TensorFlow Interview Questions

TensorFlow Interview Questions and Answers

TensorFlow Interview Questions can help you land jobs such as machine learning engineer, deep learning expert, NLP Developer, AI Engineer, Computer Vision Engineer, and other job roles that include working with TensorFlow Software Library. You will be able to ace your interview by learning from these TensorFlow interview questions and answers. So, start preparing now!

Most Frequently asked TensorFlow Interview Questions What exactly is TensorFlow? What is the different type of Tensors? List a few advantages of TensorFlow What is TensorFlow used for? What do you know about TensorFlow managers? What is the basic operation of a TensorFlow algorithm? Difference between TensorFlow's tf.variable and tf.placeholder Mention the dashboards supported by TensorFlow In TensorFlow, what are activation functions? What APIs does the TensorFlow project make use of? Mention the variables in TensorFlow  What is a variable's lifetime?

Q1) What exactly is TensorFlow?

Ans: TensorFlow is a machine learning open-source framework created by Google. This library enables the creation and execution of user-friendly and system-appropriate algorithms. It facilitates eager execution through visualization, which it delivers with Tensor Board's support. It simplifies calculations, lowering the complexity of calculations.

Q2) How well do you understand tensors?

Ans: Tensors are multi-dimensional arrays or vectors. It's a string of digits that represents data in its coded form. Tensors are represented in the form of edges. The shape of the data recorded in vectors determines the dimensionality of a matrix. Tensors can represent both scalars and vectors. A tensor is a three-dimensional object with three distinct properties: name, shape, and dtype. A tensor's operations are also shown in the graphs. A feature vector is any object that is originally present in the model. Graphs have a feature vector that aids in the mapping of tensors.

Q3) What is Tensor Board?

Ans: Tensor Board is a TensorFlow-provided Graphical User Interface (GUI) that allows users to easily visualize graphs, plots, and other metrics without having to write a lot of code. In terms of readability, the convenience of usage, and performance metrics, Tensor Board offers a plethora of benefits.

Q4) Name some features of TensorFlow

Ans: Some features of TensorFlow are:

• The most powerful feature of TensorFlow is its capacity to create neural networks, which allow machines to develop logical reasoning and learning in the same way that people do. 
• It is one of the greatest deep learning libraries that can also express some simple math processes. 
• It may perform a variety of tasks, including pre-processing, calculation, status, data loading, and output. 
• There's also Define and Run, which creates calculation processes via a graph and then gathers it. 
• TensorFlow can also be used to disseminate learning across Android and iOS devices.

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Q5) What is the different type of Tensors?

Ans: Tensors are of three types – Constants, Placeholders, and Variables. 

• Constants: It denotes a node that only has one value and does not change over time. Value, name='Const, shape=None, and dtype=None are the parameters that are in sync with the constant. tf.constant is its denotation.
• Placeholders: The nodes for these tensors are created, but the data assignment is done afterward. When the input is dependent on a third party, the assignment is made. As a result, this tensor just requires the data type and shape defined at the start.
• Variables:  It is called a variable because it represents the varied values at each run. When it is run, it prints the most recent value. tf.variable is its denotation.

Q6) How do you load data into TensorFlow?

Ans: There are two methods for loading data into TensorFlow.

• Loading data into memory: Data is loaded into memory as a single array unit. It is the simplest method of data loading.
• Data pipeline with TensorFlow: It is loading the data and feeds it to the algorithm using the built-in APIs.

Q7) List a few advantages of TensorFlow

Ans: Advantages of TensorFlow are:

• It offers platform flexibility
• For distributed computing, it is easily trainable on both CPU and GPU.
• TensorFlow has automatic differentiation capabilities
• It offers advanced support for threads, asynchronous computations, and queues.
• It is customizable and open source.

Q8) Name the disadvantages of TensorFlow

Ans: Disadvantages of TensorFlow are:

• Symbolic cycles
• Dependence
• Frequent updates
• Architectural constraints
• Low speed
• GPU support
• Windows support
• Inconsistency

Q9) What is TensorFlow used for?

