teras.layers.SAINTEmbedding

teras.layers.SAINTEmbedding#

class teras.layers.SAINTEmbedding(embedding_dim, cardinalities, **kwargs)[source]#

SAINTEmbedding layer as proposed in the paper, “SAINT: Improved Neural Networks for Tabular Data”.

Reference(s):

https://arxiv.org/abs/2106.01342

Parameters:

  • embedding_dim (int) – int, dimensionality of the embeddings

  • cardinalities (list) – list, a list cardinalities of all the features in the dataset in the same order as the features’ occurrence. For numerical features, use the value 0 as indicator at the corresponding index. You can use the compute_cardinalities function from teras.utils package for this purpose.

Shapes:

Input Shape: (batch_size, input_dim) Output Shape: (batch_size, input_dim, embedding_dim)

__init__(embedding_dim, cardinalities, **kwargs)[source]#

Methods

__init__(embedding_dim, cardinalities, **kwargs)

add_loss(loss)

Can be called inside of the call() method to add a scalar loss.

add_metric()

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(inputs)

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)

quantized_call(*args, **kwargs)

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.
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