# encoding: utf-8
# author: BrikerMan
# contact: eliyar917@gmail.com
# blog: https://eliyar.biz
# file: cnn_model.py
# time: 3:31 下午
from typing import Dict, Any
from tensorflow import keras
from kashgari.layers import L
from kashgari.tasks.classification.abc_model import ABCClassificationModel
[docs]class CNN_Model(ABCClassificationModel):
[docs] @classmethod
def default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]:
return {
'conv1d_layer': {
'filters': 128,
'kernel_size': 5,
'activation': 'relu'
},
'max_pool_layer': {},
'dense_layer': {
'units': 64,
'activation': 'relu'
},
'layer_output': {
},
}
[docs] def build_model_arc(self) -> None:
output_dim = self.label_processor.vocab_size
config = self.hyper_parameters
embed_model = self.embedding.embed_model
# build model structure in sequent way
layer_stack = [
L.Conv1D(**config['conv1d_layer']),
L.GlobalMaxPooling1D(**config['max_pool_layer']),
L.Dense(**config['dense_layer']),
L.Dense(output_dim, **config['layer_output']),
self._activation_layer()
]
tensor = embed_model.output
for layer in layer_stack:
tensor = layer(tensor)
self.tf_model = keras.Model(embed_model.inputs, tensor)
if __name__ == "__main__":
pass