def __init__(self, embedding: BaseEmbedding = None, hyper_parameters: Dict = None, **kwargs):
if embedding is None:
self.embedding = CustomEmbedding('custom', sequence_length=0, embedding_size=100)
else:
self.embedding = embedding
self.model: Model = None
self._hyper_parameters_ = self.__base_hyper_parameters__.copy()
self._label2idx = {}
self._idx2label = {}
self.model_info = {}
self.task = 'classification'
if hyper_parameters:
self._hyper_parameters_.update(hyper_parameters)
class CustomEmbeddingsTest(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(CustomEmbeddingsTest, self).__init__(*args, **kwargs)
self.embedding = CustomEmbedding('empty_embedding',
sequence_length=SEQUENCE_LENGTH,
embedding_size=100)
def test_build_word2idx(self):
corpus = [['我', '们', '变', '而', '以', '书', '会', '友', '，', '以', '书', '结', '缘', '，',
'把', '欧', '美', '、', '港', '台', '流', '行', '的',
'食', '品', '类', '图', '谱', '、', '画', '册', '、',
'工', '具', '书', '汇', '集', '一', '堂', '。'],
['为', '了', '跟', '踪', '国', '际', '最', '新', '食', '品',
'工', '艺', '、', '流', '行', '趋', '势', '，', '大', '量',
'搜', '集', '海', '外', '专', '业', '书', '刊', '资', '料',
'是', '提', '高', '技', '艺', '的', '捷', '径', '。'],
['其', '中', '线', '装', '古', '籍', '逾', '千', '册',
'；', '民', '国', '出', '版', '物', '几', '百', '种',
'；', '珍', '本', '四', '册', '、', '稀', '见', '本',
'四', '百', '余', '册', '，', '出', '版', '时', '间',
'跨', '越', '三', '百', '余', '年', '。'],
['有', '的', '古', '木', '交', '柯', '，',
'春', '机', '荣', '欣', '，', '从', '诗',
'人', '句', '中', '得', '之', '，', '而',
'入', '画', '中', '，', '观', '之', '令', '人', '心', '驰', '。', '我']]
self.embedding.build_token2idx_dict(x_data=corpus, min_count=2)
def test_build(self):
self.test_build_word2idx()
self.assertEqual(self.embedding.token_count, 33)
self.assertTrue(all(isinstance(x, str) for x in self.embedding.token2idx.keys()))
self.assertTrue(all(isinstance(x, int) for x in self.embedding.token2idx.values()))
self.assertEqual(self.embedding.idx2token[0], k.PAD)
self.assertEqual(self.embedding.idx2token[1], k.BOS)
self.assertEqual(self.embedding.idx2token[2], k.EOS)
self.assertEqual(self.embedding.idx2token[3], k.UNK)
def test_tokenize(self):
self.test_build_word2idx()
sentence = ['我', '想', '看', '电影', '%%##!$#%']
tokens = self.embedding.tokenize(sentence)
logging.info('tokenize test: {} -> {}'.format(sentence, tokens))
self.assertEqual(len(tokens), len(sentence) + 2)
self.assertEqual(tokens[-2], 3, msg='check unk value')
token_list = self.embedding.tokenize([sentence])
self.assertEqual(len(token_list[0]), len(sentence) + 2)
def test_embed(self):
self.test_build_word2idx()
sentence = ['我', '想', '看', '电影', '%%##!$#%']
embedded_sentence = self.embedding.embed(sentence)
logging.info('embed test: {} -> {}'.format(sentence, embedded_sentence))
self.assertEqual(embedded_sentence.shape, (SEQUENCE_LENGTH, self.embedding.embedding_size))
embedded_sentences = self.embedding.embed([sentence])
self.assertEqual(embedded_sentences.shape, (1, SEQUENCE_LENGTH, self.embedding.embedding_size))
def setUpClass(cls):
cls.embedding = CustomEmbedding('empty_embedding',
sequence_length=SEQUENCE_LENGTH,
embedding_size=100)
corpus = [['我', '们', '变', '而', '以', '书', '会', '友', '，', '以', '书', '结', '缘', '，',
'把', '欧', '美', '、', '港', '台', '流', '行', '的',
'食', '品', '类', '图', '谱', '、', '画', '册', '、',
'工', '具', '书', '汇', '集', '一', '堂', '。'],
['为', '了', '跟', '踪', '国', '际', '最', '新', '食', '品',
'工', '艺', '、', '流', '行', '趋', '势', '，', '大', '量',
'搜', '集', '海', '外', '专', '业', '书', '刊', '资', '料',
'是', '提', '高', '技', '艺', '的', '捷', '径', '。'],
['其', '中', '线', '装', '古', '籍', '逾', '千', '册',
'；', '民', '国', '出', '版', '物', '几', '百', '种',
'；', '珍', '本', '四', '册', '、', '稀', '见', '本',
'四', '百', '余', '册', '，', '出', '版', '时', '间',
'跨', '越', '三', '百', '余', '年', '。'],
['有', '的', '古', '木', '交', '柯', '，',
'春', '机', '荣', '欣', '，', '从', '诗',
'人', '句', '中', '得', '之', '，', '而',
