def train(self):
self.isTrain = True
self.getSaveFolder()
self.saveConf()
self.vocab, self.char_vocab, vocab_embedding = self.preproc.load_data()
self.log('-----------------------------------------------')
self.log("Initializing model...")
self.setup_model(vocab_embedding)
if 'RESUME' in self.opt:
model_path = os.path.join(self.opt['datadir'],
self.opt['MODEL_PATH'])
self.load_model(model_path)
print('Loading train json...')
with open(
os.path.join(self.opt['FEATURE_FOLDER'],
self.data_prefix + 'train-preprocessed.json'),
'r') as f:
train_data = json.load(f)
print('Loading dev json...')
with open(
os.path.join(self.opt['FEATURE_FOLDER'],
self.data_prefix + 'dev-preprocessed.json'),
'r') as f:
dev_data = json.load(f)
best_f1_score = 0.0
numEpochs = self.opt['EPOCH']
for epoch in range(self.epoch_start, numEpochs):
self.log('Epoch {}'.format(epoch))
self.network.train()
startTime = datetime.now()
train_batches = BatchGen(self.opt, train_data['data'],
self.use_cuda, self.vocab,
self.char_vocab)
dev_batches = BatchGen(self.opt,
dev_data['data'],
self.use_cuda,
self.vocab,
self.char_vocab,
evaluation=True)
for i, batch in enumerate(train_batches):
if i == len(train_batches) - 1 or (
epoch == 0 and i == 0 and
('RESUME' in self.opt)) or (i > 0 and i % 1500 == 0):
print('Saving folder is', self.saveFolder)
print('Evaluating on dev set...')
predictions = []
confidence = []
dev_answer = []
final_json = []
for j, dev_batch in enumerate(dev_batches):
phrase, phrase_score, pred_json = self.predict(
dev_batch)
final_json.extend(pred_json)
predictions.extend(phrase)
confidence.extend(phrase_score)
dev_answer.extend(dev_batch[-3]) # answer_str
result, all_f1s = score(predictions, dev_answer,
final_json)
f1 = result['f1']
if f1 > best_f1_score:
model_file = os.path.join(self.saveFolder,
'best_model.pt')
self.save_for_predict(model_file, epoch)
best_f1_score = f1
pred_json_file = os.path.join(self.saveFolder,
'prediction.json')
with open(pred_json_file, 'w') as output_file:
json.dump(final_json, output_file)
score_per_instance = []
for instance, s in zip(final_json, all_f1s):
score_per_instance.append({
'id':
instance['id'],
'turn_id':
instance['turn_id'],
'f1':
s
})
score_per_instance_json_file = os.path.join(
self.saveFolder, 'score_per_instance.json')
with open(score_per_instance_json_file,
'w') as output_file:
json.dump(score_per_instance, output_file)
self.log("Epoch {0} - dev: F1: {1:.3f} (best F1: {2:.3f})".
format(epoch, f1, best_f1_score))
self.log("Results breakdown\n{0}".format(result))
self.update(batch)
if i % 100 == 0:
self.log(
'updates[{0:6}] train loss[{1:.5f}] remaining[{2}]'.
format(
self.updates, self.train_loss.avg,
str((datetime.now() - startTime) / (i + 1) *
(len(train_batches) - i - 1)).split('.')[0]))
print("PROGRESS: {0:.2f}%".format(100.0 * (epoch + 1) / numEpochs))
print('Config file is at ' + self.opt['confFile'])
def train(self):
"""
train()函数进行批次处理，即对于一个batch的数据，计算当前预测结果并求导更新参数。
每训练1500个batch，利用predict()函数在验证数据上进行一次预测并计算准确率得分。
当前得分最高的模型参数保存在run_id文件夹中。
"""
self.isTrain = True # 标记训练模式
self.getSaveFolder()
self.saveConf()
self.vocab, self.char_vocab, vocab_embedding = self.preproc.load_data(
) # 从CoQAPreprocess中获得词表和编码
self.log('-----------------------------------------------')
self.log('Initializing model...')
