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