DataEmbedding#

class tfts.layers.embed_layer.DataEmbedding(*args, **kwargs)[source]#

Bases: Layer

Data Embedding layer that combines value embeddings with optional positional embeddings.

This layer first embeds the input data using a TokenEmbedding, then optionally adds positional information using one of several positional embedding techniques, and finally applies dropout.

Parameters:

  • embed_size (int) – Embedding size for tokens.

  • positional_type (str, optional) –

    Type of positional embedding to use. Options:

    • ”positional encoding”: Uses sinusoidal positional encoding

    • ”positional embedding”: Uses learned positional embeddings

    • ”relative encoding”: Uses relative position embeddings

    • None: No positional embedding is applied

    Defaults to None.

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 DataEmbedding layer with embedding size of 256 and positional encoding embedding_layer = DataEmbedding(

embed_size=256, positional_type=”positional encoding”

)

# 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 DataEmbedding layer.

Parameters:

  • embed_size (int) – Embedding size for tokens.

  • positional_type (str, optional) – Type of positional embedding to use. Options: “positional encoding”, “positional embedding”, “relative encoding”, or None. Defaults to None.

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_from_config(config)

Builds the layer's states with the supplied config dict.

call(x)

Forward pass of the layer.

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.
call(x: Tensor) → Tensor[source]#

Forward pass of the layer.

Parameters:

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

Returns:

Output tensor of shape (batch_size, seq_length, embed_size).

Return type:

tf.Tensor

get_config() → Dict[str, Any][source]#

Returns the config of the object.

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