tf.keras.layers.IntegerLookup

A preprocessing layer that maps integers to (possibly encoded) indices.

Inherits From: Layer, Operation

tf.keras.layers.IntegerLookup(
    max_tokens=None,
    num_oov_indices=1,
    mask_token=None,
    oov_token=-1,
    vocabulary=None,
    vocabulary_dtype='int64',
    idf_weights=None,
    invert=False,
    output_mode='int',
    sparse=False,
    pad_to_max_tokens=False,
    oov_method='floormod',
    salt=None,
    name=None,
    **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Working with preprocessing layers	Load a pandas DataFrame Classify structured data using Keras preprocessing layers Deep & Cross Network (DCN)

This layer maps a set of arbitrary integer input tokens into indexed integer output via a table-based vocabulary lookup. The layer's output indices will be contiguously arranged up to the maximum vocab size, even if the input tokens are non-continguous or unbounded. The layer supports multiple options for encoding the output via output_mode, and has optional support for out-of-vocabulary (OOV) tokens and masking.

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 integer 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 OOV.

There are two possible output modes for the layer. When output_mode is "int", input integers are converted to their index in the vocabulary (an integer). When output_mode is "multi_hot", "count", or "tf_idf", input integers 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 at index 0, 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.

Note: This layer uses TensorFlow internally. 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 tf.data pipeline (independently of which backend you're using).

Args
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 None, there is no cap on the size of the vocabulary. Note that this size includes the OOV and mask tokens. Defaults to None.
num_oov_indices	The number of out-of-vocabulary tokens to use. If this value is more than 1, OOV inputs are hashed or modulated to determine their OOV value (see oov_method). If this value is 0, OOV inputs will cause an error when calling the layer. Defaults to 1.
mask_token	An integer 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 None, no mask term will be added. Defaults to None.
oov_token	Only used when invert is True. The token to return for OOV indices. Defaults to -1.
vocabulary	Optional. Either an array of integers or a string path to a text file. If passing an array, can pass a tuple, list, 1D NumPy array, or 1D tensor containing the integer vocbulary 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.
vocabulary_dtype	The dtype of the vocabulary terms. Only vocabulary_dtype='int64' is supported at this time. Defaults to "int64".
idf_weights	Only valid when output_mode is "tf_idf". A tuple, list, 1D NumPy 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.
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. Defaults to False.
output_mode	Specification for the output of the layer. Values can be "int", "one_hot", "multi_hot", "count", or "tf_idf" configuring the layer as follows: "int": Return the vocabulary 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, the output shape matches the input shape. For "one_hot" output, the output shape is input_shape + (vocabulary_size,), where input_shape may have arbitrary rank. For other output modes ("multi_hot", "count", "tf_idf"), the output shape is (batch_size, vocabulary_size). Defaults to "int".
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.
sparse	Boolean. Only applicable to "multi_hot", "count", and "tf_idf" output modes. Only supported with TensorFlow backend. If True, returns a SparseTensor instead of a dense Tensor. Defaults to False.
oov_method	Only relevant when num_oov_indices > 1. Controls how OOV tokens are assigned to OOV buckets. "floormod" (default): uses token % num_oov_indices. Preserves backwards compatibility but can produce severe bucket imbalance when input IDs share a common factor with num_oov_indices (e.g. all-even IDs with an even bucket count). "farmhash": applies FarmHash64. Distributes OOV tokens uniformly regardless of the arithmetic structure of the input IDs. This parameter is ignored for string inputs, which always use FarmHash64.
salt	Only valid when oov_method="farmhash". If passed, the hash function used for OOV bucket assignment will be SipHash64, with these values used as an additional input (known as a "salt" in cryptography). Can be a tuple or list of 2 integers, or a single integer (which is used for both key components). If None (default), FarmHash64 is used. Defaults to None.

Examples:

Creating a lookup layer with a known vocabulary

This example creates a lookup layer with a pre-existing vocabulary.

vocab = [12, 36, 1138, 42]
data = np.array([[12, 1138, 42], [42, 1000, 36]])  # Note OOV tokens
layer = IntegerLookup(vocabulary=vocab)
layer(data)
array([[1, 3, 4],
       [4, 0, 2]])

Creating a lookup layer with an adapted vocabulary

This example creates a lookup layer and generates the vocabulary by analyzing the dataset.

data = np.array([[12, 1138, 42], [42, 1000, 36]])
layer = IntegerLookup()
layer.adapt(data)
layer.get_vocabulary()
[-1, 42, 1138, 1000, 36, 12]

