layer_category_encoding
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
| Arguments | Description |
|---|---|
| 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()