Course Content
Introduction
TensorFlow is an open-source and free library for differentiable programming and data flow across a variety of tasks. TensorFlow is a math library and it is used for machine learning applications for example neural networks.
0/14
TensorFlow for Beginners

To create variables in TensorFlow we use tensorflow.get_variable()

Syntax:

tf.get_variable(name = "", values, dtype, initializer)

argument

• `name = “”`: Name of the variable
• `values`: Dimension of the tensor
• `dtype`: Type of data. Optional
• `initializer`: How to initialize the tensor. Optional

If the initializer is specified, there is no need to include the `values` as the shape of `initializer` is used.

import tensorflow as tf

# Create a Variable
var = tf.get_variable("var", [1, 2])
print(var)

#following initializes the variable with a initial/default value
var_init_1 = tf.get_variable("var_init_1",
[1, 2],
dtype=tf.int32,
initializer=tf.zeros_initializer)
print(var_init_1)

#Initializes the first value of the tensor equals to tensor_const
tensor_const = tf.constant([[10, 20],[30, 40]])
var_init_2 = tf.get_variable("var_init_2",
dtype=tf.int32,
initializer=tensor_const)
print(var_init_2)

The output will be <tf.Variable ‘var:0’ shape=(1, 2) dtype=float32_ref> <tf.Variable ‘var_init_1:0’ shape=(1, 2) dtype=int32_ref> <tf.Variable ‘var_init_2:0’ shape=(2, 2) dtype=int32_ref>