Ans: TensorFlow is used for classification, understanding, perception, prediction, discovery, and creation. It's broadly applied in the following sectors:

• Vocal recognition
• Image detection
• Video detection
• Text applications
• Time series
• Sentiment analysis

Q10) What are the most common applications of TensorFlow?

Ans: TensorFlow is utilized in all the Machine Learning and Deep Learning fields. TensorFlow's key use cases, as it is the most important tool, are as follows:

• Analyzing time series
• Recognition of images
• Recognition of speech
• Upscaling of video in test-based applications

Q11) What do you know about TensorFlow managers?

Ans: TensorFlow managers are organizations that are in charge of handling the following servable object activities:

• Loading
• Unloading
• Lookup
• Lifetime management

Q12) What is Servable?

Ans: One of the main attractions that aids in wrapping TensorFlow items is the Servable. It's an underlying object that clients use to perform operations and calculations like inference. In a production and distributed context, there is a servable focus on the interference element of ML projects. The size is key as, the smaller the servable faster the load time.

Q13) What is the basic operation of a TensorFlow algorithm?

Ans: The bulk of algorithms in TensorFlow follow a five-step process. The following are the details:

• Importing or creating data, as well as putting up a data pipeline
• Computational graphs are used to input data.
• To analyze the output, the loss function is created.
• Backpropagation is a technique for modifying data.
• Iterate until the output criteria are satisfied.

Q14) What languages does TensorFlow support?

Ans: TensorFlow allows programmers to write code in a wide range of languages. Python is the most popular format today. Coming to the usage of other languages, such as Go, Java, and C++, is currently being experimented with. 

Q15) How well does TensorFlow function with the Python API?

Ans: When it comes to dealing with TensorFlow, Python is the language of choice. When combined with the API, TensorFlow offers a wide range of capabilities, including:

• Checkpoints that are automatically generated
• Logging that is done automatically
• The distribution of training materials is simple.
• Methods for designing queue-runners

Q16) Difference between TensorFlow's tf.variable and tf.placeholder

Ans:

Tf.variable	Tf.placeholder
Values for variables that change over time are defined.	Inputs that do not vary over time are defined.
Initialization is required when stated.	Initialization is not required during the definition process.

Q17) Explain TensorFlow’s Graph Explorer

Ans: On Tensor Board, you may use a graph explorer to visualize a graph. In TensorFlow, it's also used to inspect a model. It is advised that you utilize Tensor Board's graph visualizer to readily grasp the flow of a graph.

Q18) Mention the dashboards supported by TensorFlow

Ans: TensorFlow includes several dashboards that may be used to quickly do a range of activities on Tensor Board:

• Dashboard with scalars
• Picture of a dashboard
• Dashboard with graphs
• Dashboard with text
• Dashboard for distributors
• Dashboard with histograms

Q19) Differences between TensorFlow and PyTorch

Ans: 

TensorFlow	PyTorch
This was developed by Google	This was developed by Facebook
There is no support for graph operations at runtime available.	At runtime, it performs computational graph operations.
Tensor Board is for visualization.	There are no visualization tools included in the bundle.
Uses the Theano library as a foundation	Uses the Torch library as a foundation

Q20) Is TensorFlow compatible with word embedding?

Ans: Yes, TensorFlow supports word embedding, it is commonly used in the field of Natural Language Processing. When TensorFlow is employed, it is referred to as Word2vec. In TensorFlow, there are two models for word embedding:

• The model of the Continuous Bag of Words
• The Skip-Gram model

Q21) Is it possible to utilize Tensor Board without TensorFlow?

Ans: Users can still use Tensor Board in a freestanding mode with censored functionalities if TensorFlow is not installed. The plugins listed below are supported:

• Scalars
• Image
• Audio
• Graph
• Projector
• Histograms
• Mesh

Q22) Is it always better to prioritize performance above accuracy when using TensorFlow?

Ans: When using TensorFlow, accuracy does not necessarily take precedence over performance. This is entirely dependent on the sort of requirement and the goal of the model. The basic rule of thumb is to give model accuracy and performance equal weight.

Q23) What are some examples of TensorFlow-powered products?

Ans: TensorFlow is used in the development of a large number of products. Here are a few of them:

• Teachable Machine
• Handwriting Recognition
• Giorgio Cam
• NSynth

Q24) In TensorFlow, what is the purpose of a histogram dashboard?

