Variable tensors are used when the values require updating within a session. It is the type of tensor that would be used for the weights matrix when creating neural networks, since these values will be updated as the model is being trained.

Declaring a variable tensor can be done using the `tf.Variable()`

or `tf.get_variable()`

function. It is recommended to use `tf.get_variable`

, as it offers more flexibility eg:

# Declare a 2 by 3 tensor populated by ones a = tf.Variable(tf.ones([2,3], dtype=tf.float32)) a = tf.get_variable('a', shape=[2, 3], initializer=tf.constant_initializer(1))

Something to note is that declaring a variable tensor does not automatically initialize the values. The values need to be intialized explicitly when starting a session using one of the following:

`tf.global_variables_initializer().run()`

`session.run(tf.global_variables_initializer())`

The following example shows the full process of declaring and initializing a variable tensor.

# Build a graph graph = tf.Graph() with graph.as_default(): a = tf.get_variable('a', shape=[2,3], initializer=tf.constant_initializer(1), dtype=tf.float32)) # Create a variable tensor # Create a session, and run the graph with tf.Session(graph=graph) as session: tf.global_variables_initializer().run() # Initialize values of all variable tensors output_a = session.run(a) # Return the value of the variable tensor print(output_a) # Print this value

Which prints out the following:

```
[[ 1. 1. 1.]
[ 1. 1. 1.]]
```