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, core_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 (inputs-batch, labels-batch) -> loss-batch to be used instead of the standard softmax (the default if this is None).
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_inputsut, targets, or weights is smaller than the largest (last) bucket.