def train(config):
""" model training """
config.vocab_size = len(open(config.vocab_path).readlines())
bow_loss, kl_loss, nll_loss, final_loss= knowledge_seq2seq(config)
bow_loss.persistable = True
kl_loss.persistable = True
nll_loss.persistable = True
final_loss.persistable = True
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=config.grad_clip))
optimizer = fluid.optimizer.Adam(learning_rate=config.lr)
if config.stage == 0:
print("stage 0")
optimizer.minimize(bow_loss)
else:
print("stage 1")
optimizer.minimize(final_loss)
opt_var_name_list = optimizer.get_opti_var_name_list()
if config.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
param_list = main_program.block(0).all_parameters()
param_name_list = [p.name for p in param_list]
init_model(config, param_name_list, place)
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
train_generator = processors.data_generator(
batch_size=config.batch_size,
phase="train",
shuffle=True)
valid_generator = processors.data_generator(
batch_size=config.batch_size,
phase="dev",
shuffle=False)
model_handle = [exe, place, bow_loss, kl_loss, nll_loss, final_loss]
train_loop(config,
train_generator, valid_generator,
main_program, inference_program,
model_handle, param_name_list, opt_var_name_list)
def __init__(self):
"""
Init whatever you need here
"""
vocab_file = 'data/vocab.txt'
with codecs.open(vocab_file, 'r', 'utf-8') as f:
vocab = [i.strip() for i in f.readlines() if len(i.strip()) != 0]
self.vocab = vocab
self.freqs = dict(zip(self.vocab[::-1], range(len(self.vocab))))
# Our code are as follows
config = Config()
torch.cuda.set_device(device=config.gpu)
self.config = config
# Data definition
self.corpus = KnowledgeCorpus(data_dir=config.data_dir,
data_prefix=config.data_prefix,
min_freq=0,
max_vocab_size=config.max_vocab_size,
vocab_file=config.vocab_file,
min_len=config.min_len,
max_len=config.max_len,
embed_file=config.embed_file,
share_vocab=config.share_vocab)
# Model definition
self.model = Seq2Seq(src_vocab_size=self.corpus.SRC.vocab_size,
tgt_vocab_size=self.corpus.TGT.vocab_size,
embed_size=config.embed_size,
hidden_size=config.hidden_size,
padding_idx=self.corpus.padding_idx,
num_layers=config.num_layers,
bidirectional=config.bidirectional,
attn_mode=config.attn,
with_bridge=config.with_bridge,
tie_embedding=config.tie_embedding,
dropout=config.dropout,
use_gpu=config.use_gpu)
print(self.model)
self.model.load(config.ckpt)
# Generator definition
self.generator = TopKGenerator(model=self.model,
src_field=self.corpus.SRC,
tgt_field=self.corpus.TGT,
cue_field=self.corpus.CUE,
beam_size=config.beam_size,
max_length=config.max_dec_len,
ignore_unk=config.ignore_unk,
length_average=config.length_average,
use_gpu=config.use_gpu)
self.BOS = self.generator.BOS
self.EOS = self.generator.EOS
self.stoi = self.corpus.SRC.stoi
self.itos = self.corpus.SRC.itos
def load():
""" load model for predict """
config = model_config()
config.vocab_size = len(open(config.vocab_path).readlines())
final_score, final_ids, final_index = knowledge_seq2seq(config)
final_score.persistable = True
final_ids.persistable = True
final_index.persistable = True
main_program = fluid.default_main_program()
if config.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
fluid.io.load_params(executor=exe, dirname=config.model_path, main_program=main_program)
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
# load dict
id_dict_array = load_id2str_dict(config.vocab_path)
model_handle = [exe, place, final_score, final_ids, final_index, processors, id_dict_array]
return model_handle
def main():
config = model_config()
if config.check:
config.save_dir = "./tmp/"
config.use_gpu = torch.cuda.is_available() and config.gpu >= 0
device = config.gpu
