评估 Tensor 对象的实际值的最简单的^[A] 方法是传递它到 Session.run() 方法，或者当你有一个默认会话时调用 Tensor.eval()(即在 中使用 tf.Session(): 块，或见下文).通常^[B]，如果不在会话中运行一些代码，就无法打印张量的值.

The easiest^[A] way to evaluate the actual value of a Tensor object is to pass it to the Session.run() method, or call Tensor.eval() when you have a default session (i.e. in a with tf.Session(): block, or see below). In general^[B], you cannot print the value of a tensor without running some code in a session.

如果您正在试验编程模型，并且想要一种简单的方法来评估张量，tf.InteractiveSession 允许您在程序开始时打开一个会话，然后将该会话用于所有 Tensor.eval()(和 Operation.run())调用.这在交互式设置中会更容易，例如 shell 或 IPython 笔记本，因为到处传递 Session 对象很乏味.例如，以下内容适用于 Jupyter 笔记本:

If you are experimenting with the programming model, and want an easy way to evaluate tensors, the tf.InteractiveSession lets you open a session at the start of your program, and then use that session for all Tensor.eval() (and Operation.run()) calls. This can be easier in an interactive setting, such as the shell or an IPython notebook, when it's tedious to pass around a Session object everywhere. For example, the following works in a Jupyter notebook:

with tf.Session() as sess:  print(product.eval()) 

对于这么小的表达式来说，这可能看起来很愚蠢，但 Tensorflow 1.x 中的一个关键思想是延迟执行:构建一个大而复杂的表达式非常便宜，而且当你想要的时候为了对其进行评估，后端(您通过 Session 连接到该后端)能够更有效地安排其执行(例如并行执行独立部分和使用 GPU).

This might seem silly for such a small expression, but one of the key ideas in Tensorflow 1.x is deferred execution: it's very cheap to build a large and complex expression, and when you want to evaluate it, the back-end (to which you connect with a Session) is able to schedule its execution more efficiently (e.g. executing independent parts in parallel and using GPUs).

[A]:要打印张量的值而不将其返回到 Python 程序，您可以使用 tf.print() 运算符，如 Andrzej 在另一个答案中建议.根据官方文档:

[A]: To print the value of a tensor without returning it to your Python program, you can use the tf.print() operator, as Andrzej suggests in another answer. According to the official documentation:

为了保证operator运行，用户需要将生成的op传递给tf.compat.v1.Session的run方法，或者使用op作为执行ops的控制依赖使用 tf.compat.v1.control_dependencies([print_op]) 指定，打印到标准输出.

To make sure the operator runs, users need to pass the produced op to tf.compat.v1.Session's run method, or to use the op as a control dependency for executed ops by specifying with tf.compat.v1.control_dependencies([print_op]), which is printed to standard output.

还要注意:

在 Jupyter 笔记本和 colab 中，tf.print 打印到笔记本单元输出.它不会写入笔记本内核的控制台日志.

In Jupyter notebooks and colabs, tf.print prints to the notebook cell outputs. It will not write to the notebook kernel's console logs.

[B]:您可能能够使用tf.get_static_value() 函数用于获取给定张量的常量值(如果其值可有效计算).

[B]: You might be able to use the tf.get_static_value() function to get the constant value of the given tensor if its value is efficiently calculable.