tf.contrib.legacy_seq2seq.sequence_loss_by_example

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).