'入', '画', '中', '，', '观', '之', '令', '人', '心', '驰', '。', '我']]
cls.embedding.build_token2idx_dict(x_data=corpus, min_count=2)
class BaseModel(object):
__base_hyper_parameters__ = {}
__architect_name__ = ''
@property
def hyper_parameters(self):
return self._hyper_parameters_
def __init__(self,
embedding: BaseEmbedding = None,
hyper_parameters: Dict = None,
**kwargs):
if embedding is None:
self.embedding = CustomEmbedding('custom',
sequence_length=0,
embedding_size=100)
else:
self.embedding = embedding
self.model: Model = None
self._hyper_parameters_ = self.__base_hyper_parameters__.copy()
self._label2idx = {}
self._idx2label = {}
self.model_info = {}
self.task = 'classification'
if hyper_parameters:
self._hyper_parameters_.update(hyper_parameters)
def info(self):
return {
'architect_name': self.__architect_name__,
'task': self.task,
'embedding': self.embedding.info(),
'hyper_parameters': self.hyper_parameters,
'model_info': self.model_info
}
def save(self, model_path: str):
pathlib.Path(model_path).mkdir(exist_ok=True, parents=True)
model_info = self.info()
with open(os.path.join(model_path, 'labels.json'),
'w',
encoding='utf-8') as f:
f.write(json.dumps(self._label2idx, indent=2, ensure_ascii=False))
with open(os.path.join(model_path, 'words.json'),
'w',
encoding='utf-8') as f:
f.write(
json.dumps(self.embedding.token2idx,
indent=2,
ensure_ascii=False))
with open(os.path.join(model_path, 'model.json'),
'w',
encoding='utf-8') as f:
f.write(json.dumps(model_info, indent=2, ensure_ascii=False))
with open(os.path.join(model_path, 'struct.json'),
'w',
encoding='utf-8') as f:
f.write(self.model.to_json())
#self.model.save_weights(os.path.join(model_path, 'weights.h5'))
optimizer_weight_values = None
try:
symbolic_weights = getattr(self.model.optimizer, 'weights')
optimizer_weight_values = K.batch_get_value(symbolic_weights)
except Exception as e:
logger.warn('error occur: {}'.format(e))
traceback.print_tb(e.__traceback__)
logger.warn('No optimizer weights found.')
if optimizer_weight_values is not None:
with open(os.path.join(model_path, 'optimizer.pkl'), 'wb') as f:
pickle.dump(optimizer_weight_values, f)
self.model.save(os.path.join(model_path, 'model.model'))
logger.info('model saved to {}'.format(os.path.abspath(model_path)))
@staticmethod
def create_custom_objects(model_info):
custom_objects = {}
loss = model_info.get('model_info', {}).get('loss')
if loss and loss['name'] == 'weighted_categorical_crossentropy':
loss_f = helper.weighted_categorical_crossentropy(
np.array(loss['weights']))
custom_objects['loss'] = loss_f
architect_name = model_info.get('architect_name')
if architect_name and 'CRF' in architect_name:
custom_objects['CRF'] = CRF
custom_objects['crf_loss'] = crf_loss
custom_objects['crf_accuracy'] = crf_accuracy
embedding = model_info.get('embedding')
if embedding and embedding['embedding_type'] == 'bert':
custom_objects['NonMaskingLayer'] = NonMaskingLayer
custom_objects.update(get_bert_custom_objects())
custom_objects['AttentionWeightedAverage'] = AttentionWeightedAverage
custom_objects['KMaxPooling'] = KMaxPooling
return custom_objects
@classmethod
def load_model(cls, model_path: str):
with open(os.path.join(model_path, 'labels.json'),
'r',
encoding='utf-8') as f:
label2idx = json.load(f)
with open(os.path.join(model_path, 'words.json'),
'r',
encoding='utf-8') as f:
token2idx = json.load(f)
with open(os.path.join(model_path, 'model.json'),
'r',
encoding='utf-8') as f:
model_info = json.load(f)
agent = cls()
custom_objects = cls.create_custom_objects(model_info)
if custom_objects:
logger.debug('prepared custom objects: {}'.format(custom_objects))
try:
agent.model = keras.models.load_model(
os.path.join(model_path, 'model.model'),
custom_objects=custom_objects)
except Exception as e:
logger.warn(
'Error `{}` occured trying directly model loading. Try to rebuild.'
