a3_entity_network.py 文件源码

def smoothing_cross_entropy(self,logits, labels, vocab_size, confidence=0.9): #confidence = 1.0 - label_smoothing. where label_smooth=0.1. from http://github.com/tensorflow/tensor2tensor
        """Cross entropy with label smoothing to limit over-confidence."""
        with tf.name_scope("smoothing_cross_entropy", [logits, labels]):
            # Low confidence is given to all non-true labels, uniformly.
            low_confidence = (1.0 - confidence) / tf.to_float(vocab_size - 1)
            # Normalizing constant is the best cross-entropy value with soft targets.
            # We subtract it just for readability, makes no difference on learning.
            normalizing = -(confidence * tf.log(confidence) + tf.to_float(vocab_size - 1) * low_confidence * tf.log(low_confidence + 1e-20))
            # Soft targets.
            soft_targets = tf.one_hot(
                tf.cast(labels, tf.int32),
                depth=vocab_size,
                on_value=confidence,
                off_value=low_confidence)
            xentropy = tf.nn.softmax_cross_entropy_with_logits(
                logits=logits, labels=soft_targets)
        return xentropy - normalizing