TokenEmbedding

class tfts.layers.embed_layer.TokenEmbedding(*args, **kwargs)

Bases: Layer

A layer that performs token embedding, equivalent to a dense layer for time series data.

This layer transforms input features into an embedding space of specified dimension. It applies a linear transformation to the last dimension of the input tensor.

Parameters:

embed_size (int) – The size of the embedding output dimension.

Input shape:

• 3D tensor with shape (batch_size, time_steps, input_dim)

Output shape:

• 3D tensor with shape (batch_size, time_steps, embed_size)

Example

```python # Create a TokenEmbedding layer with embedding size of 256 embedding_layer = TokenEmbedding(embed_size=256)

# Apply to input with shape (batch_size, sequence_length, features) input_tensor = tf.random.normal((32, 100, 10)) output_tensor = embedding_layer(input_tensor) # Shape: (32, 100, 256) ```

Initialize the TokenEmbedding layer.

Parameters:

embed_size (int) – The size of the embedding output dimension.

Inherited-members:

Methods

add_loss(loss)	Can be called inside of the call() method to add a scalar loss.
add_metric(*args, **kwargs)
add_variable(shape, initializer[, dtype, ...])	Add a weight variable to the layer.
add_weight([shape, initializer, dtype, ...])	Add a weight variable to the layer.
build(input_shape)	Build the layer's weights based on input shape.
build_from_config(config)	Builds the layer's states with the supplied config dict.
call(x)	Performs the token embedding transformation.
compute_mask(inputs, previous_mask)
compute_output_shape(input_shape)
compute_output_spec(*args, **kwargs)
count_params()	Count the total number of scalars composing the weights.
from_config(config)	Creates an operation from its config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_config()	Returns the config of the object.
get_weights()	Return the values of layer.weights as a list of NumPy arrays.
load_own_variables(store)	Loads the state of the layer.
quantize(mode[, type_check])
quantized_build(input_shape, mode)
quantized_call(*args, **kwargs)
rematerialized_call(layer_call, *args, **kwargs)	Enable rematerialization dynamically for layer's call method.
save_own_variables(store)	Saves the state of the layer.
set_weights(weights)	Sets the values of layer.weights from a list of NumPy arrays.
stateless_call(trainable_variables, ...[, ...])	Call the layer without any side effects.
symbolic_call(*args, **kwargs)

Attributes

compute_dtype	The dtype of the computations performed by the layer.
dtype	Alias of layer.variable_dtype.
dtype_policy
input	Retrieves the input tensor(s) of a symbolic operation.
input_dtype	The dtype layer inputs should be converted to.
input_spec
losses	List of scalar losses from add_loss, regularizers and sublayers.
metrics	List of all metrics.
metrics_variables	List of all metric variables.
non_trainable_variables	List of all non-trainable layer state.
non_trainable_weights	List of all non-trainable weight variables of the layer.
output	Retrieves the output tensor(s) of a layer.
path	The path of the layer.
quantization_mode	The quantization mode of this layer, None if not quantized.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable	Settable boolean, whether this layer should be trainable or not.
trainable_variables	List of all trainable layer state.
trainable_weights	List of all trainable weight variables of the layer.
variable_dtype	The dtype of the state (weights) of the layer.
variables	List of all layer state, including random seeds.
weights	List of all weight variables of the layer.

build(input_shape: Tuple[int | None, ...]) → None

Build the layer’s weights based on input shape.

Parameters:

input_shape (Tuple[Optional[int], ...]) – The input shape to the layer.

call(x: Tensor) → Tensor

Performs the token embedding transformation.

Parameters:

x (tf.Tensor) – Input tensor of shape (batch_size, time_steps, input_dim).

Returns:

Embedded tensor of shape (batch_size, time_steps, embed_size).

Return type:

tf.Tensor

classmethod from_config(config)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

```python if “dtype” in config and isinstance(config[“dtype”], dict):

policy = dtype_policies.deserialize(config[“dtype”])

```

Parameters:

config – A Python dictionary, typically the output of get_config.

Returns:

An operation instance.

get_config() → Dict[str, Any]

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.