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 | |
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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 | |
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1D batch-sized float Tensor: The log-perplexity for each sequence. |
Raises | |
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ValueError |
If len(logits) is different from len(targets) or len(weights). |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/legacy_seq2seq/sequence_loss_by_example