mnist.py 文件源码

def loss(logits, labels):
    """Calculates the loss from the logits and the labels."""
    labels = tf.to_int64(labels)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits, labels, name='xentropy')
    loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
    return loss