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 _activation_layer(self) -> L.Layer: if self.multi_label: return L.Activation('sigmoid') else: return L.Activation('softmax')