A preprocessing layer which crosses features using the "hashing trick".

This layer performs crosses of categorical features using the "hashing trick". Conceptually, the transformation can be thought of as: hash(concatenate(features)) %% num_bins.

This layer currently only performs crosses of scalar inputs and batches of scalar inputs. Valid input shapes are (batch_size, 1), (batch_size) and ().

Note: This layer wraps tf.keras.layers.HashedCrossing. It cannot be used as part of the compiled computation graph of a model with any backend other than TensorFlow. It can however be used with any backend when running eagerly. It can also always be used as part of an input preprocessing pipeline with any backend (outside the model itself), which is how we recommend to use this layer.

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

Usage

layer_hashed_crossing(
  object,
  num_bins,
  output_mode = "int",
  sparse = FALSE,
  name = NULL,
  dtype = NULL,
  ...
)

Arguments

object

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

num_bins

Number of hash bins.

output_mode

Specification for the output of the layer. Values can be "int", or "one_hot" configuring the layer as follows:

"int": Return the integer bin indices directly.
"one_hot": Encodes each individual element in the input into an array the same size as num_bins, containing a 1 at the input's bin index. Defaults to "int".

sparse

Boolean. Only applicable to "one_hot" mode and only valid when using the TensorFlow backend. If TRUE, returns a SparseTensor instead of a dense Tensor. Defaults to FALSE.

name

String, name for the object

dtype

datatype (e.g., "float32").

...

Keyword arguments to construct a layer.

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

feat1 <- c('A', 'B', 'A', 'B', 'A') |> as.array()
feat2 <- c(101, 101, 101, 102, 102) |> as.integer() |> as.array()

Crossing two scalar features.

layer <- layer_hashed_crossing(num_bins = 5)
layer(list(feat1, feat2))

## tf.Tensor([1 4 1 1 3], shape=(5), dtype=int64)

Crossing and one-hotting two scalar features.

layer <- layer_hashed_crossing(num_bins = 5, output_mode = 'one_hot')
layer(list(feat1, feat2))

## tf.Tensor(
## [[0. 1. 0. 0. 0.]
##  [0. 0. 0. 0. 1.]
##  [0. 1. 0. 0. 0.]
##  [0. 1. 0. 0. 0.]
##  [0. 0. 0. 1. 0.]], shape=(5, 5), dtype=float32)

Other preprocessing layers:
layer_category_encoding()
layer_center_crop()
layer_discretization()
layer_feature_space()
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_category_encoding()
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_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()

A preprocessing layer which crosses features using the "hashing trick".

Usage

Arguments

Value

Examples

See also