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): """ 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_LSTMDecoder = LSTMDecoder(**config['layer_LSTMDecoder'], name='layer_LSTMDecoder') layer_dense = L.Dense(**config['layer_dense'], name='layer_dense') layer_decoder_dense = L.Dense(output_dim, name='layer_decoder_dense') softmax_layer = L.Activation(tf.nn.softmax, name="softmax_layer") tensor = layer_blstm(embed_model.output) tensor = layer_LSTMDecoder(tensor) tensor = layer_dense(tensor) tensor = layer_decoder_dense(tensor) output_tensor = softmax_layer(tensor) self.layer_LSTMDecoder = layer_LSTMDecoder self.tf_model = keras.Model(embed_model.inputs, output_tensor)
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_bert = bert_attention(name='layer_bert') layer_position = Position_attention_layer(name='layer_position') layer_blstm = L.Bidirectional(L.CuDNNLSTM(**config['layer_blstm']), name='layer_blstm') layer_LSTMDecoder = LSTMDecoder(**config['layer_LSTMDecoder'], name='layer_LSTMDecoder') layer_attention = Attention(name='layer_attention') layer_Activation = L.Activation("tanh", name="layer_Activation") layer_dense1 = L.Dense(**config['layer_dense1'], name='layer_dense1') layer_dense2 = L.Dense(**config['layer_dense2'], name='layer_dense2') layer_crf_dense = L.Dense(output_dim, name='layer_crf_dense') layer_crf = CRF(output_dim, name='layer_crf') #全局定制类 tensor = layer_bert(embed_model.output) tensor = layer_position(tensor) tensor = layer_blstm(tensor) tensor = layer_LSTMDecoder(tensor) tensor = layer_attention(tensor) tensor = layer_Activation(tensor) tensor = layer_dense1(tensor) tensor = layer_dense2(tensor) tensor = layer_crf_dense(tensor) output_tensor = layer_crf(tensor) self.layer_crf = layer_crf self.tf_model = keras.Model(embed_model.inputs, output_tensor)
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_conv = L.Conv1D(**config['layer_conv'], name='layer_conv') layer_blstm = L.Bidirectional(L.CuDNNLSTM(**config['layer_blstm']), name='layer_blstm') layer_dense = L.Dense(**config['layer_dense'], name='layer_dense') layer_attention = Attention(name='layer_attention') layer_Activation = L.Activation("tanh", name="layer_Activation") layer_crf_dense = L.Dense(output_dim, name='layer_crf_dense') layer_crf = CRF(output_dim, name='layer_crf') #全局定制类 tensor = layer_conv(embed_model.output) tensor = layer_blstm(tensor) tensor = layer_dense(tensor) tensor = layer_attention(tensor) tensor = layer_Activation(tensor) tensor = layer_crf_dense(tensor) output_tensor = layer_crf(tensor) self.layer_crf = layer_crf 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)