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# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
from tensorflow import nest
from autokeras.blocks import basic
from autokeras.blocks import preprocessing
from autokeras.blocks import reduction
from autokeras.engine import block as block_module
BLOCK_TYPE = "block_type"
RESNET = "resnet"
XCEPTION = "xception"
VANILLA = "vanilla"
EFFICIENT = "efficient"
NORMALIZE = "normalize"
AUGMENT = "augment"
TRANSFORMER = "transformer"
MAX_TOKENS = "max_tokens"
NGRAM = "ngram"
BERT = "bert"
class ImageBlock(block_module.Block):
"""Block for image data.
The image blocks is a block choosing from ResNetBlock, XceptionBlock, ConvBlock,
which is controlled by a hyperparameter, 'block_type'.
# Arguments
block_type: String. 'resnet', 'xception', 'vanilla'. The type of Block
to use. If unspecified, it will be tuned automatically.
normalize: Boolean. Whether to channel-wise normalize the images.
If unspecified, it will be tuned automatically.
augment: Boolean. Whether to do image augmentation. If unspecified,
it will be tuned automatically.
"""
def __init__(
self,
block_type: Optional[str] = None,
normalize: Optional[bool] = None,
augment: Optional[bool] = None,
**kwargs
):
super().__init__(**kwargs)
self.block_type = block_type
self.normalize = normalize
self.augment = augment
def get_config(self):
config = super().get_config()
config.update(
{
BLOCK_TYPE: self.block_type,
NORMALIZE: self.normalize,
AUGMENT: self.augment,
}
)
return config
def _build_block(self, hp, output_node, block_type):
if block_type == RESNET:
return basic.ResNetBlock().build(hp, output_node)
elif block_type == XCEPTION:
return basic.XceptionBlock().build(hp, output_node)
elif block_type == VANILLA:
return basic.ConvBlock().build(hp, output_node)
elif block_type == EFFICIENT:
return basic.EfficientNetBlock().build(hp, output_node)
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_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)
if self.augment is None and hp.Boolean(AUGMENT):
with hp.conditional_scope(AUGMENT, [True]):
output_node = preprocessing.ImageAugmentation().build(
hp, output_node
)
elif self.augment:
output_node = preprocessing.ImageAugmentation().build(hp, output_node)
if self.block_type is None:
block_type = hp.Choice(
BLOCK_TYPE, [RESNET, XCEPTION, VANILLA, EFFICIENT]
)
with hp.conditional_scope(BLOCK_TYPE, [block_type]):
output_node = self._build_block(hp, output_node, block_type)
else:
output_node = self._build_block(hp, output_node, self.block_type)
return output_node
class TextBlock(block_module.Block):
"""Block for text data.
# Arguments
block_type: String. 'vanilla', 'transformer', and 'ngram'. The type of Block
to use. 'vanilla' and 'transformer' use a TextToIntSequence vectorizer,
whereas 'ngram' uses TextToNgramVector. If unspecified, it will be tuned
automatically.
max_tokens: Int. The maximum size of the vocabulary.
If left unspecified, it will be tuned automatically.
pretraining: String. 'random' (use random weights instead any pretrained
model), 'glove', 'fasttext' or 'word2vec'. Use pretrained word embedding.
If left unspecified, it will be tuned automatically.
"""
def __init__(
self,
block_type: Optional[str] = None,
max_tokens: Optional[int] = None,
pretraining: Optional[str] = None,
**kwargs
):
super().__init__(**kwargs)
self.block_type = block_type
self.max_tokens = max_tokens
self.pretraining = pretraining
def get_config(self):
config = super().get_config()
config.update(
{
BLOCK_TYPE: self.block_type,
MAX_TOKENS: self.max_tokens,
"pretraining": self.pretraining,
}
)
return config
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
if self.block_type is None:
block_type = hp.Choice(BLOCK_TYPE, [VANILLA, TRANSFORMER, NGRAM, BERT])
with hp.conditional_scope(BLOCK_TYPE, [block_type]):
output_node = self._build_block(hp, output_node, block_type)
else:
output_node = self._build_block(hp, output_node, self.block_type)
return output_node
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
class StructuredDataBlock(block_module.Block):
"""Block for structured data.
# Arguments
categorical_encoding: Boolean. Whether to use the CategoricalToNumerical to
encode the categorical features to numerical features. Defaults to True.
normalize: Boolean. Whether to normalize the features.
If unspecified, it will be tuned automatically.
seed: Int. Random seed.
"""
def __init__(
self,
categorical_encoding: bool = True,
normalize: Optional[bool] = None,
seed: Optional[int] = None,
**kwargs
):
super().__init__(**kwargs)
self.categorical_encoding = categorical_encoding
self.normalize = normalize
self.seed = seed
self.column_types = None
self.column_names = None
@classmethod
def from_config(cls, config):
column_types = config.pop("column_types")
column_names = config.pop("column_names")
instance = cls(**config)
instance.column_types = column_types
instance.column_names = column_names
return instance
def get_config(self):
config = super().get_config()
config.update(
{
"categorical_encoding": self.categorical_encoding,
"normalize": self.normalize,
"seed": self.seed,
"column_types": self.column_types,
"column_names": self.column_names,
}
)
return config
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
class TimeseriesBlock(block_module.Block):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def get_config(self):
return super().get_config()
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
output_node = basic.RNNBlock().build(hp, output_node)
return output_node
class GeneralBlock(block_module.Block):
"""A general neural network block when the input type is unknown.
When the input type is unknown. The GeneralBlock would search in a large space
for a good model.
# Arguments
name: String.
"""
def build(self, hp, inputs=None):
raise NotImplementedError