张量流中tf.losses.log_loss（标签，预测）的输入是什么？

Question

任何人都可以举一些例子吗？ 官方文件中没有指南。 我想知道什么类型的标签和预测是？ 谢谢。

Answer 1

输入是张量，第一个包含数据点的正确预测，第二个是您的模型为数据点提供的实际预测。这些张量既可以代表一个数据点，也可以代表批次。它们必须具有相同的形状。

例子：

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)