torch.cuda.set_device(device)
# Data definition
corpus = KnowledgeCorpus(data_dir=config.data_dir, data_prefix=config.data_prefix,
min_freq=3, max_vocab_size=config.max_vocab_size,
min_len=config.min_len, max_len=config.max_len,
embed_file=config.embed_file, with_label=config.with_label,
share_vocab=config.share_vocab)
corpus.load()
if config.test and config.ckpt:
corpus.reload(data_type='test')
train_iter = corpus.create_batches(
config.batch_size, "train", shuffle=True, device=device)
valid_iter = corpus.create_batches(
config.batch_size, "valid", shuffle=False, device=device)
test_iter = corpus.create_batches(
config.batch_size, "test", shuffle=False, device=device)
# Model definition
model = KnowledgeSeq2Seq(src_vocab_size=corpus.SRC.vocab_size,
tgt_vocab_size=corpus.TGT.vocab_size,
embed_size=config.embed_size, hidden_size=config.hidden_size,
padding_idx=corpus.padding_idx,
num_layers=config.num_layers, bidirectional=config.bidirectional,
attn_mode=config.attn, with_bridge=config.with_bridge,
tie_embedding=config.tie_embedding, dropout=config.dropout,
use_gpu=config.use_gpu,
use_bow=config.use_bow, use_dssm=config.use_dssm,
use_pg=config.use_pg, use_gs=config.use_gs,
pretrain_epoch=config.pretrain_epoch,
use_posterior=config.use_posterior,
weight_control=config.weight_control,
concat=config.decode_concat)
model_name = model.__class__.__name__
# Generator definition
generator = TopKGenerator(model=model,
src_field=corpus.SRC, tgt_field=corpus.TGT, cue_field=corpus.CUE,
max_length=config.max_dec_len, ignore_unk=config.ignore_unk,
length_average=config.length_average, use_gpu=config.use_gpu)
# Interactive generation testing
if config.interact and config.ckpt:
model.load(config.ckpt)
return generator
# Testing
elif config.test and config.ckpt:
print(model)
model.load(config.ckpt)
print("Testing ...")
metrics, scores = evaluate(model, test_iter)
print(metrics.report_cum())
print("Generating ...")
evaluate_generation(generator, test_iter, save_file=config.gen_file, verbos=True)
else:
# Load word embeddings
if config.use_embed and config.embed_file is not None:
model.encoder.embedder.load_embeddings(
corpus.SRC.embeddings, scale=0.03)
model.decoder.embedder.load_embeddings(
corpus.TGT.embeddings, scale=0.03)
# Optimizer definition
optimizer = getattr(torch.optim, config.optimizer)(
model.parameters(), lr=config.lr)
# Learning rate scheduler
if config.lr_decay is not None and 0 < config.lr_decay < 1.0:
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
factor=config.lr_decay, patience=1, verbose=True, min_lr=1e-5)
else:
lr_scheduler = None
# Save directory
date_str, time_str = datetime.now().strftime("%Y%m%d-%H%M%S").split("-")
result_str = "{}-{}".format(model_name, time_str)
if not os.path.exists(config.save_dir):
os.makedirs(config.save_dir)
# Logger definition
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG, format="%(message)s")
fh = logging.FileHandler(os.path.join(config.save_dir, "train.log"))
logger.addHandler(fh)
# Save config
params_file = os.path.join(config.save_dir, "params.json")
with open(params_file, 'w') as fp:
json.dump(config.__dict__, fp, indent=4, sort_keys=True)
print("Saved params to '{}'".format(params_file))
logger.info(model)
# Train
logger.info("Training starts ...")
trainer = Trainer(model=model, optimizer=optimizer, train_iter=train_iter,
valid_iter=valid_iter, logger=logger, generator=generator,
valid_metric_name="-loss", num_epochs=config.num_epochs,
save_dir=config.save_dir, log_steps=config.log_steps,
valid_steps=config.valid_steps, grad_clip=config.grad_clip,
lr_scheduler=lr_scheduler, save_summary=False)
if config.ckpt is not None:
trainer.load(file_prefix=config.ckpt)
trainer.train()
logger.info("Training done!")
