TensorFlow is an open-source machine learning platform that has emerged as a major force in the field of artificial intelligence. It was developed by Google and provides an extensive range of tools, libraries, and resources that allow developers to build and implement machine learning models with a great deal of scalability and flexibility. TensorFlow allows researchers and developers to push the boundaries of AI by training deep neural networks and executing them on devices ranging from smartphones to massive server farms. In this article, we will look at why TensorFlow is so popular and how it is defining the future of machine learning.
Table of Contents
What is TensorFlow?
TensorFlow is one of the most in-demand tools used by ML or AI engineers. It is an open-source framework, developed by Google, that is used to build various machine learning and deep learning models.
TensorFlow helps you to train and execute neural network image recognition, natural language processing, digit classification, and much more. TensorFlow facilitates the estimation of output at various scales using the same models used for development.
The main objective of using TensorFlow is not just the development of a deep neural network, but on reducing the complexity of implementing computations on large numerical data sets. Since deep learning models require a lot of computation to attain accuracy, companies started using TensorFlow. Subsequently, Google made TensorFlow available to all.
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How TensorFlow Works?
One of the best things about TensorFlow is that it provides a feature that helps to create structures for our machine learning models. These structures are built using dataflow graphs. Dataflow graphs denote the functionalities that you want to implement. It consists of a set of nodes in a well-defined order where you can specify the methods for computation.
Dataflow graphs also show us how data, including its functioning, moves through the graph. The above diagram gives more clarity about the mechanism of TensorFlow.
However, the data that you need to feed into the model should be a multidimensional array while using TensorFlow for your applications. Tensors are multidimensional arrays, and they are helpful for handling large amounts of data.
In a graph, every node represents a mathematical operation, while each connection or edge between nodes is a multidimensional data array.
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Why is TensorFlow Popular?
Here are a few reasons for the popularity of TensorFlow:
- TensorFlow is known as the best library for developing AI-based applications because it is designed to be open to all.
- Tensorflow library integrates various APIs to construct deep learning architectures such as convolutional or recurrent neural networks.
- The tensorFlow framework is based on the computation of dataflow graphs. These graphs enable developers to represent the development of a neural network.
- The tensorFlow framework enables the debugging of applications.
- You can easily learn and implement TensorFlow because it is built on Python.
- TensorFlow supports both C++ and Python APIs, making development easier than frameworks used for the same purpose.
- In earlier days, in the development of AI- or ML-based applications, engineers used to create each mechanism of the application without the help of any library or framework. But, with the emergence of various frameworks, such as TensorFlow, the development of complex applications has become easier.
- The library and packages have thousands of built-in functions that enable developers to avoid writing complex and time-consuming codes.
- Moreover, if developers are not comfortable with C++ or Python, then they can use Java or R programming as well because these languages are integrated with TensorFlow as well.
- Another major advantage of using TensorFlow is that it enables developers to work with both GPUs and CPUs.
TensorFlow Components
There are various components of TensorFlow that help to create and execute programs. It includes tensors and graphs. Now, let us understand them in detail.
Tensors
TensorFlow’s name is derived from its core structure, the tensor. All computations in TensorFlow require tensors to execute a program. Now, what exactly is a tensor? A tensor is an n-dimensional vector or matrix that can contain all data types. All tensor values carry the same type of data with a known, or partially known, form. The shape of the input data defines the dimensionality of the matrix.
Tensors can be sourced from either original input data or computed results. All functions and methods in TensorFlow exist within the library’s defined graph structure. This graph is basically an ordered function sequence. Every single operation within the graph is represented as a node (typically referred to as an “op node”), and these nodes are connected. The graph itself specifies both these nodes of operation and the relationship among them. The edges connecting the nodes describe the operations that are to be performed.
Further, let us look at the graphs in detail.
Graphs
A graph is one of the important components that enable the graphical representation of the programmed process. Therefore, the graph framework in TensorFlow is used to represent complex ML or AI processes. Graphs help us to collect and describe the sequence of computations that you want your model to perform. Below are some of the advantages of using graphs:
- You can run graphs on CPUs, GPUs, and mobile operating systems.
- The portability feature of these graphs enables you to save them for performing computations in the future.
- You can visualize the operations and their outputs by using graphs with nodes and edges.
Now, that you understand why graphs are used, let us discuss about dataflow graphs.
