tf.compat.v1.keras.layers.experimental.preprocessing.CategoryEncoding CategoryEncoding layer. Inherits From: CategoryEncoding, PreprocessingLayer, Layer, Module 
tf.compat.v1.keras.layers.experimental.preprocessing.CategoryEncoding(
    max_tokens=None, output_mode=BINARY, sparse=False, **kwargs
)
 This layer provides options for condensing input data into denser representations. It accepts either integer values or strings as inputs, allows users to map those inputs into a contiguous integer space, and outputs either those integer values (one sample = 1D tensor of integer token indices) or a dense representation (one sample = 1D tensor of float values representing data about the sample's tokens). If desired, the user can call this layer's adapt() method on a dataset. When this layer is adapted, it will analyze the dataset, determine the frequency of individual integer or 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_elements   The maximum size of the vocabulary for this layer. If None, there is no cap on the size of the vocabulary.  
  output_mode   Optional specification for the output of the layer. Values can be "int", "binary", "count" or "tf-idf", configuring the layer as follows: "int": Outputs integer indices, one integer index per split string token. "binary": Outputs a single int array per batch, of either vocab_size or max_elements size, containing 1s in all elements where the token mapped to that index exists at least once in the batch item. "count": As "binary", but the int array contains a count of the number of times the token at that index appeared in the batch item. "tf-idf": As "binary", but the TF-IDF algorithm is applied to find the value in each token slot.  
  output_sequence_length   Only valid in INT mode. If set, the output will have its time dimension padded or truncated to exactly output_sequence_length values, resulting in a tensor of shape [batch_size, output_sequence_length] regardless of the input shape.  
  pad_to_max_elements   Only valid in "binary", "count", and "tf-idf" modes. If True, the output will have its feature axis padded to max_elements even if the number of unique values in the vocabulary is less than max_elements, resulting in a tensor of shape [batch_size, max_elements] regardless of vocabulary size. Defaults to False.    Methods adapt View source 
adapt(
    data, reset_state=True
)
 Fits the state of the preprocessing layer to the dataset. Overrides the default adapt method to apply relevant preprocessing to the inputs before passing to the combiner.
 


 Arguments
  data   The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.  
  reset_state   Optional argument specifying whether to clear the state of the layer at the start of the call to adapt. This must be True for this layer, which does not support repeated calls to adapt.   
 


 Raises
  RuntimeError   if the layer cannot be adapted at this time.    set_num_elements View source 
set_num_elements(
    num_elements
)
 set_tfidf_data View source 
set_tfidf_data(
    tfidf_data
)