tf.contrib.legacy_seq2seq.sequence_loss_by_example(
logits,
targets,
weights,
average_across_timesteps=True,
softmax_loss_function=None,
name=None
)
Defined in tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
.
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, 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).