def build_model_arc(self) -> None:
config = self.hyper_parameters
output_dim = self.label_processor.vocab_size
embed_model = self.embedding.embed_model
# 定义模型架构
self.tf_model = keras.Sequential([
embed_model,
L.Bidirectional(L.LSTM(**config['layer_lstm1'])),
L.Bidirectional(L.LSTM(**config['layer_lstm2'])),
L.Dropout(**config['layer_dropout']),
L.Dense(output_dim, **config['layer_output']),
self._activation_layer()
])
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_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_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):
output_dim = self.processor.output_dim
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_bi_lstm = L.Bidirectional(L.LSTM(**config['layer_bi_lstm']))
layer_dense = L.Dense(output_dim, **config['layer_dense'])
tensor = layer_bi_lstm(embed_model.output)
output_tensor = layer_dense(tensor)
self.tf_model = keras.Model(embed_model.inputs, output_tensor)
def build_model_arc(self):
output_dim = len(self.processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
# Define your layers
layer_blstm1 = L.Bidirectional(L.LSTM(**config['layer_blstm1']),
name='layer_blstm1')
layer_blstm2 = L.Bidirectional(L.LSTM(**config['layer_blstm2']),
name='layer_blstm2')
layer_blstm3 = L.Bidirectional(L.LSTM(**config['layer_blstm3']),
name='layer_blstm3')
layer_dropout1 = L.Dropout(**config['layer_dropout1'],
name='layer_dropout1')
layer_dropout2 = L.Dropout(**config['layer_dropout2'],
name='layer_dropout2')
layer_dropout3 = L.Dropout(**config['layer_dropout3'],
name='layer_dropout3')
#layer_flatten = L.Flatten(**config['layer_flatten'])
#layer_activation = L.Activation(**config['layer_activation'])
layer_dense = L.Dense(output_dim, **config['layer_dense'])
# Define tensor flow
tensor = layer_dropout1(embed_model.output)
tensor = layer_blstm1(tensor)
tensor = layer_dropout2(tensor)
tensor = layer_blstm2(tensor)
#tensor = layer_dropout3(tensor)
#tensor = layer_blstm3(tensor)
output_tensor = layer_dense(tensor)
# Init model
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
crf = KConditionalRandomField()
layer_stack = [
L.Bidirectional(L.LSTM(**config['layer_blstm1']),
name='layer_blstm1'),
L.Bidirectional(L.LSTM(**config['layer_blstm2']),
name='layer_blstm2'),
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
def build_model_arc(self):
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layers_seq = []
layers_seq.append(L.Conv1D(**config['conv_layer']))
layers_seq.append(L.MaxPooling1D(**config['max_pool_layer']))
layers_seq.append(L.LSTM(**config['lstm_layer']))
layers_seq.append(L.Dense(output_dim, **config['activation_layer']))
tensor = embed_model.output
for layer in layers_seq:
tensor = layer(tensor)
self.tf_model = tf.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
# build model structure in sequent way
layer_stack = [
L.Bidirectional(L.LSTM(**config['layer_bi_lstm'])),
L.Dense(output_dim, **config['layer_output']),
self._activation_layer()
]
tensor = embed_model.output
for layer in layer_stack:
tensor = layer(tensor)
self.tf_model: keras.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
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 build_model_arc(self):
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_bi_lstm = L.Bidirectional(L.LSTM(**config['layer_bi_lstm']),
name='layer_bi_lstm')
layer_output_1 = L.Dense(3,
activation='sigmoid',
name='layer_output_1')
layer_output_2 = L.Dense(3,
activation='sigmoid',
name='layer_output_2')
tensor = layer_bi_lstm(embed_model.output)
output_tensor_1 = layer_output_1(tensor)
output_tensor_2 = layer_output_2(tensor)
self.tf_model = tf.keras.Model(embed_model.inputs,
[output_tensor_1, output_tensor_2])
