layer_category_encoding: A preprocessing layer which encodes integer features.

In keras: R Interface to 'Keras'

A preprocessing layer which encodes integer features.

Description

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 layer_integer_lookup() instead.

Usage

layer_category_encoding(
  object,
  num_tokens = NULL,
  output_mode = "multi_hot",
  sparse = FALSE,
  ...
)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
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. Defaults to "multi_hot". 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.
sparse	Boolean. If TRUE, returns a SparseTensor instead of a dense Tensor. Defaults to FALSE.
...	standard layer arguments.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/CategoryEncoding

• https://keras.io/api/layers/preprocessing_layers/categorical/category_encoding/

Other categorical features preprocessing layers: layer_hashing(), layer_integer_lookup(), layer_string_lookup()

Other preprocessing layers: layer_center_crop(), layer_discretization(), layer_hashing(), layer_integer_lookup(), layer_normalization(), layer_random_brightness(), layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_rotation(), layer_random_translation(), layer_random_width(), layer_random_zoom(), layer_rescaling(), layer_resizing(), layer_string_lookup(), layer_text_vectorization()

keras documentation built on May 29, 2024, 3:20 a.m.