How to assign a value to a TensorFlow variable

Question

I am trying to assign a new value to a tensorflow variable in python   import tensorflow as tf import numpy as np  x   tf Variable 0  init   tf initialize all variables   sess   tf InteractiveSession   sess run init   print x eval     x assign 1  print x eval      But the output I get is  0 0   So the value has not changed  What am I missing

User · Answer

First of all you can assign values to variables/constants just by feeding values into them the same way you do it with placeholders. So this is perfectly legal to do:

import tensorflow as tf
x = tf.Variable(0)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print sess.run(x, feed_dict={x: 3})

Regarding your confusion with the tf.assign() operator. In TF nothing is executed before you run it inside of the session. So you always have to do something like this: op_name = tf.some_function_that_create_op(params) and then inside of the session you run sess.run(op_name). Using assign as an example you will do something like this:

import tensorflow as tf
x = tf.Variable(0)
y = tf.assign(x, 1)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print sess.run(x)
    print sess.run(y)
    print sess.run(x)

User · Answer

Here is the complete working example   import numpy as np import tensorflow as tf  w  tf Variable 0  dtype tf float32   good practice to set the type of the variable cost   10   5 w   w w train   tf train GradientDescentOptimizer 0 01  minimize cost   init   tf global variables initializer   session   tf Session   session run init   print session run w    session run train  print session run w     runs one step of gradient descent  for i in range 10000     session run train   print session run w     Note the output will be   0 0 -0 049999997 -2 499994   This means at the very start the Variable was 0  as defined  then after just one step of gradient decent the variable was -0 049999997  and after 10 000 more steps we are reaching -2 499994  based on our cost function    Note  You originally used the Interactive session  Interactive session is useful when multiple different sessions needed to be run in the same script  However  I used the non interactive session for simplicity

User · Answer

So i had a adifferent case where i needed to assign values before running a session  So this was the easiest way to do that   other variable   tf get variable  other variable   dtype tf int32    initializer tf constant  23  42      here i m creating a variable as well as assigning it values at the same time

User · Answer

There is an easier approach    x   tf Variable 0  x   x   1 print x eval

User · Answer

I answered a similar question here   I looked in a lot of places that always created the same problem   Basically  I did not want to assign a value to the weights  but simply change the weights   The short version of the above answer is   tf keras backend set value tf var  numpy weights

User · Answer

You can also assign a new value to a tf Variable without adding an operation to the graph  tf Variable load value  session   This function can also save you adding placeholders when assigning a value from outside the graph and it is useful in case the graph is finalized   import tensorflow as tf x   tf Variable 0  sess   tf Session   sess run tf global variables initializer    print sess run x      Prints 0  x load 1  sess  print sess run x      Prints 1    Update  This is depricated in TF2 as eager execution is default and graphs are no longer exposed in the user-facing API

User · Answer

Use Tensorflow eager execution mode which is latest   import tensorflow as tf tf enable eager execution   my int variable   tf get variable  my int variable    1  2  3   print my int variable

User · Answer

In TF1  the statement x assign 1  does not actually assign the value 1 to x  but rather creates a tf Operation that you have to explicitly run to update the variable   A call to Operation run   or Session run   can be used to run the operation   assign op   x assign 1  sess run assign op     or  assign op op run    print x eval         gt  1      In fact  it returns a tf Tensor  corresponding to the updated value of the variable  to make it easier to chain assignments    However  in TF2 x assign 1  will now assign the value eagerly   x assign 1  print x numpy         gt  1

User · Answer

Also  it has to be noted that if you re using your tensor assign    then the tf global variables initializer need not be called explicitly since the assign operation does it for you in the background   Example   In  212   w   tf Variable 12  In  213   w new   w assign 34   In  214   with tf Session   as sess                sess run w new                print w new eval       output 34    However  this will not initialize all variables  but it will only initialize the variable on which assign was executed on

[python] How to assign a value to a TensorFlow variable?

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