def downsample(self, inputs, pool_type: str = 'max',
sorted: bool = True, stage: int = 1): # noqa: A002
layers_pool = []
if pool_type == 'max':
layers_pool.append(
L.MaxPooling1D(pool_size=3,
strides=2,
padding='same',
name=f'pool_{stage}'))
elif pool_type == 'k_max':
k = int(inputs.shape[1].value / 2)
layers_pool.append(
KMaxPoolingLayer(k=k,
sorted=sorted,
name=f'pool_{stage}'))
elif pool_type == 'conv':
layers_pool.append(
L.Conv1D(filters=inputs.shape[-1].value,
kernel_size=3,
strides=2,
padding='same',
name=f'pool_{stage}'))
layers_pool.append(
L.BatchNormalization())
elif pool_type is None:
layers_pool = []
else:
raise ValueError(f'unsupported pooling type `{pool_type}`!')
tensor_out = inputs
for layer in layers_pool:
tensor_out = layer(tensor_out)
return 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_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) -> 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.Conv1D(**config['conv1d_layer']),
L.MaxPooling1D(**config['max_pool_layer']),
L.LSTM(**config['lstm_layer']),
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(embed_model.inputs, tensor)