.format(e))
logger.debug('Load model structure from json.')
with open(os.path.join(model_path, 'struct.json'),
'r',
encoding='utf-8') as f:
model_struct = f.read()
agent.model = keras.models.model_from_json(
model_struct, custom_objects=custom_objects)
logger.debug('Build optimizer with model info.')
optimizer_conf = model_info['hyper_parameters'].get(
'optimizer', None)
optimizer = 'adam' #default
if optimizer_conf is not None and isinstance(optimizer_conf, dict):
module_str = optimizer_conf.get('module', 'None')
name_str = optimizer_conf.get('name', 'None')
params = optimizer_conf.get('params', None)
invalid_set = [None, 'None', '', {}]
if not any([
module_str.strip() in invalid_set,
name_str.strip() in invalid_set, params in invalid_set
]):
try:
optimizer = getattr(eval(module_str),
name_str)(**params)
except:
logger.warn(
'Invalid optimizer configuration in model info. Use `adam` as default.'
)
else:
logger.warn(
'No optimizer configuration found in model info. Use `adam` as default.'
)
default_compile_params = {
'loss': 'categorical_crossentropy',
'metrics': ['accuracy']
}
compile_params = model_info['hyper_parameters'].get(
'compile_params', default_compile_params)
logger.debug('Compile model from scratch.')
try:
agent.model.compile(optimizer=optimizer, **compile_params)
except:
logger.warn(
'Failed to compile model. Compile params seems incorrect.')
logger.warn('Use default options `{}` to compile.'.format(
default_compile_params))
agent.model.compile(optimizer=optimizer,
**default_compile_params)
logger.debug('Load model weights.')
agent.model.summary()
agent.model.load_weights(os.path.join(model_path, 'model.model'))
agent.model._make_train_function()
optimizer_weight_values = None
logger.debug('Load optimizer weights.')
try:
with open(os.path.join(model_path, 'optimizer.pkl'),
'rb') as f:
optimizer_weight_values = pickle.load(f)
except Exception as e:
logger.warn(
'Try to load optimizer weights but no optimizer weights file found.'
)
if optimizer_weight_values is not None:
agent.model.optimizer.set_weights(optimizer_weight_values)
else:
logger.warn(
'Rebuild model but optimizer weights missed. Retrain needed.'
)
logger.info('Model rebuild finished.')
