A preprocessing layer which maps integer features to contiguous ranges.
layer_integer_lookup(
object,
max_tokens = NULL,
num_oov_indices = 1L,
mask_token = NULL,
oov_token = -1L,
vocabulary = NULL,
vocabulary_dtype = "int64",
idf_weights = NULL,
invert = FALSE,
output_mode = "int",
sparse = FALSE,
pad_to_max_tokens = FALSE,
...
)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.
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.
The number of out-of-vocabulary tokens to use. If this value is more than 1, OOV inputs are modulated to determine their OOV value. If this value is 0, OOV inputs will cause an error when calling the layer. Defaults to 1.
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 NULL, no mask term will be added. Defaults to NULL.
Only used when invert is TRUE. The token to return for OOV
indices. Defaults to -1.
Optional. Either an array of integers or a string path to a
text file. If passing an array, can pass a list, list, 1D numpy array,
or 1D tensor containing the integer 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.
The dtype of the vocabulary terms, for example
"int64" or "int32". Defaults to "int64".
Only valid when output_mode is "tf_idf". A list, 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.
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.
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 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, any shape of input and output is supported. For all
other output modes, currently only output up to rank 2 is supported.
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.
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.
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.
For an overview and full list of preprocessing layers, see the preprocessing guide.
adapt()
https://www.tensorflow.org/api_docs/python/tf/keras/layers/IntegerLookup
https://keras.io/api/layers/preprocessing_layers/categorical/integer_lookup
Other categorical features preprocessing layers:
layer_category_encoding(),
layer_hashing(),
layer_string_lookup()
Other preprocessing layers:
layer_category_encoding(),
layer_center_crop(),
layer_discretization(),
layer_hashing(),
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_string_lookup(),
layer_text_vectorization()