tf.keras.layers.CategoryEncoding

A preprocessing layer which encodes integer features.

Inherits From: Layer, Operation

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

Used in the notebooks

Used in the guide Used in the tutorials

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.

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="multi_hot")
layer([[0, 1], [0, 0], [1, 2], [3, 1]])
array([[1., 1., 0., 0.],
        [1., 0., 0., 0.],
        [0., 1., 1., 0.],
        [0., 1., 0., 1.]]>

Using weighted inputs in "count" mode

layer = keras.layers.CategoryEncoding(
          num_tokens=4, output_mode="count")
count_weights = np.array([[.1, .2], [.1, .1], [.2, .3], [.4, .2]])
layer([[0, 1], [0, 0], [1, 2], [3, 1]], count_weights=count_weights)
  array([[0.1, 0.2, 0. , 0. ],
         [0.2, 0. , 0. , 0. ],
         [0. , 0.2, 0.3, 0. ],
         [0. , 0.2, 0. , 0.4]]>

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.

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.

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

convert_weight

View source

convert_weight(
    weight
)

Convert the weight if it is from the a different backend.

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config A Python dictionary, typically the output of get_config.

Returns
An operation instance.