tf.contrib.legacy_seq2seq.sequence_loss_by_example

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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
)

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".

1D batch-sized float Tensor: The log-perplexity for each sequence.

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