Deep Learning with TensorFlow

Variables

Variables are TensorFlow objects that hold and update parameters. A variable must be initialized; also you can save and restore it to analyze your code.

Variables are created by the tf.Variable() statement.

In the following example, we want to count the numbers from 1 to 10:

import tensorflow as tf

We create a variable that will be initialized to the scalar value 0:

value = tf.Variable(0,name="value")

The assign() and add() operators are just nodes of the computation graph so they do not execute the assignment until the session is run:

one = tf.constant(1) new_value = tf.add(value,one) update_value=tf.assign(value,new_value) initialize_var = tf.global_variables_initializer()

We can instantiate the computation graph:

 with ...