self.setup_model(vocab_embedding) # 初始化模型
if 'RESUME' in self.opt: # 在继续训练模式时，读取之前存储的模型
model_path = os.path.join(self.opt['datadir'],
self.opt['MODEL_PATH'])
self.load_model(model_path)
print('Loading train json') # 读取处理好的训练数据
with open(
os.path.join(self.opt['FEATURE_FOLDER'],
self.data_prefix + 'train-preprocessed.json'),
'r') as f:
train_data = json.load(f)
print('Loading dev json') # 读取处理好的验证数据
with open(
os.path.join(self.opt['FEATURE_FOLDER'],
self.data_prefix + 'dev-preprocessed.json'),
'r') as f:
dev_data = json.load(f)
best_f1_score = 0.0 # 训练中得到的验证集上的最高的F1得分
numEpochs = self.opt['EPOCH'] # 配置文件中EPOCH为训练轮数
for epoch in range(self.epoch_start, numEpochs):
self.log('Epoch {}'.format(epoch))
# 训练模式，开启Dropout等功能
self.network.train()
startTime = datetime.now()
# 获得训练数据的batch迭代器
train_batches = BatchGen(self.opt, train_data['data'],
self.use_cuda, self.vocab,
self.char_vocab)
# 获得验证数据的batch迭代器
dev_batches = BatchGen(self.opt,
dev_data['data'],
self.use_cuda,
self.vocab,
self.char_vocab,
evaluation=True)
for i, batch in enumerate(train_batches):
# 每轮结束时或继续训练模式的第一个batch或每1500个batch，在验证数据上预测并计算得分
if i == len(train_batches) - 1 or (
epoch == 0 and i == 0 and
('RESUME' in self.opt)) or (i > 0 and i % 1500 == 0):
print('Saving folder is', self.saveFolder)
print('Evaluating on dev set...')
predictions = []
confidence = []
dev_answer = []
final_json = []
for j, dev_batch in enumerate(dev_batches):
# 预测的结果包括答案文本、答案可能性打分以及JSON格式结果
phrase, phrase_score, pred_json = self.predict(
dev_batch)
final_json.extend(pred_json)
predictions.extend(phrase)
confidence.extend(phrase_score)
dev_answer.extend(dev_batch[-3]) # answer_str
# 计算精确匹配EM和F1得分
result, all_f1s = score(pred=predictions,
truth=dev_answer,
final_json=final_json)
f1 = result['f1']
# 如果F1得分高于之前的所有模型，则存储此模型
if f1 > best_f1_score:
model_file = os.path.join(self.saveFolder,
'best_model.pt')
self.save_for_predict(model_file, epoch)
best_f1_score = f1
pred_json_file = os.path.join(self.saveFolder,
'prediction.json')
with open(pred_json_file, 'w') as output_file:
json.dump(final_json, output_file)
score_per_instance = []
for instance, s in zip(final_json, all_f1s):
score_per_instance.append({
'id':
instance['id'],
'turn_id':
instance['turn_id'],
'f1':
s
})
score_per_instance_json_file = os.path.join(
self.saveFolder, 'score_per_instance.json')
with open(score_per_instance_json_file,
'w') as output_file:
json.dump(score_per_instance, output_file)
self.log('Epoch {0} - dev F1: {1:.3f} (best F1: {2:.3f})'.
format(epoch, f1, best_f1_score))
self.log('Results breakdown\n{0}'.format(result))
# 对本批次进行计算、求导和参数更新
self.update(batch)
if i % 100 == 0:
self.log(
'updates[{0: 6}] train loss[{1: .5f}] remaining[{2}]'.
format(
self.updates, self.train_loss.avg,
str((datetime.now() - startTime) / (i + 1) *
(len(train_batches) - i - 1)).split('.')[0]))
print('PROGRESS: {0:.2F}%'.format(100.0 * (epoch + 1) /
numEpochs))
print('Config file is at ' + self.opt['confFile'])