agent.embedding.update(model_info.get('embedding', {}))
agent.model.summary()
agent.label2idx = label2idx
agent.embedding.token2idx = token2idx
logger.info('loaded model from {}'.format(os.path.abspath(model_path)))
return agent
class SequenceLabelingModel(object):
__base_hyper_parameters__ = {}
@property
def hyper_parameters(self):
return self._hyper_parameters_
def __init__(self,
embedding: BaseEmbedding = None,
hyper_parameters: Dict = None):
if embedding is None:
self.embedding = CustomEmbedding('custom',
sequence_length=0,
embedding_size=100)
else:
self.embedding = embedding
self.model: Model = None
self._hyper_parameters_ = self.__base_hyper_parameters__.copy()
self._label2idx = {}
self._idx2label = {}
if hyper_parameters:
self._hyper_parameters_.update(hyper_parameters)
self.model_info = {}
@property
def label2idx(self) -> Dict[str, int]:
return self._label2idx
@property
def token2idx(self) -> Dict[str, int]:
return self.embedding.token2idx
@label2idx.setter
def label2idx(self, value):
self._label2idx = value
self._idx2label = dict([(val, key) for (key, val) in value.items()])
def build_model(self, loss_f=None, optimizer=None, metrics=None, **kwargs):
"""
build model function
:return:
"""
raise NotImplementedError()
def build_token2id_label2id_dict(self,
x_train: List[List[str]],
y_train: List[List[str]],
x_validate: List[List[str]] = None,
y_validate: List[str] = None):
x_data = x_train
y_data = y_train
if x_validate:
x_data += x_validate
y_data += y_validate
self.embedding.build_token2idx_dict(x_data, 3)
label_set = []
for seq in y_data:
for y in seq:
if y not in label_set:
label_set.append(y)
label2idx = {k.PAD: 0, k.BOS: 1, k.EOS: 2}
label_set = [i for i in label_set if i not in label2idx]
for label in label_set:
label2idx[label] = len(label2idx)
self.label2idx = label2idx
def convert_labels_to_idx(
self,
label: Union[List[List[str]], List[str]],
add_eos_bos: bool = True) -> Union[List[List[int]], List[int]]:
def tokenize_tokens(seq: List[str]):
tokens = [self._label2idx[i] for i in seq]
if add_eos_bos:
tokens = [self._label2idx[k.BOS]
] + tokens + [self._label2idx[k.EOS]]
return tokens
if isinstance(label[0], str):
return tokenize_tokens(label)
else:
return [tokenize_tokens(l) for l in label]
def convert_idx_to_labels(
self,
idx: Union[List[List[int]], List[int]],
tokens_length: Union[List[int], int],
remove_eos_bos: bool = True) -> Union[List[str], str]:
def reverse_tokenize_tokens(idx_item, seq_length):
if remove_eos_bos:
seq = idx_item[1:1 + seq_length]
else:
seq = idx_item
tokens = [self._idx2label[i] for i in seq]
return tokens
if isinstance(idx[0], int):
return reverse_tokenize_tokens(idx, tokens_length)
else:
labels = []
for index in range(len(idx)):
idx_item = idx[index]
seq_length = tokens_length[index]
labels.append(reverse_tokenize_tokens(idx_item, seq_length))
return labels
def get_data_generator(self,
x_data: List[List[str]],
y_data: List[List[str]],
batch_size: int = 64,
is_bert: bool = False):
while True:
page_list = list(range(len(x_data) // batch_size + 1))
random.shuffle(page_list)
for page in page_list:
start_index = page * batch_size
end_index = start_index + batch_size
target_x = x_data[start_index:end_index]
target_y = y_data[start_index:end_index]
if len(target_x) == 0:
target_x = x_data[0:batch_size]
target_y = y_data[0:batch_size]
tokenized_x = self.embedding.tokenize(target_x)
tokenized_y = self.convert_labels_to_idx(target_y)
padded_x = sequence.pad_sequences(
tokenized_x,
maxlen=self.embedding.sequence_length,
padding='post')
padded_y = sequence.pad_sequences(
tokenized_y,
maxlen=self.embedding.sequence_length,
padding='post')
one_hot_y = to_categorical(padded_y,
num_classes=len(self.label2idx))
if is_bert:
padded_x_seg = np.zeros(
shape=(len(padded_x), self.embedding.sequence_length))
x_input_data = [padded_x, padded_x_seg]
else:
x_input_data = padded_x
yield (x_input_data, one_hot_y)
def fit(self,
x_train: List[List[str]],
y_train: List[List[str]],
x_validate: List[List[str]] = None,
y_validate: List[List[str]] = None,
batch_size: int = 64,
epochs: int = 5,
labels_weight: bool = None,
default_labels_weight: float = 50.0,
fit_kwargs: Dict = None,
**kwargs):
"""
:param x_train: list of training data.
:param y_train: list of training target label data.
:param batch_size: batch size for trainer model
:param epochs: Number of epochs to train the model.
:param x_validate: list of validation data.
:param y_validate: list of validation target label data.
:param y_validate: list of validation target label data.
:param y_validate: list of validation target label data.
