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.
layer_category_encoding(
object,
num_tokens = NULL,
output_mode = "multi_hot",
sparse = FALSE,
...
)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.
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.
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.
Boolean. If TRUE, returns a SparseTensor instead of a dense
Tensor. Defaults to FALSE.
standard layer arguments.
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()