# Test
logger.info("")
trainer.load(os.path.join(config.save_dir, "best"))
logger.info("Testing starts ...")
metrics, scores = evaluate(model, test_iter)
logger.info(metrics.report_cum())
logger.info("Generation starts ...")
test_gen_file = os.path.join(config.save_dir, "test.result")
evaluate_generation(generator, test_iter, save_file=test_gen_file, verbos=True)
def train(config):
""" model training """
config.vocab_size = len(open(config.vocab_path).readlines())
#搭建网络：Bi-GRU Utterance encoder +Bi-GRU KG encoder +层级GRU decoder
bow_loss, kl_loss, nll_loss, final_loss= knowledge_seq2seq(config)
#持久性变量（Persistables）是一种在每次迭代结束后均不会被删除的变量
bow_loss.persistable = True
kl_loss.persistable = True
nll_loss.persistable = True
final_loss.persistable = True
#fluid.layers 接口中添加的op和variable会存储在 default main program 中
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
#给指定参数做梯度裁剪
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=config.grad_clip))
optimizer = fluid.optimizer.Adam(learning_rate=config.lr)
if config.stage == 0:
print("stage 0")
optimizer.minimize(bow_loss)
else:
print("stage 1")
optimizer.minimize(final_loss)
#优化器的训练参数如lr
opt_var_name_list = optimizer.get_opti_var_name_list()
if config.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = Executor(place)
#初始化 default_startup_program函数可以获取默认/全局 startup Program (初始化启动程序)。
exe.run(framework.default_startup_program())
#block0 表示一段代码的最外层块
param_list = main_program.block(0).all_parameters()
param_name_list = [p.name for p in param_list]
#TODO：init包含
init_model(config, param_name_list, place)
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
#train_generator为yeild 生成函数
#进行了如下操作：
#读取stream record file
#对读入的文本进行tokennize；根据max min len 进行过滤
#并根据词汇表把src\tgt\cue文本串（chatpath+knowledge+":"+history\ response\ KG cue）转为数字串
##进行padding并返回padding后的串和每个串的原长
train_generator = processors.train_generator(
batch_size=config.batch_size,
phase="train",
shuffle=True)
valid_generator = processors.data_generator(
batch_size=config.batch_size,
phase="dev",
shuffle=False)
model_handle = [exe, place, bow_loss, kl_loss, nll_loss, final_loss]
#在训练过程中，每config.valid_steps 个batch（步）进行一次valid，并储存一次最好模型
train_loop(config,
train_generator, valid_generator,
main_program, inference_program,
model_handle, param_name_list, opt_var_name_list)
def test(config):
""" test """
batch_size = config.batch_size
config.vocab_size = len(open(config.vocab_path).readlines())
final_score, final_ids, final_index = knowledge_seq2seq(config)
final_score.persistable = True
final_ids.persistable = True
final_index.persistable = True
main_program = fluid.default_main_program()
if config.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
fluid.io.load_params(executor=exe, dirname=config.model_path,
main_program=main_program)
print("laod params finsihed")
# test data generator
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
test_generator = processors.data_generator(
batch_size=config.batch_size,
phase="test",
shuffle=False)
# load dict
id_dict_array = load_id2str_dict(config.vocab_path)
out_file = config.output
fout = open(out_file, 'w')
for batch_id, data in enumerate(test_generator()):
data_feed, sent_num = build_data_feed(data, place, batch_size=batch_size)
if data_feed is None:
break
out = exe.run(feed=data_feed,
fetch_list=[final_score.name, final_ids.name, final_index.name])
batch_score = out[0]
batch_ids = out[1]
batch_pre_index = out[2]
batch_score_arr = np.split(batch_score, batch_size, axis=1)
batch_ids_arr = np.split(batch_ids, batch_size, axis=1)
batch_pre_index_arr = np.split(batch_pre_index, batch_size, axis=1)
index = 0
for (score, ids, pre_index) in zip(batch_score_arr, batch_ids_arr, batch_pre_index_arr):
trace_ids, trace_score = trace_fianl_result(score, ids, pre_index, topk=1, EOS=3)
fout.write(id_to_text(trace_ids[0][:-1], id_dict_array))
fout.write('\n')
index += 1
if index >= sent_num:
break
fout.close()
class Model:
"""
This is an example model. It reads predefined dictionary and predict a fixed distribution.
