def padded_accuracy(logits, labels):
    'Percentage of times that predictions matches labels on non-0s.'
    with tf.variable_scope('padded_accuracy', values=[logits, labels]):
        (logits, labels) = _pad_tensors_to_same_length(logits, labels)
        weights = tf.to_float(tf.not_equal(labels, 0))
        outputs = tf.to_int32(tf.argmax(logits, axis=(- 1)))
        padded_labels = tf.to_int32(labels)
        return (tf.to_float(tf.equal(outputs, padded_labels)), weights)
Percentage of times that predictions matches labels on non-0s.
def padded_accuracy(logits, labels):
    
    with tf.variable_scope('padded_accuracy', values=[logits, labels]):
        (logits, labels) = _pad_tensors_to_same_length(logits, labels)
        weights = tf.to_float(tf.not_equal(labels, 0))
        outputs = tf.to_int32(tf.argmax(logits, axis=(- 1)))
        padded_labels = tf.to_int32(labels)
        return (tf.to_float(tf.equal(outputs, padded_labels)), weights)