TensorFlow - show all variables in session

问题

I have played a little

import tensorflow as tf
x = tf.Variable([1.0, 2.0])
initializer = tf.global_variables_initializer()
session.run(initializer)
x
<tf.Variable 'Variable:0' shape=(2,) dtype=float32_ref>
y = 2 * x
y
<tf.Tensor 'mul:0' shape=(2,) dtype=float32>
z = y + 1
z
<tf.Tensor 'add:0' shape=(2,) dtype=float32>
v = session.run(x)
sess.run(initializer)
v = sess.run(x) 
print (v)
[ 1.  2.]
v1 = sess.run(z)
print (v1)
[ 3.  5.]
v = sess.run(x)

I have 3 variables x,y,z.Is it possible to show all the variables defined with one command from prompt? If I try what Jonas suggested

new = tf.trainable_variables()
print (new)
[<tf.Variable 'Variable:0' shape=(2,) dtype=float32_ref>]

回答1:

tf.trainable_variables() prints out all the trainable variables in your graph, which in your case, is only x. When you're doing y = 2 * x, this is actually implicitly defining a constant value mul/x, and taking in the original variable as a Variable/read

If you run the following code:

x = tf.Variable(1)
y = 2 * x
z = y + 1
for v in tf.get_default_graph().as_graph_def().node:
  print v.name

You will get the following output:

Variable/initial_value
Variable
Variable/Assign
Variable/read
mul/x
mul
add/y
add

These are all the nodes in your graph. You can use this to filter out all the relevant information that you need. Specific to your case, I wouldn't call y and z variables.

Note that this is getting all the information from a graph and not a session. If you'd like to get it from a particular session, you'd need to get the relevant session and call sess.graph.

As a last note, the above example used v.name, but each graph node actually has more attributes, such as name, op, input, device, attr. Refer to the API for more information.

回答2:

Maybe I'm misunderstanding the question, but what's wrong with this?

print(sess.run([x, y, z]))

来源：https://stackoverflow.com/questions/45007823/tensorflow-show-all-variables-in-session

标签

python

tensorflow