class BaseModel(object):
"""Base Sequence Labeling Model"""
@classmethod
def get_default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]:
raise NotImplementedError
def info(self):
model_json_str = self.tf_model.to_json()
return {
'config': {
'hyper_parameters': self.hyper_parameters,
},
'tf_model': json.loads(model_json_str),
'embedding': self.embedding.info(),
'class_name': self.__class__.__name__,
'module': self.__class__.__module__,
'tf_version': tf.__version__,
'kashgari_version': kashgari.__version__
}
@property
def task(self):
return self.embedding.task
@property
def token2idx(self) -> Dict[str, int]:
return self.embedding.token2idx
@property
def label2idx(self) -> Dict[str, int]:
return self.embedding.label2idx
@property
def pre_processor(self):
warnings.warn(
"The 'pre_processor' property is deprecated, "
"use 'processor' instead", DeprecationWarning, 2)
"""Deprecated. Use `self.processor` instead."""
return self.embedding.processor
@property
def processor(self):
return self.embedding.processor
def __init__(self,
embedding: Optional[Embedding] = None,
hyper_parameters: Optional[Dict[str, Dict[str, Any]]] = None):
"""
Args:
embedding: model embedding
hyper_parameters: a dict of hyper_parameters.
Examples:
You could change customize hyper_parameters like this::
# get default hyper_parameters
hyper_parameters = BLSTMModel.get_default_hyper_parameters()
# change lstm hidden unit to 12
hyper_parameters['layer_blstm']['units'] = 12
# init new model with customized hyper_parameters
labeling_model = BLSTMModel(hyper_parameters=hyper_parameters)
labeling_model.fit(x, y)
"""
if embedding is None:
self.embedding = BareEmbedding(task=self.__task__)
else:
self.embedding = embedding
self.tf_model: keras.Model = None
self.hyper_parameters = self.get_default_hyper_parameters()
self.model_info = {}
if hyper_parameters:
self.hyper_parameters.update(hyper_parameters)
def build_model(self,
x_train: Union[Tuple[List[List[str]], ...],
List[List[str]]],
y_train: Union[List[List[str]], List[str]],
x_validate: Union[Tuple[List[List[str]], ...],
List[List[str]]] = None,
y_validate: Union[List[List[str]], List[str]] = None):
"""
Build model with corpus
Args:
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
Returns:
"""
if x_validate is not None and not isinstance(x_validate, tuple):
self.embedding.analyze_corpus(x_train + x_validate,
y_train + y_validate)
else:
self.embedding.analyze_corpus(x_train, y_train)
if self.tf_model is None:
self.build_model_arc()
self.compile_model()
def build_multi_gpu_model(self,
gpus: int,
x_train: Union[Tuple[List[List[str]], ...],
List[List[str]]],
y_train: Union[List[List[str]], List[str]],
cpu_merge: bool = True,
cpu_relocation: bool = False,
x_validate: Union[Tuple[List[List[str]], ...],
List[List[str]]] = None,
y_validate: Union[List[List[str]],
List[str]] = None):
"""
Build multi-GPU model with corpus
Args:
gpus: Integer >= 2, number of on GPUs on which to create model replicas.
cpu_merge: A boolean value to identify whether to force merging model weights
under the scope of the CPU or not.
cpu_relocation: A boolean value to identify whether to create the model's weights
under the scope of the CPU. If the model is not defined under any preceding device
scope, you can still rescue it by activating this option.
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
Returns:
"""
if x_validate is not None and not isinstance(x_validate, tuple):
self.embedding.analyze_corpus(x_train + x_validate,
y_train + y_validate)
else:
self.embedding.analyze_corpus(x_train, y_train)
if self.tf_model is None:
with utils.custom_object_scope():
self.build_model_arc()
self.tf_model = tf.keras.utils.multi_gpu_model(
self.tf_model,
gpus,
cpu_merge=cpu_merge,
cpu_relocation=cpu_relocation)
self.compile_model()
def build_tpu_model(self,
strategy: tf.contrib.distribute.TPUStrategy,
x_train: Union[Tuple[List[List[str]], ...],
List[List[str]]],
y_train: Union[List[List[str]], List[str]],
x_validate: Union[Tuple[List[List[str]], ...],
List[List[str]]] = None,
y_validate: Union[List[List[str]], List[str]] = None):
"""
Build TPU model with corpus
Args:
strategy: `TPUDistributionStrategy`. The strategy to use for replicating model
across multiple TPU cores.
