Category encoding layer.
Inherits From: PreprocessingLayer
, Layer
, Module
tf.keras.layers.experimental.preprocessing.CategoryEncoding( max_tokens=None, output_mode=BINARY, sparse=False, **kwargs )
This layer provides options for condensing data into a categorical encoding. It accepts integer values as inputs and outputs a dense representation (one sample = 1-index tensor of float values representing data about the sample's tokens) of those inputs.
layer = tf.keras.layers.experimental.preprocessing.CategoryEncoding( max_tokens=4, output_mode="count") layer([[0, 1], [0, 0], [1, 2], [3, 1]]) <tf.Tensor: shape=(4, 4), dtype=float32, numpy= array([[1., 1., 0., 0.], [2., 0., 0., 0.], [0., 1., 1., 0.], [0., 1., 0., 1.]], dtype=float32)>
Examples with weighted inputs:
layer = tf.keras.layers.experimental.preprocessing.CategoryEncoding( max_tokens=4, output_mode="count") count_weights = np.array([[.1, .2], [.1, .1], [.2, .3], [.4, .2]]) layer([[0, 1], [0, 0], [1, 2], [3, 1]], count_weights=count_weights) <tf.Tensor: shape=(4, 4), dtype=float64, numpy= array([[0.1, 0.2, 0. , 0. ], [0.2, 0. , 0. , 0. ], [0. , 0.2, 0.3, 0. ], [0. , 0.2, 0. , 0.4]])>
inputs
: A 2D tensor (samples, timesteps)
.count_weights
: A 2D tensor in the same shape as inputs
indicating the weight for each sample value when summing up in count
mode. Not used in binary
or tfidf
mode.Attributes | |
---|---|
max_tokens | The maximum size of the vocabulary for this layer. If None, there is no cap on the size of the vocabulary. |
output_mode | Specification for the output of the layer. Defaults to "binary". Values can be "binary", "count" or "tf-idf", configuring the layer as follows: "binary": Outputs a single int array per batch, of either vocab_size or max_tokens 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. |
sparse | Boolean. If true, returns a SparseTensor instead of a dense Tensor . Defaults to False . |
adapt
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
set_num_elements( num_elements )
set_tfidf_data
set_tfidf_data( tfidf_data )
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/experimental/preprocessing/CategoryEncoding