def sequence_accuracy(predictions, targets, rej_char, streaming=False):
"""Computes sequence level accuracy.
Both input tensors should have the same shape: [batch_size x seq_length].
Args:
predictions: predicted character classes.
targets: ground truth character classes.
rej_char: the character id used to mark empty element (end of sequence).
streaming: if True, uses the streaming mean from the slim.metric module.
Returns:
a update_ops for execution and value tensor whose value on evaluation
returns the total sequence accuracy.
"""
with tf.variable_scope('SequenceAccuracy'):
predictions.get_shape().assert_is_compatible_with(targets.get_shape())
targets = tf.to_int32(targets)
const_rej_char = tf.constant(
rej_char, shape=targets.get_shape(), dtype=tf.int32)
include_mask = tf.not_equal(targets, const_rej_char)
include_predictions = tf.to_int32(
tf.where(include_mask, predictions, tf.zeros_like(predictions) + rej_char))
correct_chars = tf.to_float(tf.equal(include_predictions, targets))
correct_chars_counts = tf.cast(
tf.reduce_sum(correct_chars, reduction_indices=[1]), dtype=tf.int32)
target_length = targets.get_shape().dims[1].value
target_chars_counts = tf.constant(
target_length, shape=correct_chars_counts.get_shape())
accuracy_per_example = tf.to_float(
tf.equal(correct_chars_counts, target_chars_counts))
if streaming:
return tf.contrib.metrics.streaming_mean(accuracy_per_example)
else:
return tf.reduce_mean(accuracy_per_example)
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