tf.contrib.legacy_seq2seq.sequence_loss_by_example

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

tf.contrib.legacy_seq2seq.sequence_loss_by_example(
    logits, targets, weights, average_across_timesteps=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.
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, 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).