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)
