-
-
Notifications
You must be signed in to change notification settings - Fork 439
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
13 changed files
with
397 additions
and
247 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,267 @@ | ||
| # encoding: utf-8 | ||
|
|
||
| # author: BrikerMan | ||
| # contact: eliyar917@gmail.com | ||
| # blog: https://eliyar.biz | ||
|
|
||
| # file: base_model.py | ||
| # time: 2019-05-22 11:21 | ||
|
|
||
|
|
||
| from typing import Dict, Any, List, Optional, Union, Tuple | ||
|
|
||
| import logging | ||
| import numpy as np | ||
| from tensorflow import keras | ||
| from kashgari import utils | ||
| from kashgari.embeddings import BareEmbedding | ||
| from kashgari.embeddings.base_embedding import Embedding | ||
|
|
||
| L = keras.layers | ||
|
|
||
|
|
||
| class BaseModel(object): | ||
| """Base Sequence Labeling Model""" | ||
| __architect_name__ = 'BLSTMModel' | ||
| __task__ = "labeling" | ||
|
|
||
| @classmethod | ||
| def get_default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]: | ||
| raise NotImplementedError | ||
|
|
||
| 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: Optional[keras.Model] = None | ||
| self.hyper_parameters = self.get_default_hyper_parameters() | ||
| self._label2idx = {} | ||
| self._idx2label = {} | ||
| self.model_info = {} | ||
| self.pre_processor = self.embedding.processor | ||
|
|
||
| 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: List[List[str]], | ||
| x_validate: Union[Tuple[List[List[str]], ...], List[List[str]]] = None, | ||
| y_validate: List[List[str]] = None, | ||
| **kwargs): | ||
| x_train = utils.wrap_as_tuple(x_train) | ||
| y_train = utils.wrap_as_tuple(y_train) | ||
| if x_validate: | ||
| x_validate = utils.wrap_as_tuple(x_validate) | ||
| y_validate = utils.wrap_as_tuple(y_validate) | ||
| if self.embedding.token_count == 0: | ||
| # if x_validate is not None: | ||
| # y_all = (y_train + y_validate) | ||
| # x_all = (x_train + x_validate) | ||
| # else: | ||
| # x_all = x_train.copy() | ||
| # y_all = y_train.copy() | ||
| self.embedding.analyze_corpus(x_train, y_train) | ||
|
|
||
| if self.tf_model is None: | ||
| self.build_model_arc() | ||
| self.compile_model() | ||
|
|
||
| def get_data_generator(self, | ||
| x_data, | ||
| y_data, | ||
| batch_size: int = 64): | ||
| """ | ||
| 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. | ||
| Returns: | ||
| data generator | ||
| """ | ||
|
|
||
| index_list = np.arange(len(x_data[0])) | ||
| page_count = len(x_data) // batch_size + 1 | ||
|
|
||
| x_data = utils.wrap_as_tuple(x_data) | ||
| y_data = utils.wrap_as_tuple(y_data) | ||
|
|
||
| while True: | ||
| 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: List[List[str]], | ||
| x_validate: Union[Tuple[List[List[str]], ...], List[List[str]]] = None, | ||
| y_validate: List[List[str]] = None, | ||
| batch_size: int = 64, | ||
| epochs: int = 5, | ||
| fit_kwargs: Dict = None, | ||
| **kwargs): | ||
| """ | ||
| 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. | ||
| 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 | ||
| **kwargs: | ||
| Returns: | ||
| """ | ||
| self.build_model(x_train, y_train, x_validate, y_validate, **kwargs) | ||
|
|
||
| train_generator = self.get_data_generator(x_train, | ||
| y_train, | ||
| batch_size) | ||
| if fit_kwargs is None: | ||
| fit_kwargs = {} | ||
|
|
||
| if x_validate: | ||
| validation_generator = self.get_data_generator(x_validate, | ||
| y_validate, | ||
| batch_size) | ||
|
|
||
| if isinstance(x_validate, tuple): | ||
| validation_steps = len(x_validate[0]) // batch_size + 1 | ||
| else: | ||
| validation_steps = len(x_validate) // batch_size + 1 | ||
|
|
||
| fit_kwargs['validation_data'] = validation_generator | ||
| fit_kwargs['validation_steps'] = validation_steps | ||
|
|
||
| if isinstance(x_validate, tuple): | ||
| steps_per_epoch = len(x_train[0]) // batch_size + 1 | ||
| else: | ||
| steps_per_epoch = len(x_train) // batch_size + 1 | ||
|
|
||
| self.tf_model.fit_generator(train_generator, | ||
| steps_per_epoch=steps_per_epoch, | ||
| epochs=epochs, | ||
| **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) | ||
| self.tf_model.summary() | ||
|
|
||
| def predict(self, | ||
| x_data, | ||
| batch_size=None, | ||
| debug_info=False): | ||
| """ | ||
| 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. | ||
| Returns: | ||
| array(s) of predictions. | ||
| """ | ||
| x_data = utils.wrap_as_tuple(x_data) | ||
| lengths = [len(sen) for sen in x_data[0]] | ||
| tensor = self.embedding.process_x_dataset(x_data) | ||
| pred = self.tf_model.predict(tensor, batch_size=batch_size) | ||
| 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]: | ||
| raise NotImplementedError | ||
|
|
||
| def build_model_arc(self): | ||
| raise NotImplementedError | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| from kashgari.tasks.labeling import CNNLSTMModel | ||
| from kashgari.corpus import ChineseDailyNerCorpus | ||
|
|
||
| train_x, train_y = ChineseDailyNerCorpus.load_data('valid') | ||
|
|
||
| model = CNNLSTMModel() | ||
| model.build_model(train_x[:100], train_y[:100]) | ||
| r = model.predict_entities(train_x[:5]) | ||
| import pprint | ||
|
|
||
| pprint.pprint(r) | ||
| # model.evaluate(train_x[:20], train_y[:20]) | ||
| print("Hello world") |
Oops, something went wrong.