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keras (version 2.13.0)

layer_string_lookup: A preprocessing layer which maps string features to integer indices.

Description

A preprocessing layer which maps string features to integer indices.

Usage

layer_string_lookup(
  object,
  max_tokens = NULL,
  num_oov_indices = 1L,
  mask_token = NULL,
  oov_token = "[UNK]",
  vocabulary = NULL,
  idf_weights = NULL,
  encoding = "utf-8",
  invert = FALSE,
  output_mode = "int",
  sparse = FALSE,
  pad_to_max_tokens = FALSE,
  ...
)

Arguments

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.

max_tokens

Maximum size of the vocabulary for this layer. This should only be specified when adapting the vocabulary or when setting pad_to_max_tokens = TRUE. If NULL, there is no cap on the size of the vocabulary. Note that this size includes the OOV and mask tokens. Defaults to NULL.

num_oov_indices

The number of out-of-vocabulary tokens to use. If this value is more than 1, OOV inputs are hashed to determine their OOV value. If this value is 0, OOV inputs will cause an error when calling the layer. Defaults to 1.

mask_token

A token that represents masked inputs. When output_mode is "int", the token is included in vocabulary and mapped to index 0. In other output modes, the token will not appear in the vocabulary and instances of the mask token in the input will be dropped. If set to NULL, no mask term will be added. Defaults to NULL.

oov_token

Only used when invert is TRUE. The token to return for OOV indices. Defaults to "[UNK]".

vocabulary

Optional. Either an array of strings or a string path to a text file. If passing an array, can pass a character vector or or 1D tensor containing the string vocabulary terms. If passing a file path, the file should contain one line per term in the vocabulary. If this argument is set, there is no need to adapt() the layer.

idf_weights

Only valid when output_mode is "tf_idf". An array, or 1D tensor or the same length as the vocabulary, containing the floating point inverse document frequency weights, which will be multiplied by per sample term counts for the final tf_idf weight. If the vocabulary argument is set, and output_mode is "tf_idf", this argument must be supplied.

encoding

Optional. The text encoding to use to interpret the input strings. Defaults to "utf-8".

invert

Only valid when output_mode is "int". If TRUE, this layer will map indices to vocabulary items instead of mapping vocabulary items to indices. Default to FALSE.

output_mode

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

  • "int": Return the raw integer indices of the input tokens.

  • "one_hot": Encodes each individual element in the input into an array the same size as the vocabulary, 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 the same size as the vocabulary, 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": As "multi_hot", but the int array contains a count of the number of times the token at that index appeared in the sample.

  • "tf_idf": As "multi_hot", but the TF-IDF algorithm is applied to find the value in each token slot. For "int" output, any shape of input and output is supported. For all other output modes, currently only output up to rank 2 is supported.

sparse

Boolean. Only applicable when output_mode is "multi_hot", "count", or "tf_idf". If TRUE, returns a SparseTensor instead of a dense Tensor. Defaults to FALSE.

pad_to_max_tokens

Only applicable when output_mode is "multi_hot", "count", or "tf_idf". If TRUE, the output will have its feature axis padded to max_tokens even if the number of unique tokens in the vocabulary is less than max_tokens, resulting in a tensor of shape [batch_size, max_tokens] regardless of vocabulary size. Defaults to FALSE.

...

standard layer arguments.

Details

This layer translates a set of arbitrary strings into integer output via a table-based vocabulary lookup. This layer will perform no splitting or transformation of input strings. For a layer than can split and tokenize natural language, see the layer_text_vectorization() layer.

The vocabulary for the layer must be either supplied on construction or learned via adapt(). During adapt(), the layer will analyze a data set, determine the frequency of individual strings tokens, and create a vocabulary from them. If the vocabulary is capped in size, the most frequent tokens will be used to create the vocabulary and all others will be treated as out-of-vocabulary (OOV).

There are two possible output modes for the layer. When output_mode is "int", input strings are converted to their index in the vocabulary (an integer). When output_mode is "multi_hot", "count", or "tf_idf", input strings are encoded into an array where each dimension corresponds to an element in the vocabulary.

The vocabulary can optionally contain a mask token as well as an OOV token (which can optionally occupy multiple indices in the vocabulary, as set by num_oov_indices). The position of these tokens in the vocabulary is fixed. When output_mode is "int", the vocabulary will begin with the mask token (if set), followed by OOV indices, followed by the rest of the vocabulary. When output_mode is "multi_hot", "count", or "tf_idf" the vocabulary will begin with OOV indices and instances of the mask token will be dropped.

For an overview and full list of preprocessing layers, see the preprocessing guide.

See Also

Other categorical features preprocessing layers: layer_category_encoding(), layer_hashing(), layer_integer_lookup()

Other preprocessing layers: layer_category_encoding(), 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_text_vectorization()