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Monotonic attention mechanism with Bahadanau-style energy function.
This type of attention enforces a monotonic constraint on the attention distributions; that is once the model attends to a given point in the memory it can't attend to any prior points at subsequence output timesteps. It achieves this by using the _monotonic_probability_fn instead of softmax to construct its attention distributions. Since the attention scores are passed through a sigmoid, a learnable scalar bias parameter is applied after the score function and before the sigmoid. Otherwise, it is equivalent to BahdanauAttention. This approach is proposed in
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck, "Online and Linear-Time Attention by Enforcing Monotonic Alignments." ICML 2017. https://arxiv.org/abs/1704.00784
__init__( num_units, memory, memory_sequence_length=None, normalize=False, score_mask_value=None, sigmoid_noise=0.0, sigmoid_noise_seed=None, score_bias_init=0.0, mode='parallel', dtype=None, name='BahdanauMonotonicAttention' )
Construct the Attention mechanism.
num_units: The depth of the query 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.
normalize: Python boolean. Whether to normalize the energy term.
score_mask_value: (optional): The mask value for score before passing into
probability_fn. The default is -inf. Only used if
memory_sequence_lengthis not None.
sigmoid_noise: Standard deviation of pre-sigmoid noise. See the docstring for
_monotonic_probability_fnfor more information.
sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
score_bias_init: Initial value for score bias scalar. It's recommended to initialize this to a negative value when the length of the memory is large.
mode: How to compute the attention distribution. Must be one of 'recursive', 'parallel', or 'hard'. See the docstring for
tf.contrib.seq2seq.monotonic_attentionfor more information.
dtype: The data type for the query and memory layers of the attention mechanism.
name: Name to use when creating ops.
__call__( query, state )
Score the query based on the keys and values.
query: Tensor of dtype matching
state: Tensor of dtype matching
alignments: Tensor of dtype matching
initial_alignments( batch_size, dtype )
Creates the initial alignment values for the monotonic attentions.
Initializes to dirac distributions, i.e. [1, 0, 0, ...memory length..., 0] for all entries in the batch.
int32scalar, the batch_size.
dtype tensor shaped
alignments_size is the values'
initial_state( batch_size, dtype )
Creates the initial state values for the
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.
int32scalar, the batch_size.
A structure of all-zero tensors with shapes as described by