:param labels_weight: set class weights for imbalanced classes
:param default_labels_weight: default weight for labels not in labels_weight dict
:param fit_kwargs: additional kwargs to be passed to
:func:`~keras.models.Model.fit`
:return:
"""
assert len(x_train) == len(y_train)
self.build_token2id_label2id_dict(x_train, y_train, x_validate,
y_validate)
if len(x_train) < batch_size:
batch_size = len(x_train) // 2
if not self.model:
if self.embedding.sequence_length == 0:
self.embedding.sequence_length = sorted(
[len(y) for y in y_train])[int(0.95 * len(y_train))]
logging.info('sequence length set to {}'.format(
self.embedding.sequence_length))
if labels_weight:
weights = []
initial_weights = {k.PAD: 1, k.BOS: 1, k.EOS: 1, 'O': 1}
for label in self.label2idx.keys():
weights.append(
initial_weights.get(label, default_labels_weight))
loss_f = helper.weighted_categorical_crossentropy(
np.array(weights))
self.model_info['loss'] = {
'func': 'weighted_categorical_crossentropy',
'weights': weights
}
self.build_model(loss_f=loss_f,
metrics=['categorical_accuracy', 'acc'])
else:
self.build_model()
train_generator = self.get_data_generator(
x_train, y_train, batch_size, is_bert=self.embedding.is_bert)
if fit_kwargs is None:
fit_kwargs = {}
if x_validate:
validation_generator = self.get_data_generator(
x_validate,
y_validate,
batch_size,
is_bert=self.embedding.is_bert)
fit_kwargs['validation_data'] = validation_generator
fit_kwargs['validation_steps'] = len(x_validate) // batch_size
self.model.fit_generator(train_generator,
steps_per_epoch=len(x_train) // batch_size,
epochs=epochs,
**fit_kwargs)
def predict(self,
sentence: Union[List[str], List[List[str]]],
batch_size=None):
tokens = self.embedding.tokenize(sentence)
is_list = not isinstance(sentence[0], str)
if is_list:
seq_length = [len(item) for item in sentence]
padded_tokens = sequence.pad_sequences(
tokens, maxlen=self.embedding.sequence_length, padding='post')
else:
seq_length = [len(sentence)]
padded_tokens = sequence.pad_sequences(
[tokens],
maxlen=self.embedding.sequence_length,
padding='post')
if self.embedding.is_bert:
x = [
padded_tokens,
np.zeros(shape=(len(padded_tokens),
self.embedding.sequence_length))
]
else:
x = padded_tokens
predict_result = self.model.predict(x,
batch_size=batch_size).argmax(-1)
labels = self.convert_idx_to_labels(predict_result, seq_length)
if is_list:
return labels
else:
return labels[0]
def evaluate(self,
x_data,
y_data,
batch_size=None) -> Tuple[float, float, Dict]:
y_pred = self.predict(x_data, batch_size=batch_size)
weighted_f1 = f1_score(y_data, y_pred)
weighted_recall = recall_score(y_data, y_pred)
report = classification_report(y_data, y_pred)
print(classification_report(y_data, y_pred))
return weighted_f1, weighted_recall, report
def save(self, model_path: str):
pathlib.Path(model_path).mkdir(exist_ok=True, parents=True)
with open(os.path.join(model_path, 'labels.json'),
'w',
encoding='utf-8') as f:
f.write(json.dumps(self.label2idx, indent=2, ensure_ascii=False))
with open(os.path.join(model_path, 'words.json'),
'w',
encoding='utf-8') as f:
f.write(
json.dumps(self.embedding.token2idx,
indent=2,
ensure_ascii=False))
with open(os.path.join(model_path, 'model.json'),
'w',
encoding='utf-8') as f:
f.write(json.dumps(self.model_info, indent=2, ensure_ascii=False))
self.model.save(os.path.join(model_path, 'model.model'))
logging.info('model saved to {}'.format(os.path.abspath(model_path)))
@staticmethod
def create_custom_objects(model_info):
custom_objects = {}
loss = model_info.get('loss')
if loss and loss['name'] == 'weighted_categorical_crossentropy':
loss_f = helper.weighted_categorical_crossentropy(
np.array(loss['weights']))
custom_objects['loss'] = loss_f
if loss and loss['name'] == 'crf':
custom_objects['CRF'] = CRF
custom_objects['crf_loss'] = crf_loss
custom_objects['crf_viterbi_accuracy'] = CRF(128).accuracy
return custom_objects
@staticmethod
def load_model(model_path: str):
with open(os.path.join(model_path, 'labels.json'),
'r',
encoding='utf-8') as f:
label2idx = json.load(f)
