tf.keras.layers.CategoryEncoding | TensorFlow v2.16.1 (original) (raw)
tf.keras.layers.CategoryEncoding
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A preprocessing layer which encodes integer features.
Inherits From: Layer, Operation
tf.keras.layers.CategoryEncoding(
num_tokens=None, output_mode='multi_hot', sparse=False, **kwargs
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
| Migrate `tf.feature_column`s to Keras preprocessing layers Working with preprocessing layers | Load CSV data Classify structured data using Keras preprocessing layers |
This layer provides options for condensing data into a categorical encoding when the total number of tokens are known in advance. It accepts integer values as inputs, and it outputs a dense or sparse representation of those inputs. For integer inputs where the total number of tokens is not known, use keras.layers.IntegerLookup instead.
Examples:
One-hot encoding data
layer = keras.layers.CategoryEncoding(
num_tokens=4, output_mode="one_hot")
layer([3, 2, 0, 1])
array([[0., 0., 0., 1.],
[0., 0., 1., 0.],
[1., 0., 0., 0.],
[0., 1., 0., 0.]]>
Multi-hot encoding data
layer = keras.layers.CategoryEncoding(
num_tokens=4, output_mode="multi_hot")
layer([[0, 1], [0, 0], [1, 2], [3, 1]])
array([[1., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 1., 1., 0.],
[0., 1., 0., 1.]]>
Using weighted inputs in "count" mode
layer = keras.layers.CategoryEncoding(
num_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)
array([[0.1, 0.2, 0. , 0. ],
[0.2, 0. , 0. , 0. ],
[0. , 0.2, 0.3, 0. ],
[0. , 0.2, 0. , 0.4]]>
| Args | |
|---|---|
| num_tokens | The total number of tokens the layer should support. All inputs to the layer must integers in the range 0 <= value < num_tokens, or an error will be thrown. |
| output_mode | Specification for the output of the layer. Values can be "one_hot", "multi_hot" or "count", configuring the layer as follows:- `"one_hot"`: Encodes each individual element in the input into an array of `num_tokens` size, 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 of `num_tokens` size, 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"`: Like `"multi_hot"`, but the int array contains a count of the number of times the token at that index appeared in the sample. For all output modes, currently only output up to rank 2 is supported. Defaults to "multi_hot". |
| sparse | Whether to return a sparse tensor; for backends that support sparse tensors. |
| Call arguments | |
|---|---|
| inputs | A 1D or 2D tensor of integer inputs. |
| count_weights | A tensor in the same shape as inputs indicating the weight for each sample value when summing up in count mode. Not used in "multi_hot" or "one_hot" modes. |
| 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
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).
| Args | |
|---|---|
| config | A Python dictionary, typically the output of get_config. |
| Returns |
|---|
| A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)