When you develop any complex deep learning models, they contain a lot of complex processes with input data stored in tensors. While using the data in tensors, you need to define the flow of execution to perform the computations correctly. For this, you need to use dataflow graphs that help to visualize the flow of data. Dataflow graphs are made of nodes and edges. The nodes show the component where computation is performed, and the edges represent the data that needs to be transferred after the computation process.
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TensorFlow Architecture
In this section of the blog, we will discuss the architecture of TensorFlow. Basically, TensorFlow’s architecture is similar to that used in machine learning, but the components that are used in TensorFlow are different. The tensorFlow architecture consists of three parts:
- Data preprocessing: Here, you have to prepare data to feed it to the model that you need to build. It includes removing duplicate values, feature scaling, standardization, and many other tasks.
- Model building: The next step after data preprocessing is model building, where you create your model by using various algorithms.
- Model training and evaluation: The final step after building your model is training and evaluating it to check whether it generates accurate output or not.
Elements in TensorFlow
In this section, you will get to know about the basics of TensorFlow and the various elements of a program. First, take a look at the two basic concepts that are involved in the working of TensorFlow:
- Constructing a computational graph: The first step is to construct a graph with the help of code.
- Implementing the computational graph: Then, to implement the graph, you have to create a session. A graph cannot be executed without creating a session. You will learn more about sessions when the components of a program are discussed.
Now, take a look at the elements of a program used to store and manipulate data in TensorFlow:
Constants
Similar to other programming languages, constants in TensorFlow are immutable values. A constant is a simple entity that modifies the value of the result of a program. You can use the below-mentioned command to create a constant:
Syntax:
tf.constant()
Example:
#One dimensional constant
x = tf.constant([1,2,3,4,5,6], dtype=tf.float64)
#We can give shape to the tensor
tf.constant( [10,20,30,40,50,60,70,80,90] , shape=(3,3))
0utput:
array([10,20,30],
[40,50,60],
[70,80,90] )
Variables
Variables enable you to change the values while implementing a program. If you are working with a supervised learning algorithm, several iterations are required to train the model to generate accurate results. The objective is to reduce the error by trying out different values. Here, you cannot use constants to store the values. Therefore, in this case, the variables help you iteratively change the values to evaluate the models by using different parameters or values. Variables are also known as mutable tensors.
Syntax:
tf.Variable(argument 1, argument 2)
Example:
#Creating variables
m = tf.Variable([.3],dtype=tf.float32)
x = tf.Variable([-.3],dtype=tf.float32)
#Creating constants
c = tf.constant(tf.float32)
#Linear regression model using variables and constants
Lin_mod = m*x+b
Placeholders
The special type of variables in TensorFlow that enable you to feed data from outside are called placeholders. Typically, placeholders help you to load data from the local system in the form of a CSV file, image, or any other format; this allows you to allocate values later. To initialize a placeholder, use feed_dict; this helps you give value to a placeholder. Use the following command to create a placeholder:
Syntax:
tf.placeholder()
Example:
x = tf.placeholder(tf.float32)
y = x*2
sess = tf.Session()
Output = sess.run(y,feed_dict={a:3.0})
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Sessions
All computations in a TensorFlow program are represented by a graph. However, creating a graph is not sufficient as you are designing a set of programs to execute through the graph. Therefore, you need to execute a graph, and for that, you need to use sessions.
A session enables you to allot resources for AI or DL models, and it maintains the record of actual values. It can provide memory to save the current state of a variable. A session is also executed to measure the performance of the model by evaluating the logic contained by the nodes.
Syntax:
tf.Session()
Example:
# Creating constants
m = tf.constant(18.0)
bn= tf.constant(4.0)
# Defining the operation
k =m*n
# Executing the session
session = tf.Session()
print(sess.run(k))
Without creating a session, you cannot execute the program and the logic.
Conclusion
Google developed the incredibly potent open-source machine learning environment known as TensorFlow, which has gained a lot of popularity. It is adaptable and capable of handling big projects. TensorFlow is based on tensors and graphs, which form the foundation of its information processing mechanism. When building a TensorFlow program, you’ll come across elements such as constants, variables, placeholders, and sessions, each of which has a specific purpose. If you want to learn more about this technology, then check out our Comprehensive Artificial Intelligence Course.
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