tf.contrib.legacy_seq2seq.sequence_loss_by_example(logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None)

tf.contrib.legacy_seq2seq.sequence_loss_by_example(logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None)

Weighted cross-entropy loss for a sequence of logits (per example).

Args:

  • logits: List of 2D Tensors of shape [batch_size x num_decoder_symbols].
  • targets: List of 1D batch-sized int32 Tensors of the same length as logits.
  • weights: List of 1D batch-sized float-Tensors of the same length as logits.
  • average_across_timesteps: If set, divide the returned cost by the total label weight.
  • softmax_loss_function: Function (labels-batch, inputs-batch) -> loss-batch to be used instead of the standard softmax (the default if this is None).
  • name: Optional name for this operation, default: "sequence_loss_by_example".

Returns:

1D batch-sized float Tensor: The log-perplexity for each sequence.

Raises:

  • ValueError: If len(logits) is different from len(targets) or len(weights).

Defined in tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py.