For a correct evaluation, each team should implement 3 functions:
next_word_probability
gen_response
"""
def __init__(self):
"""
Init whatever you need here
"""
vocab_file = 'data/vocab.txt'
with codecs.open(vocab_file, 'r', 'utf-8') as f:
vocab = [i.strip() for i in f.readlines() if len(i.strip()) != 0]
self.vocab = vocab
self.freqs = dict(zip(self.vocab[::-1], range(len(self.vocab))))
# Our code are as follows
config = Config()
torch.cuda.set_device(device=config.gpu)
self.config = config
# Data definition
self.corpus = KnowledgeCorpus(data_dir=config.data_dir,
data_prefix=config.data_prefix,
min_freq=0,
max_vocab_size=config.max_vocab_size,
vocab_file=config.vocab_file,
min_len=config.min_len,
max_len=config.max_len,
embed_file=config.embed_file,
share_vocab=config.share_vocab)
# Model definition
self.model = Seq2Seq(src_vocab_size=self.corpus.SRC.vocab_size,
tgt_vocab_size=self.corpus.TGT.vocab_size,
embed_size=config.embed_size,
hidden_size=config.hidden_size,
padding_idx=self.corpus.padding_idx,
num_layers=config.num_layers,
bidirectional=config.bidirectional,
attn_mode=config.attn,
with_bridge=config.with_bridge,
tie_embedding=config.tie_embedding,
dropout=config.dropout,
use_gpu=config.use_gpu)
print(self.model)
self.model.load(config.ckpt)
# Generator definition
self.generator = TopKGenerator(model=self.model,
src_field=self.corpus.SRC,
tgt_field=self.corpus.TGT,
cue_field=self.corpus.CUE,
beam_size=config.beam_size,
max_length=config.max_dec_len,
ignore_unk=config.ignore_unk,
length_average=config.length_average,
use_gpu=config.use_gpu)
self.BOS = self.generator.BOS
self.EOS = self.generator.EOS
self.stoi = self.corpus.SRC.stoi
self.itos = self.corpus.SRC.itos
def next_word_probability(self, context, partial_out):
"""
Return probability distribution over next words given a partial true output.
This is used to calculate the per-word perplexity.
:param context: dict, contexts containing the dialogue history and personal
profile of each speaker
this dict contains following keys:
context['dialog']: a list of string, dialogue histories (tokens in each utterances
are separated using spaces).
context['uid']: a list of int, indices to the profile of each speaker
context['profile']: a list of dict, personal profiles for each speaker
context['responder_profile']: dict, the personal profile of the responder
:param partial_out: list, previous "true" words
:return: a list, the first element is a dict, where each key is a word and each value is a probability
score for that word. Unset keys assume a probability of zero.
the second element is the probability for the EOS token
e.g.