with open(os.path.join(model_path, 'words.json'),
'r',
encoding='utf-8') as f:
token2idx = json.load(f)
with open(os.path.join(model_path, 'model.json'),
'r',
encoding='utf-8') as f:
model_info = json.load(f)
agent = SequenceLabelingModel()
custom_objects = SequenceLabelingModel.create_custom_objects(
model_info)
if custom_objects:
logging.debug('prepared custom objects: {}'.format(custom_objects))
agent.model = keras.models.load_model(os.path.join(
model_path, 'model.model'),
custom_objects=custom_objects)
agent.model.summary()
agent.embedding.sequence_length = agent.model.input_shape[-1]
agent.label2idx = label2idx
agent.embedding.token2idx = token2idx
logging.info('loaded model from {}'.format(
os.path.abspath(model_path)))
return agent
def __init__(self, *args, **kwargs):
super(CustomEmbeddingsTest, self).__init__(*args, **kwargs)
self.embedding = CustomEmbedding('empty_embedding',
sequence_length=SEQUENCE_LENGTH,
embedding_size=100)
class BaseModel(object):
__base_hyper_parameters__ = {}
__architect_name__ = ''
@property
def hyper_parameters(self):
return self._hyper_parameters_
def __init__(self,
embedding: BaseEmbedding = None,
hyper_parameters: Dict = None,
**kwargs):
if embedding is None:
self.embedding = CustomEmbedding('custom',
sequence_length=0,
embedding_size=100)
else:
self.embedding = embedding
self.model: Model = None
self._hyper_parameters_ = self.__base_hyper_parameters__.copy()
self._label2idx = {}
self._idx2label = {}
self.model_info = {}
self.task = 'classification'
if hyper_parameters:
self._hyper_parameters_.update(hyper_parameters)
def info(self):
return {
'architect_name': self.__architect_name__,
'task': self.task,
'embedding': self.embedding.info(),
'hyper_parameters': self.hyper_parameters,
'model_info': self.model_info
}
def save(self, model_path: str):
pathlib.Path(model_path).mkdir(exist_ok=True, parents=True)
model_info = self.info()
with open(os.path.join(model_path, 'labels.json'),
'w',
encoding='utf-8') as f:
f.write(json.dumps(self._label2idx, indent=2, ensure_ascii=False))
with open(os.path.join(model_path, 'words.json'),
'w',
encoding='utf-8') as f:
f.write(
json.dumps(self.embedding.token2idx,
indent=2,
ensure_ascii=False))
with open(os.path.join(model_path, 'model.json'),
'w',
encoding='utf-8') as f:
f.write(json.dumps(model_info, indent=2, ensure_ascii=False))
self.model.save(os.path.join(model_path, 'model.model'))
logging.info('model saved to {}'.format(os.path.abspath(model_path)))
@staticmethod
def create_custom_objects(model_info):
custom_objects = {}
loss = model_info.get('model_info', {}).get('loss')
if loss and loss['name'] == 'weighted_categorical_crossentropy':
loss_f = helper.weighted_categorical_crossentropy(
np.array(loss['weights']))
custom_objects['loss'] = loss_f
architect_name = model_info.get('architect_name')
if architect_name and 'CRF' in architect_name:
custom_objects['CRF'] = CRF
custom_objects['crf_loss'] = crf_loss
custom_objects['crf_accuracy'] = crf_accuracy
embedding = model_info.get('embedding')
if embedding and embedding['embedding_type'] == 'bert':
custom_objects['NonMaskingLayer'] = helper.NonMaskingLayer
custom_objects.update(get_bert_custom_objects())
return custom_objects
@classmethod
def load_model(cls, model_path: str):
with open(os.path.join(model_path, 'labels.json'),
'r',
encoding='utf-8') as f:
label2idx = json.load(f)
with open(os.path.join(model_path, 'words.json'),
'r',
encoding='utf-8') as f:
token2idx = json.load(f)
with open(os.path.join(model_path, 'model.json'),
'r',
encoding='utf-8') as f:
model_info = json.load(f)
agent = cls()
custom_objects = cls.create_custom_objects(model_info)
if custom_objects:
logging.debug('prepared custom objects: {}'.format(custom_objects))
agent.model = keras.models.load_model(os.path.join(
model_path, 'model.model'),
custom_objects=custom_objects)
agent.embedding.update(model_info.get('embedding', {}))
agent.model.summary()
agent.label2idx = label2idx
agent.embedding.token2idx = token2idx
logging.info('loaded model from {}'.format(
os.path.abspath(model_path)))
return agent