118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
import tensorflow as tf
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from collections import namedtuple
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from tensorflow.keras.initializers import RandomNormal
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# 默认分组名称
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DEFAULT_GROUP_NAME="default_group"
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# 定义feature columns父类
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BaseDenseFeat = namedtuple('DenseFeat',
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['name', 'dimension', 'dtype', 'transform_fn'])
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BaseSparseFeat = namedtuple('SparseFeat',
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['name', 'vocabulary_size', 'embedding_dim',
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'use_hash', 'vocabulary_path', 'dtype',
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'embeddings_initializer','embedding_name',
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'group_name', 'trainable'])
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BaseVarLenSparseFeat = namedtuple('VarLenSparseFeat',
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['sparsefeat', 'maxlen', 'combiner',
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'length_name', 'weight_name', 'weight_norm'])
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class DenseFeat(BaseDenseFeat):
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""" Dense feature
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Args:
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name: feature name,
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dimension: dimension of the feature, default = 1.
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dtype: dtype of the feature, default="float32".
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transform_fn: If not `None` , a function that can be used to transform
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values of the feature. the function takes the input Tensor as its
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argument, and returns the output Tensor.
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(e.g. lambda x: (x - 3.0) / 4.2).
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"""
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# 加上__slots__ = ()限制，在生成实例的时候，不会为实例生成一个属性字典，
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# 可以节省内存
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__slots__ = ()
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def __new__(cls, name, dimension=1, dtype="float32", transform_fn=None):
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return super(DenseFeat, cls).__new__(
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cls, name, dimension, dtype, transform_fn)
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def __hash__(self):
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return self.name.__hash__()
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class SparseFeat(BaseSparseFeat):
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__slots__ = ()
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def __new__(cls, name, vocabulary_size, embedding_dim=4, use_hash=False,
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vocabulary_path=None, dtype="int32", embeddings_initializer=None,
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embedding_name=None, group_name=DEFAULT_GROUP_NAME, trainable=True):
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if embedding_dim == "auto":
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embedding_dim = 6 * int(pow(vocabulary_size, 0.25))
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if embeddings_initializer is None:
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# 随机初始化
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embeddings_initializer = RandomNormal(
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mean=0.0, stddev=0.0001, seed=2020)
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if embedding_name is None:
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embedding_name = name
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return super(SparseFeat, cls).__new__(cls, name, vocabulary_size,
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embedding_dim, use_hash, vocabulary_path, dtype,
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embeddings_initializer, embedding_name, group_name, trainable)
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def __hash__(self):
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return self.name.__hash__()
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class VarLenSparseFeat(BaseVarLenSparseFeat):
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__slots__ = ()
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def __new__(cls, sparsefeat, maxlen, combiner="mean", length_name=None,
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weight_name=None, weight_norm=True):
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return super(VarLenSparseFeat, cls).__new__(cls, sparsefeat, maxlen,
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combiner, length_name, weight_name, weight_norm)
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@property
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def name(self):
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return self.sparsefeat.name
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@property
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def vocabulary_size(self):
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return self.sparsefeat.vocabulary_size
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@property
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def embedding_dim(self):
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return self.sparsefeat.embedding_dim
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@property
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def use_hash(self):
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return self.sparsefeat.use_hash
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@property
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def vocabulary_path(self):
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return self.sparsefeat.vocabulary_path
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@property
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def dtype(self):
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return self.sparsefeat.dtype
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@property
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def embeddings_initializer(self):
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return self.sparsefeat.embeddings_initializer
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@property
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def embedding_name(self):
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return self.sparsefeat.embedding_name
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@property
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def group_name(self):
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return self.sparsefeat.group_name
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@property
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def trainable(self):
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return self.sparsefeat.trainable
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def __hash__(self):
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return self.name.__hash__()
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