Performs an embedding lookup suitable for accelerator devices.

This layer uses either tf.gather or tf.one_hot to translate integer indices to float embeddings.

vocab_size Number of elements in the vocabulary.
embedding_width Output size of the embedding layer.
initializer The initializer to use for the embedding weights. Defaults to "glorot_uniform".
use_one_hot Whether to use tf.one_hot over tf.gather for the embedding lookup. Defaults to False (that is, using tf.gather). Setting this option to True may improve performance, especially on small vocabulary sizes, but will generally require more memory.
scale_factor Whether to scale the output embeddings. Defaults to None (that is, not to scale). Setting this option to a float will let values in output embeddings multiplied by scale_factor.
weight_fallback_dtype When keras mix precision inferred wrong dtype for varibales, weight_fallback_dtype will be used to define the dtype of weights.





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This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

inputs Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules:

  • inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument.
  • NumPy array or Python scalar values in inputs get cast as tensors.
  • Keras mask metadata is only collected from inputs.
  • Layers are built (build(input_shape) method) using shape info from inputs only.
  • input_spec compatibility is only checked against inputs.
  • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
  • The SavedModel input specification is generated using inputs only.
  • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved:
  • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
  • mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).
  • Returns
    A tensor or list/tuple of tensors.