context:
{ "dialog": [ ["How are you ?"], ["I am fine , thank you . And you ?"] ],
"uid": [0, 1],
"profile":[ { "loc":"Beijing", "gender":"male", "tag":"" },
{ "loc":"Shanghai", "gender":"female", "tag":"" } ],
"responder_profile":{ "loc":"Beijing", "gender":"male", "tag":"" }
}
partial_out:
['I', 'am']
==> {'fine': 0.9}, 0.1
"""
test_raw = self.read_data(context)
test_data = self.corpus.build_examples(test_raw, data_type='test')
dataset = Dataset(test_data)
data_iter = dataset.create_batches(batch_size=1,
shuffle=False,
device=self.config.gpu)
inputs = None
for batch in data_iter:
inputs = batch
break
partial_out_idx = [
self.stoi[s] if s in self.stoi.keys() else self.stoi['<unk>']
for s in partial_out
]
# switch the model to evaluate mode
self.model.eval()
with torch.no_grad():
enc_outputs, dec_init_state = self.model.encode(inputs)
long_tensor_type = torch.cuda.LongTensor if self.config.use_gpu else torch.LongTensor
# Initialize the input vector
input_var = long_tensor_type([self.BOS] * 1)
# Inflate the initial hidden states to be of size: (1, H)
dec_state = dec_init_state.inflate(1)
for t in range(len(partial_out_idx)):
# Run the RNN one step forward
output, dec_state, attn = self.model.decode(
input_var, dec_state)
input_var = long_tensor_type([partial_out_idx[t]])
output, dec_state, attn = self.model.decode(input_var, dec_state)
log_softmax_output = output.squeeze(1)
log_softmax_output = log_softmax_output.cpu().numpy()
prob_output = [math.exp(i) for i in log_softmax_output[0]]
# The first 4 tokens are: '<pad>' '<unk>' '<bos>' '<eos>'
freq_dict = {}
for i in range(4, len(self.itos)):
freq_dict[self.itos[i]] = prob_output[i]
eos_prob = prob_output[3]
return freq_dict, eos_prob
def gen_response(self, contexts):
"""
Return a list of responses to each context.
:param contexts: list, a list of context, each context is a dict that contains the dialogue history and personal
profile of each speaker
this dict contains following keys:
context['dialog']: a list of string, dialogue histories (tokens in each utterances
are separated using spaces).
context['uid']: a list of int, indices to the profile of each speaker
context['profile']: a list of dict, personal profiles for each speaker
context['responder_profile']: dict, the personal profile of the responder
:return: list, responses for each context, each response is a list of tokens.
e.g.
contexts:
[{ "dialog": [ ["How are you ?"], ["I am fine , thank you . And you ?"] ],
"uid": [0, 1],
"profile":[ { "loc":"Beijing", "gender":"male", "tag":"" },
{ "loc":"Shanghai", "gender":"female", "tag":"" } ],
"responder_profile":{ "loc":"Beijing", "gender":"male", "tag":"" }
}]
==> [['I', 'am', 'fine', 'too', '!']]
"""
test_raw = self.read_data(contexts[0])
test_data = self.corpus.build_examples(test_raw, data_type='test')
dataset = Dataset(test_data)
data_iter = dataset.create_batches(batch_size=1,
shuffle=False,
device=self.config.gpu)
results = self.generator.generate(batch_iter=data_iter)
res = [result.preds[0].split(" ") for result in results]
return res
@staticmethod
def read_data(dialog):
history = dialog["dialog"]
uid = [int(i) for i in dialog["uid"]]
if "responder_profile" in dialog.keys():
responder_profile = dialog["responder_profile"]
elif "response_profile" in dialog.keys():
responder_profile = dialog["response_profile"]
else:
raise ValueError("No responder_profile or response_profile!")
