# map_file文件需要改
with open(FLAGS.map_file, "rb+") as f:
# pkllf=pickle.load(f)
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger)
while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
# 改成file in file out
line = input("请输入测试句子:")
result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
print(result)
def test_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
logger.info("start testing")
data = input_pro()
for line in data:
data = json.loads(line)
print(data)
id = data["id"]
doc_txt = data["doc"]
golden_event = data["golden_event"]
chunk_result = data["chunk_result"]
result_entity = data["result"]
for result in result_entity:
str_token = result["string"]
entities = result["entities"]
test_data = input_from_line(str_token, char_to_id)
classfy_result = model.evaluate_line(sess, test_data)
print(classfy_result)
result["mention_classify"] = classfy_result[0]
write_to_file(data)
def main():
# read in the parameters defining the model.
parameters = yaml.load(open(PARAM_FILE_DIRECTORY))
# read in all the parameters defining the model and
# how it will be trained.
solver_param = parameters['solver_param']
model_param = parameters['model_param']
model_specific_params = parameters[model_param['model_type']]
print("Training data using %s model" % (model_param['model_type']))
print('Loading Data set...')
data = utils.load_data()
print('Drawing model...')
# initialize the model
model = utils.create_model(model_param, model_specific_params)
# build it with feature, label sizes...
model.build(data['features'], data['categories'])
print('Model drawn.')
# Initialize the solver object.
solver = Solver(model)
# train model....
solver.train(data['X_train'], data['y_train'], solver_param, data['X_val'],
data['y_val'])
print('done!')
def evaluate_predictsamples():
config = load_config(FLAGS.config_file)
logger = get_logger('log/test.log')
print_config(config, logger)
# limit GPU memory
logger.info("start predict")
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
index = 1
with open('data/predict.txt', 'w', encoding='utf-8') as f_write:
read_file = 'data/test.txt'
for line in open(read_file, 'r', encoding='utf-8-sig').readlines():
result = model.evaluate_line(sess,
input_from_line(line, char_to_id),
id_to_tag)
print(result)
content = str(index) + ','
if result is not None:
entities = result['entities']
for entity in entities:
if entity:
print(entity['word'] + '\t' + entity['type'])
# content += entity['word'] + '\t' + str(entity['start']) + '\t' + str(entity['end']) + '\t' + entity['type']+ ';'
content += entity['word'] + '\t' + entity[
'type'] + ';'
f_write.write(content + '\n')
index += 1
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
f = codecs.open(os.path.join(FLAGS.test_filepath, "127_9.txt"), "r",
"utf-8")
s = f.read()
line = []
sent = ''
for i in range(len(s)):
if s[i] != '。':
sent += s[i]
else:
sent += s[i]
line.append(sent)
sent = ''
line = input("请输入测试句子:")
for info in line:
print(info)
result = model.evaluate_line(sess,
input_from_line(info, char_to_id),
id_to_tag)
for info1 in result['entities']:
print(info1)
def stacking(self):
N, M = len(self.models), len(self.y_train)
super_model = create_model([N, 500, 500, 1], 0.8, 1, 'elu', 'sigmoid',
'binary_crossentropy')
predictions = np.zeros(shape=(M, N))
new_models = []
for i in range(N):
model = self.models[i]
indices = np.array(range(M))
sample = np.random.choice(indices,
round(len(indices) * 0.7),
replace=False)
X, Y = self.X_train[sample], self.y_train[sample]
model.fit(X, Y, batch_size=5000, verbose=1)
new_models.append(model)
probs = model.predict(self.X_train)
predictions[:, i] = probs.reshape(M, )
#fit the super model to predictions:
super_model.fit(predictions, self.y_train)
return super_model, new_models
def run_test():
model = create_model(model_type=model_type,
pretrained=pretrained,
n_classes=n_classes,
input_size=input_size,
checkpoint=checkpoint)
model = model.to(device)
print(model)
# count_flops(model, device=device)
test_dataset = EvalDataset('./data/stanford-dogs/Processed/test')
test_dataloader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False)
model.eval()
loss_func = nn.CrossEntropyLoss()
acc_list, loss_list = [], []
with torch.no_grad():
for i, (inputs, labels) in enumerate(tqdm(test_dataloader)):
inputs, labels = inputs.float().to(device), labels.to(device)
preds = model(inputs)
pred_idx = preds.max(1).indices
acc = (pred_idx == labels).sum().item() / labels.size(0)
acc_list.append(acc)
loss = loss_func(preds, labels).item()
loss_list.append(loss)
final_loss = np.array(loss_list).mean()
final_acc = np.array(acc_list).mean()
print('Test loss: {}\nTest accuracy: {}'.format(final_loss, final_acc))
def setUp(self):
admin_opts = {}
fields = {'name': models.CharField(max_length=255)}
self.dynamic_model = create_model('DynamicModel', fields=fields, app_label='context_admin', admin_opts={})
fields = { 'name': models.CharField(max_length=255),
'dynamic_model': models.ForeignKey(self.dynamic_model)
}
self.dynamic_inner_model = create_model('DynamicInnerModel', fields=fields, app_label='context_admin', admin_opts={})
install(self.dynamic_model)
install(self.dynamic_inner_model)
instance = self.dynamic_model()
instance.save()
def evaluate_line():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger)
while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
line = input("请输入测试句子:")
result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
print(result)
