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This layer will place each element of its input data into one of several contiguous ranges and output an integer index indicating which range each element was placed in.

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

Usage

layer_discretization(
  object,
  bin_boundaries = NULL,
  num_bins = NULL,
  epsilon = 0.01,
  output_mode = "int",
  sparse = FALSE,
  dtype = NULL,
  name = NULL
)

Arguments

object

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

bin_boundaries

A list of bin boundaries. The leftmost and rightmost bins will always extend to -Inf and Inf, so bin_boundaries = c(0, 1, 2) generates bins (-Inf, 0), [0, 1), [1, 2), and [2, +Inf). If this option is set, adapt() should not be called.

num_bins

The integer number of bins to compute. If this option is set, adapt() should be called to learn the bin boundaries.

epsilon

Error tolerance, typically a small fraction close to zero (e.g. 0.01). Higher values of epsilon increase the quantile approximation, and hence result in more unequal buckets, but could improve performance and resource consumption.

output_mode

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

  • "int": Return the discretized 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. 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 the same size as num_bins, containing a 1 for each bin index index 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": As "multi_hot", but the int array contains a count of the number of times the bin index appeared in the sample. Defaults to "int".

sparse

Boolean. Only applicable to "one_hot", "multi_hot", and "count" output modes. Only supported with TensorFlow backend. If TRUE, returns a SparseTensor instead of a dense Tensor. Defaults to FALSE.

dtype

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

name

String, name for the object

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.

Input Shape

Any array of dimension 2 or higher.

Output Shape

Same as input shape.

Examples

Discretize float values based on provided buckets.

input <- op_array(rbind(c(-1.5, 1, 3.4, 0.5),
                       c(0, 3, 1.3, 0),
                       c(-.5, 0, .5, 1),
                       c(1.5, 2, 2.5, 3)))
output <- input |> layer_discretization(bin_boundaries = c(0, 1, 2))
output

## tf.Tensor(
## [[0 2 3 1]
##  [1 3 2 1]
##  [0 1 1 2]
##  [2 3 3 3]], shape=(4, 4), dtype=int64)

Discretize float values based on a number of buckets to compute.

layer <- layer_discretization(num_bins = 4, epsilon = 0.01)
layer |> adapt(input)
layer(input)

## tf.Tensor(
## [[0 2 3 1]
##  [1 3 2 1]
##  [0 1 1 2]
##  [2 3 3 3]], shape=(4, 4), dtype=int64)

See also

Other numerical features preprocessing layers:
layer_normalization()

Other preprocessing layers:
layer_category_encoding()
layer_center_crop()
layer_feature_space()
layer_hashed_crossing()
layer_hashing()
layer_integer_lookup()
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_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
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_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
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()