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):
if self.num_classes:
expected = self.num_classes if self.num_classes > 2 else 1
if self.output_shape[-1] != expected:
raise ValueError('The data doesn\'t match the expected shape. '
'Expecting {} but got {}'.format(
expected, self.output_shape[-1]))
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
# Reduce the tensor to a vector.
if len(output_node.shape) > 2:
output_node = reduction.SpatialReduction().build(hp, output_node)
if self.dropout_rate is not None:
dropout_rate = self.dropout_rate
else:
dropout_rate = hp.Choice('dropout_rate', [0.0, 0.25, 0.5],
default=0)
if dropout_rate > 0:
output_node = layers.Dropout(dropout_rate)(output_node)
output_node = layers.Dense(self.output_shape[-1])(output_node)
if self.loss == 'binary_crossentropy':
output_node = layers.Activation(activations.sigmoid,
name=self.name)(output_node)
else:
output_node = layers.Softmax(name=self.name)(output_node)
return output_node
def build(self, hp, inputs=None):
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
# Reduce the tensor to a vector.
if len(output_node.shape) > 2:
output_node = reduction.SpatialReduction().build(hp, output_node)
if self.dropout is not None:
dropout = self.dropout
else:
dropout = hp.Choice("dropout", [0.0, 0.25, 0.5], default=0)
if dropout > 0:
output_node = layers.Dropout(dropout)(output_node)
output_node = layers.Dense(self.shape[-1])(output_node)
if isinstance(self.loss, keras.losses.BinaryCrossentropy):
output_node = layers.Activation(activations.sigmoid, name=self.name)(
output_node
)
else:
output_node = layers.Softmax(name=self.name)(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_spatial_reduction():
utils.block_basic_exam(
reduction.SpatialReduction(),
tf.keras.Input(shape=(32, 32, 3), dtype=tf.float32),
['reduction_type'],
)
