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_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_crf_dense1 = L.Dense(output_dim, name='layer_crf_dense')
layer_crf1 = CRF(output_dim, name='layer_crf1') #全局定制类
layer_crf_dense2 = L.Dense(output_dim, name='layer_crf_dense2')
layer_crf2 = CRF(output_dim, name='layer_crf2')
tensor = layer_conv(embed_model.output)
tensor1 = layer_crf_dense1(tensor)
output_tensor1 = layer_crf1(tensor1)
tensor = layer_blstm(tensor)
tensor = layer_dense(tensor)
tensor2 = layer_crf_dense2(tensor)
output_tensor2 = layer_crf2(tensor2)
self.layer_crf1 = layer_crf1
self.layer_crf2 = layer_crf2
self.tf_model = keras.Model(inputs=embed_model.inputs,
outputs=[output_tensor2, output_tensor1])
def build_model_arc(self):
"""
build model architectural
"""
output_dim = len(self.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_dense = L.Dense(**config['layer_dense'], name='layer_dense')
layer_crf_dense = L.Dense(output_dim, name='layer_crf_dense')
layer_crf = CRF(output_dim, name='layer_crf')
layer_dropout = L.Dropout(**config['layer_dropout'],
name='layer_dropout')
tensor = layer_blstm(embed_model.output)
tensor = layer_dense(tensor)
tensor = layer_dropout(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):
"""
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_dense = L.Dense(**config['layer_dense'], name='layer_dense')
layer_crf_dense = L.Dense(output_dim, name='layer_crf_dense')
layer_crf = CRF(output_dim, name='layer_crf')
if isinstance(embed_model, keras.Model):
first_layer_output = embed_model.output
first_layer_input = embed_model.inputs
else:
first_layer_output = embed_model
first_layer_input = embed_model
tensor = layer_blstm(first_layer_output)
tensor = layer_dense(tensor)
tensor = layer_crf_dense(tensor)
output_tensor = layer_crf(tensor)
self.layer_crf = layer_crf
self.tf_model = keras.Model(first_layer_input, 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_conv2 = L.Conv1D(**config['layer_conv2'],
name='layer_conv2',
kernel_regularizer=regularizers.l2(0.01))
layer_conv3 = L.Conv1D(**config['layer_conv3'],
name='layer_conv3',
kernel_regularizer=regularizers.l2(0.02))
layer_conv4 = L.Conv1D(**config['layer_conv4'],
name='layer_conv4',
kernel_regularizer=regularizers.l2(0.03))
layer_conv5 = L.Conv1D(**config['layer_conv5'],
name='layer_conv5',
kernel_regularizer=regularizers.l2(0.04))
layer_conv6 = L.Conv1D(**config['layer_conv6'],
name='layer_conv6',
kernel_regularizer=regularizers.l2(0.05))
layer_conv7 = L.Conv1D(**config['layer_conv7'],
name='layer_conv7',
kernel_regularizer=regularizers.l2(0.06))
layer_conv8 = L.Conv1D(**config['layer_conv8'],
name='layer_conv8',
kernel_regularizer=regularizers.l2(0.07))
layer_conv9 = L.Conv1D(**config['layer_conv9'],
name='layer_conv9',
kernel_regularizer=regularizers.l2(0.08))
layer_conv10 = L.Conv1D(**config['layer_conv10'],
name='layer_conv10',
kernel_regularizer=regularizers.l2(0.09))
layer_blstm = L.Bidirectional(L.CuDNNLSTM(**config['layer_blstm']),
name='layer_blstm')
layer_dense = L.Dense(**config['layer_dense'], name='layer_dense')
layer_crf_dense = L.Dense(output_dim, name='layer_crf_dense')
layer_crf = CRF(output_dim, name='layer_crf') #全局定制类
tensor2 = layer_conv2(embed_model.output)
tensor3 = layer_conv3(embed_model.output)
tensor4 = layer_conv4(embed_model.output)
tensor5 = layer_conv5(embed_model.output)
tensor6 = layer_conv6(embed_model.output)
tensor7 = layer_conv7(embed_model.output)
tensor8 = layer_conv8(embed_model.output)
tensor9 = layer_conv9(embed_model.output)
tensor10 = layer_conv10(embed_model.output)
tensor = keras.layers.concatenate([
tensor2, tensor3, tensor4, tensor5, tensor6, tensor7, tensor8,
tensor9, tensor10
], 2)
tensor = layer_blstm(tensor)
tensor = layer_dense(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)