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
Returns:
"""
if x_validate is not None and not isinstance(x_validate, tuple):
self.embedding.analyze_corpus(x_train + x_validate,
y_train + y_validate)
else:
self.embedding.analyze_corpus(x_train, y_train)
if self.tf_model is None:
with utils.custom_object_scope():
self.build_model_arc()
self.tf_model = tf.contrib.tpu.keras_to_tpu_model(
self.tf_model, strategy=strategy)
self.compile_model(optimizer=tf.train.AdamOptimizer())
def get_data_generator(self,
x_data,
y_data,
batch_size: int = 64,
shuffle: bool = True):
"""
data generator for fit_generator
Args:
x_data: Array of feature data (if the model has a single input),
or tuple of feature data array (if the model has multiple inputs)
y_data: Array of label data
batch_size: Number of samples per gradient update, default to 64.
shuffle:
Returns:
data generator
"""
index_list = np.arange(len(x_data))
page_count = len(x_data) // batch_size + 1
while True:
if shuffle:
np.random.shuffle(index_list)
for page in range(page_count):
start_index = page * batch_size
end_index = start_index + batch_size
target_index = index_list[start_index:end_index]
if len(target_index) == 0:
target_index = index_list[0:batch_size]
x_tensor = self.embedding.process_x_dataset(
x_data, target_index)
y_tensor = self.embedding.process_y_dataset(
y_data, target_index)
yield (x_tensor, y_tensor)
def fit(self,
x_train: Union[Tuple[List[List[str]], ...], List[List[str]]],
y_train: Union[List[List[str]], List[str]],
x_validate: Union[Tuple[List[List[str]], ...],
List[List[str]]] = None,
y_validate: Union[List[List[str]], List[str]] = None,
batch_size: int = 64,
epochs: int = 5,
callbacks: List[keras.callbacks.Callback] = None,
fit_kwargs: Dict = None,
shuffle: bool = True):
"""
Trains the model for a given number of epochs with fit_generator (iterations on a dataset).
Args:
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
batch_size: Number of samples per gradient update, default to 64.
epochs: Integer. Number of epochs to train the model. default 5.
callbacks:
fit_kwargs: fit_kwargs: additional arguments passed to ``fit_generator()`` function from
``tensorflow.keras.Model``
- https://www.tensorflow.org/api_docs/python/tf/keras/models/Model#fit_generator
shuffle:
Returns:
"""
self.build_model(x_train, y_train, x_validate, y_validate)
train_generator = self.get_data_generator(x_train, y_train, batch_size,
shuffle)
if fit_kwargs is None:
fit_kwargs = {}
validation_generator = None
validation_steps = None
if x_validate:
validation_generator = self.get_data_generator(
x_validate, y_validate, batch_size, shuffle)
if isinstance(x_validate, tuple):
validation_steps = len(x_validate[0]) // batch_size + 1
else:
validation_steps = len(x_validate) // batch_size + 1
if isinstance(x_train, tuple):
steps_per_epoch = len(x_train[0]) // batch_size + 1
else:
steps_per_epoch = len(x_train) // batch_size + 1
with utils.custom_object_scope():
return self.tf_model.fit_generator(
train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
callbacks=callbacks,
**fit_kwargs)
def fit_without_generator(self,
x_train: Union[Tuple[List[List[str]], ...],
List[List[str]]],
y_train: Union[List[List[str]], List[str]],
x_validate: Union[Tuple[List[List[str]], ...],
List[List[str]]] = None,
y_validate: Union[List[List[str]],
List[str]] = None,
batch_size: int = 64,
epochs: int = 5,
callbacks: List[keras.callbacks.Callback] = None,
fit_kwargs: Dict = None):
"""
Trains the model for a given number of epochs (iterations on a dataset).
Args:
x_train: Array of train feature data (if the model has a single input),
or tuple of train feature data array (if the model has multiple inputs)
y_train: Array of train label data
x_validate: Array of validation feature data (if the model has a single input),
or tuple of validation feature data array (if the model has multiple inputs)
y_validate: Array of validation label data
batch_size: Number of samples per gradient update, default to 64.
epochs: Integer. Number of epochs to train the model. default 5.
callbacks:
fit_kwargs: fit_kwargs: additional arguments passed to ``fit_generator()`` function from
``tensorflow.keras.Model``
- https://www.tensorflow.org/api_docs/python/tf/keras/models/Model#fit_generator
Returns:
"""
self.build_model(x_train, y_train, x_validate, y_validate)
tensor_x = self.embedding.process_x_dataset(x_train)
tensor_y = self.embedding.process_y_dataset(y_train)
validation_data = None
if x_validate is not None:
tensor_valid_x = self.embedding.process_x_dataset(x_validate)
tensor_valid_y = self.embedding.process_y_dataset(y_validate)
validation_data = (tensor_valid_x, tensor_valid_y)
if fit_kwargs is None:
fit_kwargs = {}
if callbacks and 'callbacks' not in fit_kwargs:
fit_kwargs['callbacks'] = callbacks
with utils.custom_object_scope():
return self.tf_model.fit(tensor_x,
tensor_y,
validation_data=validation_data,
epochs=epochs,
batch_size=batch_size,
**fit_kwargs)
def compile_model(self, **kwargs):
"""Configures the model for training.