Ans: Histogram dashboards are commonly used to present sophisticated statistical distributions of a tensor straightforwardly. Every histogram graphic will include a data slice that represents the data the tensor has at the point of representation.

Q25) What does the term "deep speech" mean?

Ans: Deep Speech is an open-source, TensorFlow-based speech-to-text engine. It uses a simple syntax to analyze speech from input and produce written output on the other end, and it is learned using Machine Learning techniques.

Q26) Explain TensorFlow JS?

Ans: TensorFlow JS is a library that enables users to run Machine Learning models in their browsers. High-level APIs use JavaScript to support a range of backend entities, such as WebGL, which makes use of a GPU to render functionality (if available). Models may be readily imported, retrained, and executed using only a browser.

Q27) In TensorFlow, what are activation functions?

Ans: Activation functions are functions that are applied to the output side of a neural network and serve as the input for the following layer. It is an essential component of neural networks since it provides the nonlinearity that distinguishes them from logistic regression.

Q28) In TensorFlow, what is model quantization?

Ans: TensorFlow may substantially simplify the process of dealing with the complexity that arises when optimizing inferences. Model quantization is generally used to minimize the size of weight representations, as well as to store and compute the activation function.

 Users benefit from using model quantization in two ways:

• A variety of CPU platforms are supported.
• Capabilities for handling SIMD instructions

Q29) What are some of the most regularly utilized optimizers in TensorFlow while training a model?

Ans: Many optimizers can be used based on a variety of criteria, including learning rate, dropout, performance metric, gradient, and more.

Here are a few of the most popular optimizers:

• AdaDelta
• AdaGrad
• Adam
• Momentum
• RMSprop
• Stochastic Gradient Descent

Q30) In TensorFlow, what is the difference between Array Flow and FeedDictFlow?

Ans:

• Array Flow converts array entities into tensors and stores them in a queue data structure dynamically.
• From the input dataset, FeedDictFlow is utilized to generate a stream of batch data. The system is based on two queues, one for batch generation and the other for loading data and applying pre-processing algorithms to it.

Q31) What are some of the most critical factors to consider when using TensorFlow to create a random forest algorithm?

Ans: When using TensorFlow to create a random forest algorithm, there are six essential parameters to consider and plan for:

• The total number of inputs
• The number of features
• Per batch, the number of samples
• Number of training steps total
• The total number of trees
• The maximum number of nodes possible

Q32) What APIs does the TensorFlow project make use of?

Ans: The Python language underpins the majority of TensorFlow's APIs. Users can design Neural Network Architecture using low-level options such as tf.manual or tf.nn.relu. These APIs are also used to create a deep neural network with higher abstraction layers.

Q33) Outside of the TensorFlow project, what APIs are used?

Ans: Outside of the TensorFlow project, the following APIs are used:

• TF Learn: TFLearn provides a high-level API for quickly and easily creating and training neural networks. TensorFlow is fully compatible with this API. tf.contrib.learn is the API name for it.

• Tensor Layer: Tensor Layer is deep learning and reinforced learning library based on TensorFlow. It is intended for scientists and engineers. It comes with a large number of programmable neural layers and functions, which are essential for creating real-world AI applications.

• Pretty Tensor: Pretty Tensor is a TensorFlow high-level building API. It provides thin wrappers for Tensors so that multi-layer neural networks can be built quickly. Pretty Tensor is a collection of items that act like tensors. TensorFlow also enables a chainable object syntax for quickly defining neural networks and other layered architectures.

• Sonnet: Sonnet is a library for developing complicated neural networks that are built on top of TensorFlow. It's part of Google's DeepMind initiative, which takes a modular approach to machine learning. 

 Q34) Mention the variables in TensorFlow 

Ans: Tensor objects are another name for variables in TensorFlow. These objects contain the values that can be changed while the program is running. A TensorFlow variable is the best approach to define a shared, persistent state that the program manipulates. 

Q35) What is a variable's lifetime? 

Ans: When we execute the tf for the first time, a variable is created. In a session, the Variable.initializer operation is used to initialise that variable. When tf occurs, it is destroyed. Session.close has been executed.