In this tutorial, we describe how to define and initialize variables in TensorFlow. It is a crucial step as without having parameters, training, updating, saving, restoring and any other operations cannot be performed.

## Introduction

`variables`

`define`

`initialize`

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## Creating variables

For a variable generation, the class of tf.Variable() will be used. When we define a variable, we basically pass a tensor and its value to the graph. Basically, the following will happen:

- A
`variable`

tensor that holds a value will be pass to the graph. - By using tf.assign, an initializer set initial variable value.

Some arbitrary variables can be defined as follows:

import tensorflow as tf import xlrd import matplotlib.pyplot as plt import os from sklearn.utils import check_random_state # Generating artificial data. n = 50 XX = np.arange(n) rs = check_random_state(0) YY = rs.randint(-20, 20, size=(n,)) + 2.0 * XX data = np.stack([XX,YY], axis=1) ####################### ## Defining flags ##### ####################### tf.app.flags.DEFINE_integer('num_epochs', 50, 'The number of epochs for training the model. Default=50') # Store all elemnts in FLAG structure! FLAGS = tf.app.flags.FLAGS

In the above script, **line 15** gets the list of all defined variables from the defined graph. The “name” key, define a specific name for each variable on the graph

## Initialization

`Initializers`

of the variables must be run before all other operations in the model. For an analogy, we can consider the starter of the car. Instead of running an initializer, variables can be `restored`

too from saved models such as a checkpoint file. Variables can be initialized globally, specifically, or from other variables. We investigate different choices in the subsequent sections.

### Initializing Specific Variables

By using tf.variables_initializer, we can explicitly command the TensorFlow to only initialize

# "variable_list_custom" is the list of variables that we want to initialize. variable_list_custom = [weights, custom_variable] # The initializer init_custom_op = tf.variables_initializer(var_list=all_variables_list)

Noted that custom initialization does not mean that we don’t need to initialize other variables! All variables that some operations will be done upon them over the graph, must be initialized or restored from saved variables. This only let us realize how we can initialize specific variables by hand.

### Golobal variable initialization

All variables can be initialized at once using the tf.global_variables_initializer(). This op must be run after the model being fully constructed. The script is as below:

# Add an op to initialize the variables. init_all_op = tf.global_variables_initializer() # Method-2 init_all_op = tf.variables_initializer(var_list=all_variables_list)

Both the above methods are identical. We only provide the second one to demonstrate that the `tf.global_variables_initializer()`

is nothing but `tf.variables_initializer`

when you yield all the variables as its input argument.

### Initialization of a variable using other existing variables

New variables can be initialized using other existing variables’ initial values by taking the values using initialized_value().

# Create another variable with the same value as 'weights'. WeightsNew = tf.Variable(weights.initialized_value(), name="WeightsNew") # Now, the variable must be initialized. init_WeightsNew_op = tf.variables_initializer(var_list=[WeightsNew])

As it can be seen from the above script, the `WeightsNew`

variable is initialized with the values of the `weights`

predefined value.

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All we did so far was to define the

with tf.Session() as sess: # Run the initializer operation. sess.run(init_all_op) sess.run(init_custom_op) sess.run(init_WeightsNew_op)

Each of the initializers has been run separated using a session.

## Summary

In this tutorial, we walked through the variable creation and initialization. The global, custom and inherited variable initialization have been investigated. In future posts, we investigate how to save and restore the variables. Restoring a variable eliminate the necessity of its initialization.