Using ``compile()`` function of ``tf.keras.Model`` -
https://www.tensorflow.org/api_docs/python/tf/keras/models/Model#compile
Args:
**kwargs: arguments passed to ``compile()`` function of ``tf.keras.Model``
Defaults:
- loss: ``categorical_crossentropy``
- optimizer: ``adam``
- metrics: ``['accuracy']``
"""
if kwargs.get('loss') is None:
kwargs['loss'] = 'categorical_crossentropy'
if kwargs.get('optimizer') is None:
kwargs['optimizer'] = 'adam'
if kwargs.get('metrics') is None:
kwargs['metrics'] = ['accuracy']
self.tf_model.compile(**kwargs)
if not kashgari.config.disable_auto_summary:
self.tf_model.summary()
def predict(self,
x_data,
batch_size=32,
debug_info=False,
predict_kwargs: Dict = None):
"""
Generates output predictions for the input samples.
Computation is done in batches.
Args:
x_data: The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs).
batch_size: Integer. If unspecified, it will default to 32.
debug_info: Bool, Should print out the logging info.
predict_kwargs: arguments passed to ``predict()`` function of ``tf.keras.Model``
Returns:
array(s) of predictions.
"""
if predict_kwargs is None:
predict_kwargs = {}
with utils.custom_object_scope():
if isinstance(x_data, tuple):
lengths = [len(sen) for sen in x_data[0]]
else:
lengths = [len(sen) for sen in x_data]
tensor = self.embedding.process_x_dataset(x_data)
pred = self.tf_model.predict(tensor,
batch_size=batch_size,
**predict_kwargs)
res = self.embedding.reverse_numerize_label_sequences(
pred.argmax(-1), lengths)
if debug_info:
logging.info('input: {}'.format(tensor))
logging.info('output: {}'.format(pred))
logging.info('output argmax: {}'.format(pred.argmax(-1)))
return res
def evaluate(self,
x_data,
y_data,
batch_size=None,
digits=4,
debug_info=False) -> Tuple[float, float, Dict]:
"""
Evaluate model
Args:
x_data:
y_data:
batch_size:
digits:
debug_info:
Returns:
"""
raise NotImplementedError
def build_model_arc(self):
raise NotImplementedError
def save(self, model_path: str):
"""
Save model
Args:
model_path:
Returns:
"""
pathlib.Path(model_path).mkdir(exist_ok=True, parents=True)
with open(os.path.join(model_path, 'model_info.json'), 'w') as f:
f.write(json.dumps(self.info(), indent=2, ensure_ascii=True))
f.close()
self.tf_model.save_weights(os.path.join(model_path,
'model_weights.h5'))
logging.info('model saved to {}'.format(os.path.abspath(model_path)))
# ou o GloVE-300 do http://nilc.icmc.usp.br/embeddings se não der certo
# 2 - Ver como fazer o Predict. Temos que processar a frase para ficar igual a deles.
# Eles usam um PunktSentenceTokenizer com um abbrev_list. Esses scripts estao na pasta leNer-dataset.
# 3 - Ver como integrar esse codigo com o webstruct atual
# 4 - Seria uma boa ideia ter uma interface tipo o Broka. Para que existesse a lista de arquivos, e que
# pudesse abrir para re-treinar, abrindo com o plugin de Ramon.
# Uma ideia seria ate converter o dataset deles atual para o formato do broka hoje em Html ( pode ser algo simples, como colocar cada paragrafo como um p)
# 5 - Fazer a persistencia ( O kashgari tem um metodo save/load)
# 2 - Aumentar epochs para treinar
# You can use WordEmbedding or BERTEmbedding for your text embedding
text_embedding = BareEmbedding(task=kashgari.LABELING)
text_embedding.analyze_corpus(tokens, labels)
# Now we can embed with this stacked embedding layer
# We can build any labeling model with this embedding
from kashgari.tasks.labeling import BiLSTM_CRF_Model
model = BiLSTM_CRF_Model(embedding=text_embedding)
model.fit(tokens, labels, batch_size=8, epochs=10)
print(model.predict(tokens))
# print(model.predict_entities(x))