def sequence_edit_distance(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Average edit distance, ignoring padding 0s.
The score returned is the edit distance divided by the total length of
reference truth and the weight returned is the total length of the truth.
Args:
predictions: Tensor of shape [`batch_size`, `length`, 1, `num_classes`] and
type tf.float32 representing the logits, 0-padded.
labels: Tensor of shape [`batch_size`, `length`, 1, 1] and type tf.int32
representing the labels of same length as logits and 0-padded.
weights_fn: ignored. The weights returned are the total length of the ground
truth labels, excluding 0-paddings.
Returns:
(edit distance / reference length, reference length)
Raises:
ValueError: if weights_fn is not common_layers.weights_nonzero.
"""
if weights_fn is not common_layers.weights_nonzero:
raise ValueError("Only weights_nonzero can be used for this metric.")
with tf.variable_scope("edit_distance", values=[predictions, labels]):
# Transform logits into sequence classes by taking max at every step.
predictions = tf.to_int32(
tf.squeeze(tf.argmax(predictions, axis=-1), axis=(2, 3)))
nonzero_idx = tf.where(tf.not_equal(predictions, 0))
sparse_outputs = tf.SparseTensor(nonzero_idx,
tf.gather_nd(predictions, nonzero_idx),
tf.shape(predictions, out_type=tf.int64))
labels = tf.squeeze(labels, axis=(2, 3))
nonzero_idx = tf.where(tf.not_equal(labels, 0))
label_sparse_outputs = tf.SparseTensor(nonzero_idx,
tf.gather_nd(labels, nonzero_idx),
tf.shape(labels, out_type=tf.int64))
distance = tf.reduce_sum(
tf.edit_distance(sparse_outputs, label_sparse_outputs, normalize=False))
reference_length = tf.to_float(common_layers.shape_list(nonzero_idx)[0])
return distance / reference_length, reference_length
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