除了其他人的答案，他们在Tensoflow网站上的MNIST教程中也解释得很好:

We describe these interacting operations by manipulating symbolic variables. Let's create one: x = tf.placeholder(tf.float32, [None, 784]), x isn't a specific value. It's a placeholder, a value that we'll input when we ask TensorFlow to run a computation. We want to be able to input any number of MNIST images, each flattened into a 784-dimensional vector. We represent this as a 2-D tensor of floating-point numbers, with a shape [None, 784]. (Here None means that a dimension can be of any length.) We also need the weights and biases for our model. We could imagine treating these like additional inputs, but TensorFlow has an even better way to handle it: Variable. A Variable is a modifiable tensor that lives in TensorFlow's graph of interacting operations. It can be used and even modified by the computation. For machine learning applications, one generally has the model parameters be Variables. W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) We create these Variables by giving tf.Variable the initial value of the Variable: in this case, we initialize both W and b as tensors full of zeros. Since we are going to learn W and b, it doesn't matter very much what they initially are.

占位符:

A placeholder is simply a variable that we will assign data to at a later date. It allows us to create our operations and build our computation graph, without needing the data. In TensorFlow terminology, we then feed data into the graph through these placeholders. Initial values are not required but can have default values with tf.placeholder_with_default) We have to provide value at runtime like : a = tf.placeholder(tf.int16) // initialize placeholder value b = tf.placeholder(tf.int16) // initialize placeholder value use it using session like : sess.run(add, feed_dict={a: 2, b: 3}) // this value we have to assign at runtime

变量:

TensorFlow变量是表示共享的最佳方式， 由程序操纵的持久状态。 变量是通过tf操作的。变量类。一个特遣部队。变量 表示一个张量，其值可以通过对其运行操作来改变。

例如:tf。变量("欢迎来到tensorflow!! ")

Tensorflow使用三种类型的容器来存储/执行过程

Constants:Constants保存典型数据。 变量:数据值将被改变，相应的函数，如cost_function.. 占位符:训练/测试数据将被传递到图表中。

区别在于tf。变量，在声明时必须提供初始值。特遣部队。占位符，你不必提供初始值，你可以在运行时在Session.run中使用feed_dict参数指定它

博士TL;

变量

为了学习参数 价值观可以从培训中获得 初始值是必需的(通常是随机的)

占位符

为数据分配存储(例如在馈送期间用于图像像素数据) 初始值不是必需的(但可以设置，参见tf.placeholder_with_default)

想象一个计算图。在这样的图中，我们需要一个输入节点来将数据传递到图中，这些节点应该在tensorflow中定义为占位符。

不要把Python想象成一个通用的程序。你可以写一个Python程序，做所有那些在其他答案中通过变量解释的事情，但对于张量流中的计算图，为了将数据输入到图中，你需要将这些点定义为占位符。