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 = []
layers_rcnn_seq.append(L.SpatialDropout1D(**config['spatial_dropout']))
layers_rcnn_seq.append(L.Bidirectional(L.GRU(**config['rnn_0'])))
layers_rcnn_seq.append(L.Conv1D(**config['conv_0']))
layers_sensor = []
layers_sensor.append(L.GlobalMaxPooling1D())
layers_sensor.append(AttentionWeightedAverageLayer())
layers_sensor.append(L.GlobalAveragePooling1D())
layer_concat = L.Concatenate(**config['concat'])
layers_full_connect = []
layers_full_connect.append(L.Dropout(**config['dropout']))
layers_full_connect.append(L.Dense(**config['dense']))
layers_full_connect.append(
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)
# tensor_output = L.concatenate(tensor_sensors, **config['concat'])
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_embed_dropout = L.SpatialDropout1D(**config['spatial_dropout'])
layers_conv = [L.Conv1D(**config[f'conv_{i}']) for i in range(4)]
layers_sensor = [KMaxPoolingLayer(**config['maxpool_i4']), L.Flatten()]
layer_concat = L.Concatenate(**config['merged_tensor'])
layers_seq = []
layers_seq.append(L.Dropout(**config['dropout']))
layers_seq.append(L.Dense(**config['dense']))
layers_seq.append(L.Dense(output_dim, **config['activation_layer']))
embed_tensor = layer_embed_dropout(embed_model.output)
tensors_conv = [layer_conv(embed_tensor) for layer_conv in layers_conv]
tensors_sensor = []
for tensor_conv in tensors_conv:
tensor_sensor = tensor_conv
for layer_sensor in layers_sensor:
tensor_sensor = layer_sensor(tensor_sensor)
tensors_sensor.append(tensor_sensor)
tensor = layer_concat(tensors_sensor)
# tensor = L.concatenate(tensors_sensor, **config['merged_tensor'])
for layer in layers_seq:
tensor = layer(tensor)
self.tf_model = tf.keras.Model(embed_model.inputs, tensor)
def build_model_arc(self):
output_dim = len(self.processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layers_region = [
L.Conv1D(**config['region_embedding']),
L.BatchNormalization(),
L.PReLU(),
L.Dropout(**config['region_dropout'])
]
layers_main = [
L.GlobalMaxPooling1D(),
L.Dense(**config['dense']),
L.BatchNormalization(),
L.PReLU(),
L.Dropout(**config['dropout']),
L.Dense(output_dim, **config['activation'])
]
tensor_out = embed_model.output
# build region tensors
for layer in layers_region:
tensor_out = layer(tensor_out)
# build the base pyramid layer
tensor_out = self.conv_block(tensor_out, **config['conv_block'])
# build the above pyramid layers while `steps > 2`
seq_len = tensor_out.shape[1].value
if seq_len is None:
raise ValueError(
'`sequence_length` should be explicitly assigned, but it is `None`.'
)
for i in range(floor(log2(seq_len)) - 2):
tensor_out = self.resnet_block(tensor_out,
stage=i + 1,
**config['resnet_block'])
for layer in layers_main:
tensor_out = layer(tensor_out)
self.tf_model = tf.keras.Model(embed_model.inputs, tensor_out)
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']))
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_rnn = first_layer_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(first_layer_input, tensor_output)
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:
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) -> 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)
def build_model_arc(self):
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_embed_dropout = L.SpatialDropout1D(**config['spatial_dropout'])
layers_conv = [L.Conv1D(**config[f'conv_{i}']) for i in range(4)]
layers_sensor = []
layers_sensor.append(L.GlobalMaxPooling1D())
layers_sensor.append(AttentionWeightedAverageLayer())
layers_sensor.append(L.GlobalAveragePooling1D())
layer_view = L.Concatenate(**config['v_col3'])
layer_allviews = L.Concatenate(**config['merged_tensor'])
layers_seq = []
layers_seq.append(L.Dropout(**config['dropout']))
layers_seq.append(L.Dense(**config['dense']))
layers_seq.append(L.Dense(output_dim, **config['activation_layer']))
embed_tensor = layer_embed_dropout(embed_model.output)
tensors_conv = [layer_conv(embed_tensor) for layer_conv in layers_conv]
tensors_matrix_sensor = []
for tensor_conv in tensors_conv:
tensor_sensors = []
tensor_sensors = [
layer_sensor(tensor_conv) for layer_sensor in layers_sensor
]
# tensor_sensors.append(L.GlobalMaxPooling1D()(tensor_conv))
# tensor_sensors.append(AttentionWeightedAverageLayer()(tensor_conv))
# tensor_sensors.append(L.GlobalAveragePooling1D()(tensor_conv))
tensors_matrix_sensor.append(tensor_sensors)
tensors_views = [
layer_view(list(tensors))
for tensors in zip(*tensors_matrix_sensor)
]
tensor = layer_allviews(tensors_views)
# tensors_v_cols = [L.concatenate(tensors, **config['v_col3']) for tensors
# in zip(*tensors_matrix_sensor)]
# tensor = L.concatenate(tensors_v_cols, **config['merged_tensor'])
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
crf = KConditionalRandomField()
layer_stack = [
L.Bidirectional(L.GRU(**config['layer_bgru']), name='layer_bgru'),
L.Dropout(**config['layer_dropout'], name='layer_dropout'),
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):
"""
build model architectural
"""
output_dim = len(self.processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
layer_conv = L.Conv1D(**config['layer_conv'], name='layer_conv')
layer_lstm = L.LSTM(**config['layer_lstm'], name='layer_lstm')
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_conv(embed_model.output)
tensor = layer_lstm(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)
