def build_model_arc(self):
"""
build model architectural
"""
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_blstm = L.Bidirectional(L.LSTM(**config['layer_blstm']),
name='layer_blstm')
layer_self_attention = SeqSelfAttention(**
config['layer_self_attention'],
name='layer_self_attention')
layer_dropout = L.Dropout(**config['layer_dropout'],
name='layer_dropout')
layer_time_distributed = L.TimeDistributed(
L.Dense(output_dim, **config['layer_time_distributed']),
name='layer_time_distributed')
layer_activation = L.Activation(**config['layer_activation'])
tensor = layer_blstm(embed_model.output)
tensor = layer_self_attention(tensor)
tensor = layer_dropout(tensor)
tensor = layer_time_distributed(tensor)
output_tensor = layer_activation(tensor)
self.tf_model = keras.Model(embed_model.inputs, output_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.GRU(**config['layer_bgru']), name='layer_bgru'),
L.Dropout(**config['layer_dropout'], name='layer_dropout'),
L.TimeDistributed(L.Dense(output_dim, **config['layer_time_distributed']), name='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.LSTM(**config['layer_blstm']),
name='layer_blstm'),
L.Dropout(**config['layer_dropout'], name='layer_dropout'),
L.TimeDistributed(
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