def train(data_path, config):
with tf.Graph().as_default(), tf.Session() as session:
word_to_id_path = os.path.join(data_path, config.vocab_file)
with open(word_to_id_path, "rb") as f:
word_to_id = pickle.load(f)
vocab_size = len(word_to_id)
print("Vocab size: %d" % vocab_size)
sys.stdout.flush()
train_pattern = config.data_pattern.replace("{-type-}", "train") + ".part*"
valid_pattern = config.data_pattern.replace("{-type-}", "valid") + ".part*"
train_files = get_file_list(config, data_path, train_pattern, "train")
valid_files = get_file_list(config, data_path, valid_pattern, "valid")
if config.copy_temp:
temp_dir = tempfile.mkdtemp()
print("Copying data files to %s" % temp_dir)
train_files = copy_temp_files(train_files, temp_dir)
valid_files = copy_temp_files(valid_files, temp_dir)
config.vocab_size = vocab_size
train_batcher = PreBatched(train_files, config.batch_size, description="train") if config.use_prebatched \
else QueuedSequenceBatcher(train_files, config.seq_length, config.batch_size, description="train",
attns=config.attention)
valid_batcher = PreBatched(valid_files, config.batch_size, description="valid") if config.use_prebatched \
else QueuedSequenceBatcher(valid_files, config.seq_length, config.batch_size, description="valid",
attns=config.attention)
t0 = datetime.datetime.now()
initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.variable_scope("model", reuse=None, initializer=initializer):
m = create_model(config, True)
with tf.variable_scope("model", reuse=True, initializer=initializer):
mvalid = create_model(config, False)
summary_writer = tf.train.SummaryWriter(config.events_path, graph=session.graph)
valid_perplexity = PerplexityHook(summary_writer, mvalid, valid_batcher)
hooks = [
SpeedHook(summary_writer, config.status_iterations, config.batch_size),
LossHook(summary_writer, config.status_iterations),
valid_perplexity,
SaveModelHook(config.checkpoint_path, 1, config.__dict__, 5)
]
t1 = datetime.datetime.now()
print("Building models took: %s" % (t1 - t0))
def load_func():
if config.model_path is not None:
load_model(session, config.model_path)
print("Continuing training from model: %s" % config.model_path)
if config.embedding_path is not None:
load_variables(session, os.path.join(config.embedding_path, "embedding.tf"),
[m.embedding_variable])
print("Loading embedding vectors from: %s" % config.embedding_path)
trainer = Trainer(m.optimizer, config.epochs, hooks, m, m.train_op)
trainer(train_batcher, m.loss, session, config.learning_rate, config.lr_decay, load_func)
saver = tf.train.Saver(tf.trainable_variables())
embedding_saver = tf.train.Saver([m.embedding_variable])
print("Saving model...")
out_path = save_model(saver, session, config.save_path, m.predict, config.__dict__)
embedding_saver.save(session, os.path.join(out_path, "embedding.tf"))
if config.copy_temp:
shutil.rmtree(temp_dir)
def train():
# load data sets
train_sentences = load_sentences(FLAGS.train_file, FLAGS.lower, FLAGS.zeros)
dev_sentences = load_sentences(FLAGS.dev_file, FLAGS.lower, FLAGS.zeros)
test_sentences = load_sentences(FLAGS.test_file, FLAGS.lower, FLAGS.zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, FLAGS.tag_schema)
update_tag_scheme(test_sentences, FLAGS.tag_schema)
# create maps if not exist
if not os.path.isfile(FLAGS.map_file):
# create dictionary for word
if FLAGS.pre_emb:
dico_chars_train = char_mapping(train_sentences, FLAGS.lower)[0]
dico_chars, char_to_id, id_to_char = augment_with_pretrained(
dico_chars_train.copy(),
FLAGS.emb_file,
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in test_sentences])
)
)
else:
_c, char_to_id, id_to_char = char_mapping(train_sentences, FLAGS.lower)
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sentences)
with open(FLAGS.map_file, "wb") as f:
pickle.dump([char_to_id, id_to_char, tag_to_id, id_to_tag], f)
else:
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
# prepare data, get a collection of list containing index
train_data = prepare_dataset(
train_sentences, char_to_id, tag_to_id, FLAGS.lower
)
dev_data = prepare_dataset(
dev_sentences, char_to_id, tag_to_id, FLAGS.lower
)
test_data = prepare_dataset(
test_sentences, char_to_id, tag_to_id, FLAGS.lower
)
print("%i / %i / %i sentences in train / dev / test." % (
len(train_data), 0, len(test_data)))
train_manager = BatchManager(train_data, FLAGS.batch_size)
dev_manager = BatchManager(dev_data, 100)
test_manager = BatchManager(test_data, 100)
# make path for store log and model if not exist
make_path(FLAGS)
if os.path.isfile(FLAGS.config_file):
config = load_config(FLAGS.config_file)
else:
config = config_model(char_to_id, tag_to_id)
save_config(config, FLAGS.config_file)
make_path(FLAGS)
log_path = os.path.join("log", FLAGS.log_file)
logger = get_logger(log_path)
print_config(config, logger)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
steps_per_epoch = train_manager.len_data
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger)
logger.info("start training")
loss = []
for i in range(100):
for batch in train_manager.iter_batch(shuffle=True):
#print batch
step, batch_loss = model.run_step(sess, True, batch)
#print step
loss.append(batch_loss)
if step % FLAGS.steps_check == 0:
iteration = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration, step%steps_per_epoch, steps_per_epoch, np.mean(loss)))
loss = []
best = evaluate(sess, model, "dev", dev_manager, id_to_tag, logger)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
