tensorflow中tf.loss.log_loss（标签、预测）的输入是什么？

谁能举一些例子？ 官方文件中没有指南。 我想知道什么类型的标签和预测？

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谢谢。

输入是张量，第一个包含对数据点的正确预测，第二个是模型对数据点的实际预测。这些张量可以表示单个数据点（如示例中所示），也可以表示批次。它们必须有相同的形状

示例：

import tensorflow as tf

""" Minimal example """
with tf.Session() as sess:
    loss = tf.losses.log_loss(tf.Variable([0., 1., 0.]), tf.Variable([0.1, 0.8, 0.1]))
    sess.run(tf.global_variables_initializer())
    result = sess.run(loss)
    print('Minimal example loss: %f' % result)

tf.reset_default_graph()

""" More realistic example """
with tf.Session() as sess:
    # Create placeholders for inputs
    X_placeholder = tf.placeholder(tf.float32, [3])
    y_placeholder = tf.placeholder(tf.float32, [3])

    # Set up the model structure, resulting in a set of predictions
    predictions = tf.multiply(X_placeholder, 2.)

    # Compute the loss of the calculated predictions
    loss = tf.losses.log_loss(y_placeholder, predictions)

    # Run the loss tensor with values for the placeholders
    result = sess.run(loss, feed_dict={X_placeholder: [0.5, 0.5, 0.5], y_placeholder: [0., 1., 0.]})
    print('Realistic example loss: %f' % result)

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预测是否涉及logits或softmax概率？如果是，那又有什么关系呢？