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tfa.seq2seq.LuongAttention

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Class LuongAttention

Implements Luong-style (multiplicative) attention scoring.

Aliases:

This attention has two forms. The first is standard Luong attention, as described in:

Minh-Thang Luong, Hieu Pham, Christopher D. Manning. Effective Approaches to Attention-based Neural Machine Translation. EMNLP 2015.

The second is the scaled form inspired partly by the normalized form of Bahdanau attention.

To enable the second form, construct the object with parameter scale=True.

__init__

View source

__init__(
    units,
    memory=None,
    memory_sequence_length=None,
    scale=False,
    probability_fn='softmax',
    dtype=None,
    name='LuongAttention',
    **kwargs
)

Construct the AttentionMechanism mechanism.

Args:

  • units: The depth of the attention mechanism.
  • memory: The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...].
  • memory_sequence_length: (optional): Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
  • scale: Python boolean. Whether to scale the energy term.
  • probability_fn: (optional) string, the name of function to convert the attention score to probabilities. The default is softmax which is tf.nn.softmax. Other options is hardmax, which is hardmax() within this module. Any other value will result intovalidation error. Default to use softmax.
  • dtype: The data type for the memory layer of the attention mechanism.
  • name: Name to use when creating ops.
  • **kwargs: Dictionary that contains other common arguments for layer creation.

Properties

activity_regularizer

Optional regularizer function for the output of this layer.

alignments_size

dtype

dynamic

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

  • RuntimeError: If called in Eager mode.
  • AttributeError: If no inbound nodes are found.

input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input mask tensor (potentially None) or list of input mask tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns:

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises:

  • AttributeError: if the layer has no defined input_shape.
  • RuntimeError: if called in Eager mode.

input_spec

losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns:

A list of tensors.

memory_initialized

Returns True if this attention mechanism has been initialized with a memory.

metrics

name

Returns the name of this module as passed or determined in the ctor.

NOTE: This is not the same as the self.name_scope.name which includes parent module names.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

non_trainable_weights

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.
  • RuntimeError: if called in Eager mode.

output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output mask tensor (potentially None) or list of output mask tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.

output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns:

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises:

  • AttributeError: if the layer has no defined output shape.
  • RuntimeError: if called in Eager mode.

state_size

submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []

Returns:

A sequence of all submodules.

trainable

trainable_variables

Sequence of variables owned by this module and it's submodules.

Returns:

A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

__call__

View source

__call__(
    inputs,
    **kwargs
)

Preprocess the inputs before calling base_layer.__call__().

Note that there are situation here, one for setup memory, and one with actual query and state. 1. When the memory has not been configured, we just pass all the param to baselayer.call_(), which will then invoke self.call() with proper inputs, which allows this class to setup memory. 2. When the memory has already been setup, the input should contain query and state, and optionally processed memory. If the processed memory is not included in the input, we will have to append it to the inputs and give it to the baselayer.call(). The processed memory is the output of first invocation of self.call_(). If we don't add it here, then from keras perspective, the graph is disconnected since the output from previous call is never used.

Args:

  • inputs: the inputs tensors.
  • **kwargs: dict, other keyeword arguments for the __call__()

build

View source

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments:

  • input_shape: Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

compute_mask

View source

compute_mask(
    inputs,
    mask=None
)

Computes an output mask tensor.

Arguments:

  • inputs: Tensor or list of tensors.
  • mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

compute_output_shape

compute_output_shape(input_shape)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Arguments:

  • input_shape: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

An input shape tuple.

count_params

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

  • ValueError: if the layer isn't yet built (in which case its weights aren't yet defined).

deserialize_inner_layer_from_config

View source

deserialize_inner_layer_from_config(
    cls,
    config,
    custom_objects
)

Helper method that reconstruct the query and memory from the config.

In the get_config() method, the query and memory layer configs are serialized into dict for persistence, this method perform the reverse action to reconstruct the layer from the config.

Args:

  • config: dict, the configs that will be used to reconstruct the object.
  • custom_objects: dict mapping class names (or function names) of custom (non-Keras) objects to class/functions.

Returns:

  • config: dict, the config with layer instance created, which is ready to be used as init parameters.

from_config

View source

@classmethod
from_config(
    cls,
    config,
    custom_objects=None
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Arguments:

  • config: A Python dictionary, typically the output of get_config.

Returns:

A layer instance.

get_config

View source

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

get_input_at

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_input_mask_at

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors.

Returns:

List of loss tensors of the layer that depend on inputs.

get_output_at

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_output_mask_at

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors.

Returns:

List of update ops of the layer that depend on inputs.

get_weights

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

initial_alignments

View source

initial_alignments(
    batch_size,
    dtype
)

Creates the initial alignment values for the AttentionWrapper class.

This is important for AttentionMechanisms that use the previous alignment to calculate the alignment at the next time step (e.g. monotonic attention).

The default behavior is to return a tensor of all zeros.

Args:

  • batch_size: int32 scalar, the batch_size.
  • dtype: The dtype.

Returns:

A dtype tensor shaped [batch_size, alignments_size] (alignments_size is the values' max_time).

initial_state

View source

initial_state(
    batch_size,
    dtype
)

Creates the initial state values for the AttentionWrapper class.

This is important for AttentionMechanisms that use the previous alignment to calculate the alignment at the next time step (e.g. monotonic attention).

The default behavior is to return the same output as initial_alignments.

Args:

  • batch_size: int32 scalar, the batch_size.
  • dtype: The dtype.

Returns:

A structure of all-zero tensors with shapes as described by state_size.

set_weights

set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

Arguments:

  • weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).

Raises:

  • ValueError: If the provided weights list does not match the layer's specifications.

setup_memory

View source

setup_memory(
    memory,
    memory_sequence_length=None,
    memory_mask=None
)

Pre-process the memory before actually query the memory.

This should only be called once at the first invocation of call().

Args:

  • memory: The memory to query; usually the output of an RNN encoder. This tensor should be shaped [batch_size, max_time, ...]. memory_sequence_length (optional): Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
  • memory_mask: (Optional) The boolean tensor with shape [batch_size, max_time]. For any value equal to False, the corresponding value in memory should be ignored.

with_name_scope

with_name_scope(
    cls,
    method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 64]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>

Args:

  • method: The method to wrap.

Returns:

The original method wrapped such that it enters the module's name scope.