Keras
Keras 3 API documentation
Layers API
Preprocessing layers
Categorical features preprocessing layers
CategoryEncoding layer

CategoryEncoding layer

source

CategoryEncoding class 

keras.layers.CategoryEncoding(
    num_tokens=None, output_mode="multi_hot", sparse=False, **kwargs
)

A preprocessing layer which encodes integer features.

This layer provides options for condensing data into a categorical encoding when the total number of tokens are known in advance. It accepts integer values as inputs, and it outputs a dense or sparse representation of those inputs. For integer inputs where the total number of tokens is not known, use keras.layers.IntegerLookup instead.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you’re using).

Examples

One-hot encoding data

>>> layer = keras.layers.CategoryEncoding(
...           num_tokens=4, output_mode="one_hot")
>>> layer([3, 2, 0, 1])
array([[0., 0., 0., 1.],
        [0., 0., 1., 0.],
        [1., 0., 0., 0.],
        [0., 1., 0., 0.]]>

Multi-hot encoding data

>>> layer = keras.layers.CategoryEncoding(
...           num_tokens=4, output_mode="one_hot")
>>> layer([3, 2, 0, 1])
array([[0., 0., 0., 1.],
        [0., 0., 1., 0.],
        [1., 0., 0., 0.],
        [0., 1., 0., 0.]]>

Using weighted inputs in "count" mode

>>> layer = keras.layers.CategoryEncoding(
...           num_tokens=4, output_mode="one_hot")
>>> layer([3, 2, 0, 1])
array([[0., 0., 0., 1.],
        [0., 0., 1., 0.],
        [1., 0., 0., 0.],
        [0., 1., 0., 0.]]>

Arguments

  • num_tokens: The total number of tokens the layer should support. All inputs to the layer must integers in the range 0 <= value < num_tokens, or an error will be thrown.
  • output_mode: Specification for the output of the layer. Values can be "one_hot", "multi_hot" or "count", configuring the layer as follows: - "one_hot": Encodes each individual element in the input into an array of num_tokens size, containing a 1 at the element index. If the last dimension is size 1, will encode on that dimension. If the last dimension is not size 1, will append a new dimension for the encoded output. - "multi_hot": Encodes each sample in the input into a single array of num_tokens size, containing a 1 for each vocabulary term present in the sample. Treats the last dimension as the sample dimension, if input shape is (..., sample_length), output shape will be (..., num_tokens). - "count": Like "multi_hot", but the int array contains a count of the number of times the token at that index appeared in the sample. For all output modes, currently only output up to rank 2 is supported. Defaults to "multi_hot".
  • sparse: Whether to return a sparse tensor; for backends that support sparse tensors.

Call arguments

  • inputs: A 1D or 2D tensor of integer inputs.
  • count_weights: A tensor in the same shape as inputs indicating the weight for each sample value when summing up in count mode. Not used in "multi_hot" or "one_hot" modes.