Ops for building neural network seq2seq decoders and losses.
See the Contrib Seq2seq guide.
class AttentionWrapper: Wraps another
RNNCell with attention.
namedtuple storing the state of a
class BahdanauAttention: Implements Bahdanau-style (additive) attention.
class BahdanauMonotonicAttention: Monotonic attention mechanism with Bahadanau-style energy function.
class BasicDecoder: Basic sampling decoder.
class BeamSearchDecoder: BeamSearch sampling decoder.
class CustomHelper: Base abstract class that allows the user to customize sampling.
class Decoder: An RNN Decoder abstract interface object.
class FinalBeamSearchDecoderOutput: Final outputs returned by the beam search after all decoding is finished.
class GreedyEmbeddingHelper: A helper for use during inference.
class Helper: Interface for implementing sampling in seq2seq decoders.
class InferenceHelper: A helper to use during inference with a custom sampling function.
class LuongAttention: Implements Luong-style (multiplicative) attention scoring.
class LuongMonotonicAttention: Monotonic attention mechanism with Luong-style energy function.
class SampleEmbeddingHelper: A helper for use during inference.
class ScheduledEmbeddingTrainingHelper: A training helper that adds scheduled sampling.
class ScheduledOutputTrainingHelper: A training helper that adds scheduled sampling directly to outputs.
class TrainingHelper: A helper for use during training. Only reads inputs.
dynamic_decode(...): Perform dynamic decoding with
gather_tree(...): Calculates the full beams from the per-step ids and parent beam ids.
hardmax(...): Returns batched one-hot vectors.
monotonic_attention(...): Compute monotonic attention distribution from choosing probabilities.
safe_cumprod(...): Computes cumprod of x in logspace using cumsum to avoid underflow.
sequence_loss(...): Weighted cross-entropy loss for a sequence of logits.
tile_batch(...): Tile the batch dimension of a (possibly nested structure of) tensor(s) t.