Note that the OOV token -1 have been added to the vocabulary. The remaining tokens are sorted by frequency (42, which has 2 occurrences, is first) then by inverse sort order.

data = np.array([[12, 1138, 42], [42, 1000, 36]])
layer = IntegerLookup()
layer.adapt(data)
layer(data)
array([[5, 2, 1],
       [1, 3, 4]])

Lookups with multiple OOV indices

This example demonstrates how to use a lookup layer with multiple OOV indices. When a layer is created with more than one OOV index, any OOV tokens are hashed or modulated into the number of OOV buckets, distributing OOV tokens in a deterministic fashion across the set. Use oov_method to control whether floormod or FarmHash64 is used.

vocab = [12, 36, 1138, 42]
data = np.array([[12, 1138, 42], [37, 1000, 36]])
layer = IntegerLookup(vocabulary=vocab, num_oov_indices=2)
layer(data)
array([[2, 4, 5],
       [1, 0, 3]])

Note that the output for OOV token 37 is 1, while the output for OOV token 1000 is 0. The in-vocab terms have their output index increased by 1 from earlier examples (12 maps to 2, etc) in order to make space for the extra OOV token.

Uniform OOV distribution with FarmHash

This example shows how oov_method="farmhash" avoids the bucket imbalance that "floormod" produces for arithmetically structured input IDs.

vocab = [10, 20, 30]
layer_floormod = IntegerLookup(
    vocabulary=vocab, num_oov_indices=4, oov_method="floormod")
layer_farmhash = IntegerLookup(
    vocabulary=vocab, num_oov_indices=4, oov_method="farmhash")
oov_values = np.array([100, 300, 700, 1100, 1700, 2000])
layer_floormod(oov_values)
tf.Tensor([0 0 0 0 0 0], shape=(6,), dtype=int64) # All map to index 0
layer_farmhash(oov_values)
tf.Tensor([3 3 2 2 0 1], shape=(6,), dtype=int64) # Spread across indices

One-hot output

Configure the layer with output_mode='one_hot'. Note that the first num_oov_indices dimensions in the ont_hot encoding represent OOV values.

vocab = [12, 36, 1138, 42]
data = np.array([12, 36, 1138, 42, 7])  # Note OOV tokens
layer = IntegerLookup(vocabulary=vocab, output_mode='one_hot')
layer(data)
array([[0., 1., 0., 0., 0.],
        [0., 0., 1., 0., 0.],
        [0., 0., 0., 1., 0.],
        [0., 0., 0., 0., 1.],
        [1., 0., 0., 0., 0.]], dtype=float32)

Multi-hot output

Configure the layer with output_mode='multi_hot'. Note that the first num_oov_indices dimensions in the multi_hot encoding represent OOV tokens

vocab = [12, 36, 1138, 42]
data = np.array([[12, 1138, 42, 42],
                 [42,    7, 36,  7]])  # Note OOV tokens
layer = IntegerLookup(vocabulary=vocab, output_mode='multi_hot')
layer(data)
array([[0., 1., 0., 1., 1.],
       [1., 0., 1., 0., 1.]], dtype=float32)

Token count output

Configure the layer with output_mode='count'. As with multi_hot output, the first num_oov_indices dimensions in the output represent OOV tokens.

vocab = [12, 36, 1138, 42]
data = np.array([[12, 1138, 42, 42],
                 [42,    7, 36,  7]])  # Note OOV tokens
layer = IntegerLookup(vocabulary=vocab, output_mode='count')
layer(data)
array([[0., 1., 0., 1., 2.],
       [2., 0., 1., 0., 1.]], dtype=float32)

TF-IDF output

Configure the layer with output_mode='tf_idf'. As with multi_hot output, the first num_oov_indices dimensions in the output represent OOV tokens.

Each token bin will output token_count * idf_weight, where the idf weights are the inverse document frequency weights per token. These should be provided along with the vocabulary. Note that the idf_weight for OOV tokens will default to the average of all idf weights passed in.

vocab = [12, 36, 1138, 42]
idf_weights = [0.25, 0.75, 0.6, 0.4]
data = np.array([[12, 1138, 42, 42],
                 [42,    7, 36,  7]])  # Note OOV tokens
layer = IntegerLookup(
    output_mode='tf_idf', vocabulary=vocab, idf_weights=idf_weights)
layer(data)
array([[0.  , 0.25, 0.  , 0.6 , 0.8 ],
        [1.0 , 0.  , 0.75, 0.  , 0.4 ]], dtype=float32)

To specify the idf weights for oov tokens, you will need to pass the entire vocabulary including the leading oov token.

vocab = [-1, 12, 36, 1138, 42]
idf_weights = [0.9, 0.25, 0.75, 0.6, 0.4]
data = np.array([[12, 1138, 42, 42],
                 [42,    7, 36,  7]])  # Note OOV tokens
layer = IntegerLookup(
    output_mode='tf_idf', vocabulary=vocab, idf_weights=idf_weights)
layer(data)
array([[0.  , 0.25, 0.  , 0.6 , 0.8 ],
        [1.8 , 0.  , 0.75, 0.  , 0.4 ]], dtype=float32)

When adapting the layer in "tf_idf" mode, each input sample will be considered a document, and IDF weight per token will be calculated as: log(1 + num_documents / (1 + token_document_count)).

