layer_category_encoding {keras3}

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.

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

Usage

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

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

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.

...

For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

• a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.

• a keras_input(), then the output tensor from calling layer(input) is returned.

• NULL or missing, then a Layer instance is returned.

Examples

One-hot encoding data

layer <- layer_category_encoding(num_tokens = 4, output_mode = "one_hot")
x <- op_array(c(3, 2, 0, 1), "int32")
layer(x)

## tf.Tensor(
## [[0. 0. 0. 1.]
##  [0. 0. 1. 0.]
##  [1. 0. 0. 0.]
##  [0. 1. 0. 0.]], shape=(4, 4), dtype=float32)

Multi-hot encoding data

layer <- layer_category_encoding(num_tokens = 4, output_mode = "multi_hot")
x <- op_array(rbind(c(0, 1),
                   c(0, 0),
                   c(1, 2),
                   c(3, 1)), "int32")
layer(x)

## tf.Tensor(
## [[1. 1. 0. 0.]
##  [1. 0. 0. 0.]
##  [0. 1. 1. 0.]
##  [0. 1. 0. 1.]], shape=(4, 4), dtype=float32)

Using weighted inputs in "count" mode

layer <- layer_category_encoding(num_tokens = 4, output_mode = "count")
count_weights <- op_array(rbind(c(.1, .2),
                               c(.1, .1),
                               c(.2, .3),
                               c(.4, .2)))
x <- op_array(rbind(c(0, 1),
                   c(0, 0),
                   c(1, 2),
                   c(3, 1)), "int32")
layer(x, count_weights = count_weights)
#   array([[01, 02, 0. , 0. ],
#          [02, 0. , 0. , 0. ],
#          [0. , 02, 03, 0. ],
#          [0. , 02, 0. , 04]]>

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.

See Also

• https://keras.io/api/layers/preprocessing_layers/categorical/category_encoding#categoryencoding-class

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

Other preprocessing layers:
layer_center_crop()
layer_discretization()
layer_feature_space()
layer_hashed_crossing()
layer_hashing()
layer_integer_lookup()
layer_mel_spectrogram()
layer_normalization()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_rescaling()
layer_resizing()
layer_string_lookup()
layer_text_vectorization()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()