Module: tf.contrib.seq2seq

Defined in tensorflow/contrib/seq2seq/__init__.py.

Ops for building neural network seq2seq decoders and losses.

See the Contrib Seq2seq guide.

Classes

class AttentionMechanism

class AttentionWrapper: Wraps another RNNCell with attention.

class AttentionWrapperState: namedtuple storing the state of a AttentionWrapper.

class BahdanauAttention: Implements Bahdanau-style (additive) attention.

class BahdanauMonotonicAttention: Monotonic attention mechanism with Bahadanau-style energy function.

class BasicDecoder: Basic sampling decoder.

class BasicDecoderOutput

class BeamSearchDecoder: BeamSearch sampling decoder.

class BeamSearchDecoderOutput

class BeamSearchDecoderState

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.

Functions

dynamic_decode(...): Perform dynamic decoding with decoder.

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.