Operation 与 Tensor

Tensor代表操作的输出结果 ，Operation表示一个操作，

 c = tf.matmul(a, b)

c是Tensor对象，还是Operation对象？

这里c应该是Tensor对象。

有些操作没有返回值，此时该对象就是一个Operation对象。如

optimizer = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.loss)

 optimizer是一个Operation对象，因为minimize函数没有输出值。

附api:

• Class Operation

Defined in tensorflow/python/framework/ops.py.

Represents a graph node that performs computation on tensors.

An Operation is a node in a TensorFlow Graph that takes zero or more Tensor objects as input, and produces zero or more Tensor objects as output. Objects of type Operation are created by calling a Python op constructor (such as tf.matmul) or tf.Graph.create_op.

For example c = tf.matmul(a, b) creates an Operation of type "MatMul" that takes tensors a and b as input, and produces c as output.

After the graph has been launched in a session, an Operation can be executed by passing it to tf.Session.run. op.run() is a shortcut for calling tf.get_default_session().run(op).

Operation 类

表示图中的节点，实现对Tensor的计算。

一个Operation对象是Tensorflow计算图中的一个节点，接收0或多个Tensor对象作输入，输出0或多个Tensor对象。

• Class Tensor

Defined in tensorflow/python/framework/ops.py.

Represents one of the outputs of an Operation..

Operation可以有多个输出，Tensor表示Operation输出中的一个。

A Tensor is a symbolic handle to one of the outputs of an Operation. It does not hold the values of that operation's output, but instead provides a means of computing those values in a TensorFlow tf.Session.

This class has two primary purposes:

1. A Tensor can be passed as an input to another Operation. This builds a dataflow connection between operations, which enables TensorFlow to execute an entire Graph that represents a large, multi-step computation.

2. After the graph has been launched in a session, the value of the Tensor can be computed by passing it to tf.Session.run. t.eval() is a shortcut for calling tf.get_default_session().run(t).

In the following example, c, d, and e are symbolic Tensor objects, whereas result is a numpy array that stores a concrete value:

c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)