tf.contrib.legacy_seq2seq.model_with_buckets( encoder_inputs, decoder_inputs, targets, weights, buckets, seq2seq, softmax_loss_function=None, per_example_loss=False, name=None )
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.