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
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):
output_dim = len(self.pre_processor.label2idx)
config = self.hyper_parameters
embed_model = self.embedding.embed_model
# build model structure in sequent way
layers_seq = []
layers_seq.append(L.Conv1D(**config['conv1d_layer']))
layers_seq.append(L.GlobalMaxPooling1D(**config['max_pool_layer']))
layers_seq.append(L.Dense(**config['dense_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):
output_dim = len(self.pre_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 = 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 = 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
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)