# encoding: utf-8
# author: BrikerMan
# contact: eliyar917@gmail.com
# blog: https://eliyar.biz
# file: bi_lstm_model.py
# time: 4:36 下午
from typing import Dict, Any
from tensorflow import keras
from kashgari.layers import L
from kashgari.tasks.labeling.abc_model import ABCLabelingModel
[docs]class BiLSTM_Model(ABCLabelingModel):
[docs] @classmethod
def default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]:
return {
'layer_blstm': {
'units': 128,
'return_sequences': True
},
'layer_dropout': {
'rate': 0.4
},
'layer_time_distributed': {},
'layer_activation': {
'activation': 'softmax'
}
}
[docs] def build_model_arc(self) -> None:
output_dim = self.label_processor.vocab_size
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_stack = [
L.Bidirectional(L.LSTM(**config['layer_blstm']), name='layer_blstm'),
L.Dropout(**config['layer_dropout'], name='layer_dropout'),
L.Dense(output_dim, **config['layer_time_distributed']),
L.Activation(**config['layer_activation'])
]
tensor = embed_model.output
for layer in layer_stack:
tensor = layer(tensor)
self.tf_model = keras.Model(embed_model.inputs, tensor)
if __name__ == "__main__":
from kashgari.corpus import ChineseDailyNerCorpus
x, y = ChineseDailyNerCorpus.load_data()
x_valid, y_valid = ChineseDailyNerCorpus.load_data('valid')
model = BiLSTM_Model()
model.fit(x, y, x_valid, y_valid, epochs=2)
model.evaluate(*ChineseDailyNerCorpus.load_data('test'))