# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Keras string lookup preprocessing layer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import dtypes from tensorflow.python.keras.layers.preprocessing import index_lookup from tensorflow.python.keras.layers.preprocessing import table_utils from tensorflow.python.util.tf_export import keras_export @keras_export("keras.layers.experimental.preprocessing.IntegerLookup", v1=[]) class IntegerLookup(index_lookup.IndexLookup): """Maps integers from a vocabulary to integer indices. This layer translates a set of arbitrary integers into an integer output via a table-based lookup, with optional out-of-vocabulary handling. If desired, the user can call this layer's `adapt()` method on a data set, which will analyze the data set, determine the frequency of individual string values, and create a vocabulary from them. This vocabulary can have unlimited size or be capped, depending on the configuration options for this layer; if there are more unique values in the input than the maximum vocabulary size, the most frequent terms will be used to create the vocabulary. Attributes: max_values: The maximum size of the vocabulary for this layer. If None, there is no cap on the size of the vocabulary. Note that this vocabulary includes the OOV and mask values, so the effective number of values is (max_values - num_oov_values - (1 if mask_token else 0)) num_oov_indices: The number of out-of-vocabulary values to use; defaults to 1. If this value is more than 1, OOV inputs are modulated to determine their OOV value; if this value is 0, passing an OOV input will result in a '-1' being returned for that value in the output tensor. (Note that, because the value is -1 and not 0, this will allow you to effectively drop OOV values from categorical encodings.) mask_value: A value that represents masked inputs, and which is mapped to index 0. Defaults to 0. If set to None, no mask term will be added and the OOV values, if any, will be indexed from (0...num_oov_values) instead of (1...num_oov_values+1). oov_value: The value representing an out-of-vocabulary value. Defaults to -1. vocabulary: An optional list of values, or a path to a text file containing a vocabulary to load into this layer. The file should contain one value per line. If the list or file contains the same token multiple times, an error will be thrown. invert: If true, this layer will map indices to vocabulary items instead of mapping vocabulary items to indices. 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 = tf.constant([[12, 1138, 42], [42, 1000, 36]]) >>> layer = IntegerLookup(vocabulary=vocab) >>> layer(data) Creating a lookup layer with an adapted vocabulary This example creates a lookup layer and generates the vocabulary by analyzing the dataset. >>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]]) >>> layer = IntegerLookup() >>> layer.adapt(data) >>> layer.get_vocabulary() [0, -1, 42, 1138, 1000, 36, 12] Note how the mask value 0 and the OOV value -1 have been added to the vocabulary. The remaining values are sorted by frequency (1138, which has 2 occurrences, is first) then by inverse sort order. >>> data = tf.constant([[12, 1138, 42], [42, 1000, 36]]) >>> layer = IntegerLookup() >>> layer.adapt(data) >>> layer(data) Lookups with multiple OOV tokens. This example demonstrates how to use a lookup layer with multiple OOV tokens. When a layer is created with more than one OOV token, any OOV values are hashed into the number of OOV buckets, distributing OOV values in a deterministic fashion across the set. >>> vocab = [12, 36, 1138, 42] >>> data = tf.constant([[12, 1138, 42], [37, 1000, 36]]) >>> layer = IntegerLookup(vocabulary=vocab, num_oov_indices=2) >>> layer(data) Note that the output for OOV value 37 is 2, while the output for OOV value 1000 is 1. The in-vocab terms have their output index increased by 1 from earlier examples (12 maps to 3, etc) in order to make space for the extra OOV value. Inverse lookup This example demonstrates how to map indices to values 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 = tf.constant([[1, 3, 4], [4, 5, 2]]) >>> layer = IntegerLookup(vocabulary=vocab, invert=True) >>> layer(data) Note that the integer 5, which is out of the vocabulary space, returns an OOV token. 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 = tf.constant([[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) In this example, the input value 1000 resulted in an output of -1, since 1000 was not in the vocabulary - it got represented as an OOV, and all OOV values 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 fit() before calling get_vocabulary(). """ def __init__(self, max_values=None, num_oov_indices=1, mask_value=0, oov_value=-1, vocabulary=None, invert=False, **kwargs): allowed_dtypes = [dtypes.int64] if "dtype" in kwargs and kwargs["dtype"] not in allowed_dtypes: raise ValueError("IntegerLookup may only have a dtype in %s." % allowed_dtypes) if "dtype" not in kwargs: kwargs["dtype"] = dtypes.int64 # If max_values is set, the value must be greater than 1 - otherwise we # are creating a 0-element vocab, which doesn't make sense. if max_values is not None and max_values <= 1: raise ValueError("If set, max_values must be greater than 1.") if num_oov_indices < 0: raise ValueError("num_oov_indices must be greater than 0. You passed %s" % num_oov_indices) if vocabulary is not None: if isinstance(vocabulary, str): vocabulary = table_utils.get_vocabulary_from_file(vocabulary) vocabulary = [int(v) for v in vocabulary] super(IntegerLookup, self).__init__( max_tokens=max_values, num_oov_indices=num_oov_indices, mask_token=mask_value, oov_token=oov_value, vocabulary=vocabulary, invert=invert, **kwargs) def get_config(self): base_config = super(IntegerLookup, self).get_config() # Because the super config has a bunch of args we're also passing, # we need to rename and remove them from the config dict. base_config["max_values"] = base_config["max_tokens"] del base_config["max_tokens"] base_config["mask_value"] = base_config["mask_token"] del base_config["mask_token"] base_config["oov_value"] = base_config["oov_token"] del base_config["oov_token"] return base_config