if __name__ == '__main__':
# dataset = 'THUCNews'
# dataset = 'TOUTIAONews'
# dataset = 'weibo_senti_100k'
# dataset = 'simplifyweibo_4_moods'
# dataset = 'Chinese_conversation_sentiment-master'
# dataset = 'NLPCC2017'
dataset = 'testtt'
model_name = args.model
x = import_module('models.' + model_name)
config = x.Config(dataset)
np.random.seed(2)
torch.manual_seed(1)
torch.cuda.manual_seed_all(4)
torch.backends.cudnn.deterministic = True #保证每次运行结果一样
start_time = time.time()
print('加载数据集')
train_data, dev_data, test_data = utils.bulid_dataset(config)
train_iter = utils.bulid_iterator(train_data, config)
dev_iter = utils.bulid_iterator(dev_data, config)
test_iter = utils.bulid_iterator(test_data, config)
time_dif = utils.get_time_dif(start_time)
print("模型开始之前，准备数据时间：", time_dif)
# 模型训练，评估与测试
model = x.Model(config).to(config.device)
train.train(config, model, train_iter, dev_iter, test_iter)
# train.test(config, model, test_iter)
import numpy as np
import pandas as pd
from utils import bulid_dataset
import matplotlib.pyplot as plt
from keras.models import Model, Input, load_model
from keras.callbacks import ModelCheckpoint
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
plt.style.use("ggplot")
# 1 加载数据
ner_dataset_dir = '../data/ner_dataset.csv'
dataset_dir = '../data/dataset.pkl'
# 2 构建数据集
n_words, n_tags, max_len, words,tags,\
X_train, X_test, y_train, y_test=bulid_dataset(ner_dataset_dir,dataset_dir,max_len=50)
# 3 构建和训练模型
def train():
input = Input(shape=(max_len, ))
model = Embedding(input_dim=n_words, output_dim=50,
input_length=max_len)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(n_tags, activation='softmax'))(
model) # softmax output layer
model = Model(input, out)
model.compile(optimizer='rmsprop',