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.GRU(**config['layer_bi_gru'])),
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)
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)
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.GRU(**config['layer_bgru']), name='layer_bgru'),
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