tf.contrib.legacy_seq2seq.sequence_loss

Weighted cross-entropy loss for a sequence of logits, batch-collapsed.

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

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.
average_across_batch	If set, divide the returned cost by the batch size.
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.
name	Optional name for this operation, defaults to "sequence_loss".

Returns
A scalar float Tensor: The average log-perplexity per symbol (weighted).

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