def _build_block(self, hp, output_node, block_type):
max_tokens = self.max_tokens or hp.Choice(
MAX_TOKENS, [500, 5000, 20000], default=5000)
if block_type == NGRAM:
output_node = preprocessing.TextToNgramVector(
max_tokens=max_tokens).build(hp, output_node)
return basic.DenseBlock().build(hp, output_node)
if block_type == BERT:
output_node = basic.BertBlock().build(hp, output_node)
else:
output_node = preprocessing.TextToIntSequence(
max_tokens=max_tokens).build(hp, output_node)
if block_type == TRANSFORMER:
output_node = basic.Transformer(
max_features=max_tokens + 1,
pretraining=self.pretraining,
).build(hp, output_node)
else:
output_node = basic.Embedding(
max_features=max_tokens + 1,
pretraining=self.pretraining,
).build(hp, output_node)
output_node = basic.ConvBlock().build(hp, output_node)
output_node = reduction.SpatialReduction().build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
block_type = self.block_type or hp.Choice(
'block_type', ['vanilla', 'transformer', 'ngram'],
default='vanilla')
max_tokens = self.max_tokens or hp.Choice(
'max_tokens', [500, 5000, 20000], default=5000)
if block_type == 'ngram':
output_node = preprocessing.TextToNgramVector(
max_tokens=max_tokens).build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
else:
output_node = preprocessing.TextToIntSequence(
max_tokens=max_tokens).build(hp, output_node)
if block_type == 'transformer':
output_node = basic.Transformer(
max_features=max_tokens + 1,
pretraining=self.pretraining).build(hp, output_node)
else:
output_node = basic.Embedding(
max_features=max_tokens + 1,
pretraining=self.pretraining).build(hp, output_node)
output_node = basic.ConvBlock().build(hp, output_node)
output_node = reduction.SpatialReduction().build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node
def test_dense_block():
utils.block_basic_exam(
basic.DenseBlock(),
tf.keras.Input(shape=(32,), dtype=tf.float32),
[
'num_layers',
'use_batchnorm',
])
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
if self.categorical_encoding:
block = preprocessing.CategoricalToNumerical()
block.column_types = self.column_types
block.column_names = self.column_names
output_node = block.build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
if self.categorical_encoding:
block = preprocessing.CategoricalToNumerical()
block.column_types = self.column_types
block.column_names = self.column_names
output_node = block.build(hp, output_node)
if self.normalize is None and hp.Boolean(NORMALIZE):
with hp.conditional_scope(NORMALIZE, [True]):
output_node = preprocessing.Normalization().build(hp, output_node)
elif self.normalize:
output_node = preprocessing.Normalization().build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node
def build_body(self, hp, input_node):
output_node = basic.DenseBlock().build(hp, input_node)
return output_node