How to assign a value to a TensorFlow variable?

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?

Solution 1:

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())
# ==> 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())
# ==> 1

Solution 2:

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.

Solution 3:

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)