Inverse lookup

This example demonstrates how to map indices to tokens using this layer. (You can also use adapt() with inverse=True, but for simplicity we'll pass the vocab in this example.)

vocab = [12, 36, 1138, 42]
data = np.array([[1, 3, 4], [4, 0, 2]])
layer = IntegerLookup(vocabulary=vocab, invert=True)
layer(data)
array([[  12, 1138,   42],
       [  42,   -1,   36]])

Note that the first index correspond to the oov token by default.

Forward and inverse lookup pairs

This example demonstrates how to use the vocabulary of a standard lookup layer to create an inverse lookup layer.

vocab = [12, 36, 1138, 42]
data = np.array([[12, 1138, 42], [42, 1000, 36]])
layer = IntegerLookup(vocabulary=vocab)
i_layer = IntegerLookup(
    vocabulary=layer.get_vocabulary(), invert=True)
int_data = layer(data)
i_layer(int_data)
array([[  12, 1138,   42],
       [  42,   -1,   36]])

In this example, the input token 1000 resulted in an output of -1, since 1000 was not in the vocabulary - it got represented as an OOV, and all OOV tokens are returned as -1 in the inverse layer. Also, note that for the inverse to work, you must have already set the forward layer vocabulary either directly or via adapt() before calling get_vocabulary().

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

adapt

View source

adapt(
    data, steps=None
)

Computes a vocabulary of integer terms from tokens in a dataset.

Calling adapt() on an IntegerLookup layer is an alternative to passing in a precomputed vocabulary on construction via the vocabulary argument. An IntegerLookup layer should always be either adapted over a dataset or supplied with a vocabulary.

During adapt(), the layer will build a vocabulary of all integer tokens seen in the dataset, sorted by occurrence count, with ties broken by sort order of the tokens (high to low). At the end of adapt(), if max_tokens is set, the vocabulary will be truncated to max_tokens size. For example, adapting a layer with max_tokens=1000 will compute the 1000 most frequent tokens occurring in the input dataset. If output_mode='tf-idf', adapt() will also learn the document frequencies of each token in the input dataset.

Arguments
data	The data to train on. It can be passed either as a batched tf.data.Dataset, as a list of integers, or as a NumPy array.
steps	Integer or None. Total number of steps (batches of samples) to process. If data is a tf.data.Dataset, and steps is None, adapt() will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps argument. This argument is not supported with array inputs or list inputs.

finalize_state

View source

finalize_state()

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.

get_vocabulary

View source

get_vocabulary(
    include_special_tokens=True
)

Returns the current vocabulary of the layer.

Args
include_special_tokens	If True, the returned vocabulary will include mask and OOV tokens, and a term's index in the vocabulary will equal the term's index when calling the layer. If False, the returned vocabulary will not include any mask or OOV tokens.

load_assets

View source

load_assets(
    dir_path
)

reset_state

View source

reset_state()

save_assets

View source

save_assets(
    dir_path
)

set_vocabulary

View source

set_vocabulary(
    vocabulary, idf_weights=None
)

Sets vocabulary (and optionally document frequency) for this layer.

This method sets the vocabulary and idf weights for this layer directly, instead of analyzing a dataset through adapt. It should be used whenever the vocab (and optionally document frequency) information is already known. If vocabulary data is already present in the layer, this method will replace it.

Args
vocabulary	Either an array or a string path to a text file. If passing an array, can pass a tuple, list, 1D numpy array, or 1D tensor containing the vocbulary terms. If passing a file path, the file should contain one line per term in the vocabulary.
idf_weights	A tuple, list, 1D numpy array, or 1D tensor of inverse document frequency weights with equal length to vocabulary. Must be set if output_mode is "tf_idf". Should not be set otherwise.

update_state

View source

update_state(
    data
)

vocabulary_size

View source

vocabulary_size()

Gets the current size of the layer's vocabulary.

Returns
The integer size of the vocabulary, including optional mask and oov indices.