metrics.py 文件源码

def char_accuracy(predictions, targets, rej_char, streaming=False):
    """Computes character level accuracy.
    Both predictions and targets should have the same shape
    [batch_size x seq_length].

    Args:
        predictions: predicted characters ids.
        targets: ground truth character ids.
        rej_char: the character id used to mark an 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 character accuracy.
    """
    with tf.variable_scope('CharAccuracy'):
        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())
        weights = tf.to_float(tf.not_equal(targets, const_rej_char))
        correct_chars = tf.to_float(tf.equal(predictions, targets))
        accuracy_per_example = tf.div(tf.reduce_sum(tf.multiply(
            correct_chars, weights), 1),  tf.reduce_sum(weights, 1))
        if streaming:
            return tf.contrib.metrics.streaming_mean(accuracy_per_example)
        else:
            return tf.reduce_mean(accuracy_per_example)