def compute_ler(self, labels_true, labels_pred):
"""Operation for computing LER (Label Error Rate).
Args:
labels_true: A SparseTensor of target labels
labels_pred: A SparseTensor of predicted labels
Returns:
ler_op: operation for computing LER
"""
# Compute LER (normalize by label length)
ler_op = tf.reduce_mean(tf.edit_distance(
labels_pred, labels_true, normalize=True))
# TODO: consider variable lengths
# Add a scalar summary for the snapshot of LER
# with tf.name_scope("ler"):
# self.summaries_train.append(tf.summary.scalar(
# 'ler_train', ler_op))
# self.summaries_dev.append(tf.summary.scalar(
# 'ler_dev', ler_op))
# TODO: feed_dict????????????????
return ler_op
attention_seq2seq.py 文件源码
python
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