我无法理解Tensorflow系统

Question

I cannot understand Tensorflow system. First,I wrote

#coding:UTF-8

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

const1 = tf.constant(2)
const2 = tf.constant(3)
add_op = tf.add(const1,const2)

with tf.Session() as sess:
    result = sess.run(add_op)
    print(result)

and it print out 5. Second,I wrote

#coding:UTF-8

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

const1 = tf.constant(2)
const2 = tf.constant(3)
add_op = tf.add(const1,const2)
print(add_op)

and it print out Tensor("Add:0", shape=(), dtype=int32). I cannot understand this system. I use Python and other languages, so I think tf.add() method is add method.However,in the case of Tensorflow,it seems different. why is this part

with tf.Session() as sess:
    result = sess.run(add_op)
    print(result)

necessary? What functions does this part have?

Answer 1

I would suggest to read the official Getting Started with TensorFlow guide of TensorFlow to get to know the core concepts of the library, such as the one which seems to be the problem here:

Every TensorFlow program consists of two parts:

1. Building the computational graph.
2. Running the computational graph.

Now, what is a "computational graph"? In TensorFlow, you specify a series of operations which are executed on your input. This series of operations is your "computational graph". To understand that, lets look at some examples:

• Simple addition: let's look at your example, your code is

   const1 = tf.constant(2) const2 = tf.constant(3) add_op = tf.add(const1,const2) 

  This creates two constant nodes in the graph, and creates a second node which adds them. Graphically, this looks like:

  

• To make it a little bit more complex, lets say you have an input x and want to add a constant 3 to it. Then your code would be:

   const1 = tf.constant(2) x = tf.placeholder(tf.float32) add_op = tf.add(const1,x) 

  and your graph is

  

In both examples, this was the first part of the program. So far, we only defined how our computational graph should look, ie what inputs we have, what outputs, and all calculations needed.

But: no calculations have been done so far! In the second example, you don't even know what your input x is - only that it will be a float32 . If you have a GPU, you'll notice that TensorFlow hasn't even touched the GPU yet. Even if you have a huge neural network with millions of training images, this step runs in milliseconds, as no "real" work has to be done.

Now comes part two: running the graph we defined above. Here's where the work happens! We fire up TensorFlow by creating a tf.Session , and then we can run anything by calling sess.run() .

with tf.Session() as sess:
    result = sess.run(add_op)
    print(result)

In the second example, we now have to tell TensorFlow what our value x should be:

with tf.Session() as sess:
    result = sess.run(add_op, {x: 5.0})
    print(result)

tl;dr: every TensorFlow program has two parts: 1. building a computational graph, and 2. running this graph. With tf.add you only define the graph, but no addition is performed yet. To run this graph, use sess.run() as in your first piece code.