src = ""
for idx, sent in zip(uid, history):
sent_content = sent[0]
src += sent_content
src += ' '
src = src.strip()
tgt = "NULL"
filter_knowledge = []
if type(responder_profile["tag"]) is list:
filter_knowledge.append(' '.join(
responder_profile["tag"][0].split(';')))
else:
filter_knowledge.append(' '.join(
responder_profile["tag"].split(';')))
filter_knowledge.append(responder_profile["loc"])
data = [{'src': src, 'tgt': tgt, 'cue': filter_knowledge}]
return data
def train(config):
""" model training """
config.vocab_size = len(open(config.vocab_path).readlines())
#计算行数，即词的个数
bow_loss, kl_loss, nll_loss, final_loss= knowledge_seq2seq(config)
bow_loss.persistable = True
kl_loss.persistable = True
nll_loss.persistable = True
final_loss.persistable = True
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
#Program是Paddle Fluid对于计算图的一种静态描述
#1. Program.clone() 方法不会克隆例如 DataLoader 这样的数据读取相关的部分，这可能会造成的数据读取部分在克隆后丢失
#2. 此API当 for_test=True 时将会裁剪部分OP和变量。为防止错误的裁剪，推荐在 append_backward 和执行优化器之前使用 clone(for_test=True) 。
#当 for_test=True 时创建一个新的、仅包含当前Program前向内容的Program。否则创建一个新的，和当前Program完全相同的Program
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=config.grad_clip))
optimizer = fluid.optimizer.Adam(learning_rate=config.lr)
#给指定参数做梯度裁剪。
# 此API对位置使用的要求较高，其必须位于组建网络之后， minimize 之前，因此在未来版本中可能被删除，故不推荐使用。
# 推荐在 optimizer 初始化时设置梯度裁剪。 有三种裁剪策略： GradientClipByGlobalNorm 、 GradientClipByNorm 、
# GradientClipByValue 。 如果在 optimizer 中设置过梯度裁剪，又使用了 set_gradient_clip ，set_gradient_clip 将不会生效。
#深度神经网络采用链式法则进行参数求导的方式并不是绝对安全的，有时会出现梯度消失或者梯度爆炸的情况。
# 其中梯度爆炸问题的常见应对方式为“梯度裁剪”，也就是通过“clip”方式来防止迭代中梯度值过大。
if config.stage == 0:
print("stage 0")
optimizer.minimize(bow_loss)
else:
print("stage 1")
optimizer.minimize(final_loss)
#指定优化器最小化的loss
opt_var_name_list = optimizer.get_opti_var_name_list()
if config.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
#Gpu or Cpu
exe = Executor(place)
exe.run(framework.default_startup_program())
#执行程序
param_list = main_program.block(0).all_parameters()
#Block 是高级语言中变量作用域的概念，在编程语言中，Block是一对大括号，其中包含局部变量定义和一系列指令或
# 操作符。编程语言中的控制流结构 if-else 和 for 在深度学习中可以被等效为：
# Fluid 中的 Block 描述了一组以顺序、选择或是循环执行的 Operator 以及 Operator 操作的对象：Tensor。
param_name_list = [p.name for p in param_list]
#模型列表
init_model(config, param_name_list, place)
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
#知识库对象
train_generator = processors.data_generator(
batch_size=config.batch_size,
phase="train",
shuffle=True)
#训练数据生成器
valid_generator = processors.data_generator(
batch_size=config.batch_size,
phase="dev",
shuffle=False)
#验证数据生成器
model_handle = [exe, place, bow_loss, kl_loss, nll_loss, final_loss]
train_loop(config,
train_generator, valid_generator,
main_program, inference_program,
model_handle, param_name_list, opt_var_name_list)
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
#类似C，起运行作用，exe已经说明了意思
fluid.io.load_params(executor=exe, dirname=config.model_path, main_program=main_program)
#参数:
#executor (Executor) – 加载模型参数的 executor （详见 执行引擎 ） 。
#dirname (str) – 模型参数的存储路径。
#main_program (Program，可选) – 筛选模型参数变量所依据的 Program （详见 基础概念 ）。若为None, 则使用全局默认的 default_main_program 。默认值为None。
#filename (str，可选) – 若模型参数是以若干文件形式存储在 dirname 指定的目录下，则设置 filename 值为None。反之，需要通过 filename 来指明单一模型参数存储文件的名称。 默认值为None。
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
# load dict
id_dict_array = load_id2str_dict(config.vocab_path)
model_handle = [exe, place, final_score, final_ids, final_index, processors, id_dict_array]
#返回一个包含以上成分的列表
return model_handle
def predict(model_handle, text):
""" predict for text by model_handle """
batch_size = 1