First of all, let us look at the topics that we will cover in this blog:

**What is TensorFlow?****How TensorFlow Works****Why is TensorFlow Popular?****TensorFlow Components****TensorFlow Architecture****Elements in TensorFlow**

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**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 to train and execute neural network image recognition, natural language processing, digit classification, and much more. By using the same models that are used for development, TensorFlow facilitates the estimation of output at various scales.

The main objective of using TensorFlow is not just the development of a **deep neural network**, but TensorFlow is focused to reduce the complexity of implementing computations on large numerical data sets. Since deep learning models require a lot of computation for attaining accuracy, companies started using TensorFlow. Subsequently, Google made TensorFlow available to all.

**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 made of 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. These multidimensional arrays are known as tensors, and they are very helpful while dealing with 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.

**Why is TensorFlow Popular?**

Here are a few reasons for the popularity of TensorFlow:

- As it is designed to be open to all, TensorFlow is known as the best library among all other libraries that are used for developing AI-based applications.
- Tensorflow library integrates various APIs to construct deep learning architectures such as convolutional or recurrent neural networks.
- TensorFlow framework is based on the computation of dataflow graphs. These graphs enable developers to represent the development of a neural network.
- TensorFlow framework enables the debugging of applications.
- As TensorFlow is built on Python, it is easy to learn and implement.
- Both C++ and Python APIs are supported by TensorFlow, which makes development easier than other 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.

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**TensorFlow Components**

There are various components of TensorFlow that help to create and execute programs. The components of TensorFlow include tensors and graphs. Now, let us understand them in detail.

**Tensor**s

The name TensorFlow is derived from its core structure, 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 dimensionality of the matrix is defined by the shape of the input data.

A tensor may be derived from the input data or the outcome of a process. All functions or methods are carried out in a graph defined by using the TensorFlow library. A graph is a sequence of functions that are carried out consecutively. Each operation represented in a graph is known as an op node, and these nodes are related to each other. A graph describes the operation nodes and relations between the nodes. The edges connected to the nodes in the graph describe the operations to be performed.

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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, 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 it for performing computations in the future.
- You can easily visualize which operations are being performed and how you can get the output with the help of nodes and edges represented by graphs.

Now, as you understand why graphs are used, let us discuss and learn about dataflow graphs.

When you develop 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 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.

*What are TensorFlow and Keras? Check out the key differences between them in our comparison blog on **Keras vs TensorFlow**.*

**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. TensorFlow architecture consists of three parts:

**Data preprocessing:**Here, you have to prepare data for the purpose of feeding 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.

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**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, for implementing 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 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 end 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 for generating 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()

**Exampl**e:

x = tf.placeholder(tf.float32) y = x*2 sess = tf.Session() Output = sess.run(y,feed_dict={a:3.0})

**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.

In a nutshell, within a program, constants, variables, and placeholders enable data handling, after which you must run a session to execute the program.

With this, we have come to the end of this blog. In the blog, we have covered all that you need to know about TensorFlow and its related concepts.

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