哈希交叉层
HashedCrossing class
tf_keras.layers.HashedCrossing(num_bins, output_mode="int", sparse=False, **kwargs)
A preprocessing layer which crosses features using the "hashing trick".
This layer performs crosses of categorical features using the "hashing
trick". Conceptually, the transformation can be thought of as:
hash(concatenate(features)) % num_bins.
This layer currently only performs crosses of scalar inputs and batches of
scalar inputs. Valid input shapes are (batch_size, 1), (batch_size,) and
().
For an overview and full list of preprocessing layers, see the preprocessing guide.
Arguments
- num_bins: Number of hash bins.
- output_mode: Specification for the output of the layer. Values can be
"int", or"one_hot"configuring the layer as follows:"int": Return the integer bin indices directly."one_hot": Encodes each individual element in the input into an array the same size asnum_bins, containing a 1 at the input's bin index. Defaults to"int".
- sparse: Boolean. Only applicable to
"one_hot"mode. IfTrue, returns aSparseTensorinstead of a denseTensor. Defaults toFalse. - **kwargs: Keyword arguments to construct a layer.
Examples
Crossing two scalar features.
>>> layer = tf.keras.layers.HashedCrossing(
... num_bins=5)
>>> feat1 = tf.constant(['A', 'B', 'A', 'B', 'A'])
>>> feat2 = tf.constant([101, 101, 101, 102, 102])
>>> layer((feat1, feat2))
<tf.Tensor: shape=(5,), dtype=int64, numpy=array([1, 4, 1, 1, 3])>
Crossing and one-hotting two scalar features.
>>> layer = tf.keras.layers.HashedCrossing(
... num_bins=5, output_mode='one_hot')
>>> feat1 = tf.constant(['A', 'B', 'A', 'B', 'A'])
>>> feat2 = tf.constant([101, 101, 101, 102, 102])
>>> layer((feat1, feat2))
<tf.Tensor: shape=(5, 5), dtype=float32, numpy=
array([[0., 1., 0., 0., 0.],
[0., 0., 0., 0., 1.],
[0., 1., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 0., 1., 0.]], dtype=float32)>