def build_model_arc(self):
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layers_rnn = []
layers_rnn.append(L.SpatialDropout1D(**config['spatial_dropout']))
layers_rnn.append(L.Bidirectional(L.GRU(**config['rnn_0'])))
layers_rnn.append(L.SpatialDropout1D(**config['rnn_dropout']))
layers_rnn.append(L.Bidirectional(L.GRU(**config['rnn_1'])))
layers_sensor = []
layers_sensor.append(L.Lambda(lambda t: t[:, -1], name='last'))
layers_sensor.append(L.GlobalMaxPooling1D())
layers_sensor.append(AttentionWeightedAverageLayer())
layers_sensor.append(L.GlobalAveragePooling1D())
layer_allviews = L.Concatenate(**config['all_views'])
layers_full_connect = []
layers_full_connect.append(L.Dropout(**config['dropout_0']))
layers_full_connect.append(L.Dense(**config['dense']))
layers_full_connect.append(L.Dropout(**config['dropout_1']))
layers_full_connect.append(
L.Dense(output_dim, **config['activation_layer']))
tensor_rnn = embed_model.output
for layer in layers_rnn:
tensor_rnn = layer(tensor_rnn)
tensor_sensors = [layer(tensor_rnn) for layer in layers_sensor]
tensor_output = layer_allviews(tensor_sensors)
for layer in layers_full_connect:
tensor_output = layer(tensor_output)
self.tf_model = tf.keras.Model(embed_model.inputs, tensor_output)
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.GRU(**config['layer_bgru']),
name='layer_bgru')
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_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):
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layers_rcnn_seq = [
L.SpatialDropout1D(**config['spatial_dropout']),
L.Bidirectional(L.GRU(**config['rnn_0'])),
L.Conv1D(**config['conv_0'])
]
layers_sensor = [
L.GlobalMaxPooling1D(),
AttentionWeightedAverageLayer(),
L.GlobalAveragePooling1D()
]
layer_concat = L.Concatenate(**config['concat'])
layers_full_connect = [
L.Dropout(**config['dropout']),
L.Dense(**config['dense']),
L.Dense(output_dim, **config['activation_layer'])
]
tensor = embed_model.output
for layer in layers_rcnn_seq:
tensor = layer(tensor)
tensors_sensor = [layer(tensor) for layer in layers_sensor]
tensor_output = layer_concat(tensors_sensor)
for layer in layers_full_connect:
tensor_output = layer(tensor_output)
self.tf_model = tf.keras.Model(embed_model.inputs, tensor_output)
def build_model_arc(self):
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_bi_gru = L.Bidirectional(L.GRU(**config['layer_bi_gru']))
layer_dense = L.Dense(output_dim, **config['layer_dense'])
tensor = layer_bi_gru(embed_model.output)
output_tensor = layer_dense(tensor)
self.tf_model = tf.keras.Model(embed_model.inputs, output_tensor)
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.GRU(**config['gru_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):
"""
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.GRU(**config['layer_bgru']),
name='layer_bgru')
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')
tensor = layer_blstm(embed_model.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(embed_model.inputs, output_tensor)