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).
|