Source code for kashgari.tasks.labeling.bi_lstm_crf_model

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author  : BrikerMan
# Site    : https://eliyar.biz

# Time    : 2020/9/1 11:51 下午
# File    : bi_lstm_crf_model.py
# Project : Kashgari

from typing import Dict, Any

from tensorflow import keras
from kashgari.layers import L, KConditionalRandomField
from kashgari.tasks.labeling.abc_model import ABCLabelingModel


[docs]class BiLSTM_CRF_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 crf = KConditionalRandomField() 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']), crf ] tensor = embed_model.output for layer in layer_stack: tensor = layer(tensor) self.tf_model = keras.Model(embed_model.inputs, tensor) self.crf_layer = crf
[docs] def compile_model(self, loss: Any = None, optimizer: Any = None, metrics: Any = None, **kwargs: Any) -> None: if loss is None: loss = self.crf_layer.loss if metrics is None: metrics = [self.crf_layer.accuracy] super(BiLSTM_CRF_Model, self).compile_model(loss=loss, optimizer=optimizer, metrics=metrics, **kwargs)