tfm.vision.layers.PositionalEncoding

Creates a network layer that adds a sinusoidal positional encoding.

tfm.vision.layers.PositionalEncoding(
    initializer: tf.keras.initializers.Initializer = 'zeros',
    cache_encoding: bool = False,
    state_prefix: Optional[str] = None,
    **kwargs
)

Positional encoding is incremented across frames, and is added to the input. The positional encoding is first weighted at 0 so that the network can choose to ignore it. This implements:

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin. Attention Is All You Need. (https://arxiv.org/pdf/1706.03762.pdf).

Args
initializer	A str of initializer for weighting the positional encoding.
cache_encoding	A bool. If True, cache the positional encoding tensor after calling build. Otherwise, rebuild the tensor for every call. Setting this to False can be useful when we want to input a variable number of frames, so the positional encoding tensor can change shape.
state_prefix	a prefix string to identify states.
**kwargs	Additional keyword arguments to be passed to this layer.

Methods

call

View source

call(
    inputs: tf.Tensor,
    states: Optional[States] = None,
    output_states: bool = True
) -> Union[tf.Tensor, Tuple[tf.Tensor, States]]

Calls the layer with the given inputs.

Args
inputs	An input tf.Tensor.
states	A dict of states such that, if any of the keys match for this layer, will overwrite the contents of the buffer(s). Expected keys include state_prefix + '_pos_enc_frame_count'.
output_states	A bool. If True, returns the output tensor and output states. Returns just the output tensor otherwise.

Returns
An output tf.Tensor (and optionally the states if output_states=True).

Raises
ValueError	If using 'channels_first' data format.