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Defined in tensorflow/contrib/legacy_seq2seq/python/ops/

Create a sequence-to-sequence model with support for bucketing.

The seq2seq argument is a function that defines a sequence-to-sequence model, e.g., seq2seq = lambda x, y: basic_rnn_seq2seq( x, y, rnn_cell.GRUCell(24))


  • encoder_inputs: A list of Tensors to feed the encoder; first seq2seq input.
  • decoder_inputs: A list of Tensors to feed the decoder; second seq2seq input.
  • targets: A list of 1D batch-sized int32 Tensors (desired output sequence).
  • weights: List of 1D batch-sized float-Tensors to weight the targets.
  • buckets: A list of pairs of (input size, output size) for each bucket.
  • seq2seq: A sequence-to-sequence model function; it takes 2 input that agree with encoder_inputs and decoder_inputs, and returns a pair consisting of outputs and states (as, e.g., basic_rnn_seq2seq).
  • softmax_loss_function: Function (labels, logits) -> loss-batch to be used instead of the standard softmax (the default if this is None). Note that to avoid confusion, it is required for the function to accept named arguments.
  • per_example_loss: Boolean. If set, the returned loss will be a batch-sized tensor of losses for each sequence in the batch. If unset, it will be a scalar with the averaged loss from all examples.
  • name: Optional name for this operation, defaults to "model_with_buckets".


A tuple of the form (outputs, losses), where: * outputs: The outputs for each bucket. Its j'th element consists of a list of 2D Tensors. The shape of output tensors can be either [batch_size x output_size] or [batch_size x num_decoder_symbols] depending on the seq2seq model used. * losses: List of scalar Tensors, representing losses for each bucket, or, if per_example_loss is set, a list of 1D batch-sized float Tensors.


  • ValueError: If length of encoder_inputs, targets, or weights is smaller than the largest (last) bucket.