{"id":16944,"date":"2018-06-09T04:15:30","date_gmt":"2018-06-09T04:15:30","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=16944"},"modified":"2021-05-14T11:00:06","modified_gmt":"2021-05-14T05:30:06","slug":"tensorflow-interview-questions-and-answers","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/tensorflow-interview-questions-and-answers\/","title":{"rendered":"30 Latest TensorFlow Interview Questions and Answers"},"content":{"rendered":"

In our last TensorFlow<\/strong> tutorial, we have discussed \u00a0Top 30 TensorFlow Interview Questions <\/strong>and Answers. Today, we will be discussing the latest TensorFlow Interview Questions and Answers for TensorFlow upcoming interview.<\/p>\n

This TensorFlow interview tutorial will help you to crack your TensorFlow Interview the first attempt.<\/p>\n

So, let’s explore the latest TensorFlow Interview Questions and Answers.<\/p>\n

Mostly Asked TensorFlow Interview Questions and Answers<\/h2>\n

After discussing top 30\u00a0TensorFlow Interview Questions and Answers<\/strong>, we are ready to face this TensorFlow Logical Interview Questions and Answers.<\/p>\n

Q1.<\/b> How you place operations on a particular device? <\/b><\/p>\n

You should create the operations within a\u00a0with tf.device(name):\u00a0context to place them on a particular device.<\/span><\/p>\n

Q2. Which client languages are supported in TensorFlow?<\/b><\/p>\n

TensorFlow supports multiple client languages, the best language being\u00a0Python<\/strong>. There are experimental interfaces that are available for\u00a0C++\u00a0Java<\/strong> and\u00a0Go. Bindings for various other languages (such as\u00a0C#,\u00a0Julia,\u00a0Ruby\u00a0and\u00a0Scala<\/strong>) are created and supported by the opensource community.<\/span><\/p>\n

Q3. Do Sessions have a lifetime? What about intermediate tensors?<\/b><\/p>\n

Resources like as\u00a0tf.Variable,\u00a0tf.QueueBase, and\u00a0tf.ReaderBase; own by a session and may use a significant amount of memory which are released when the session is terminated with\u00a0tf.Session.close.<\/span><\/p>\n

Q4. What’s the deal with feeding and placeholders?<\/b><\/p>\n

Feeding is a phenomenon that allows you to substitute different values for one or more Tensors at the runtime. The\u00a0feed_dict\u00a0argument is used to map tf.Tensorobjects to numpy arrays for further executions.<\/span><\/p>\n

Q5. Why does\u00a0Session.run()\u00a0hang when using a reader or a queue?<\/b><\/p>\n

The\u00a0tf.ReaderBase and\u00a0tf.QueueBase\u00a0classes provide special operations that become\u00a0blocked\u00a0since the input isn\u2019t available. They allow building clear\u00a0input pipelines, by making the computation a little more complicated. <\/span><\/p>\n

Q6. What is the lifetime of a variable?<\/b><\/p>\n

A variable is created when you first run the\u00a0tf.Variable.initializer\u00a0operation for that variable in a session. It gets destroyed when that\u00a0tf.Session.close.<\/span><\/p>\n

Q7. How do variables behave when they are concurrently accessed?<\/b><\/p>\n

Variables allow concurrency in read\/write ops. The variable value may change when the concurrently updates. By default, there is no mutex (mutual exclusion). <\/span><\/p>\n

Q8. What is the simplest way to send data to TensorBoard?<\/b><\/p>\n

First of all, you should add summary operations to your graph, and then log them in a log directory. Then, TensorBoard \u00a0should be started using:<\/span><\/p>\n

python tensorflow\/tensorboard\/tensorboard.py –logdir=path\/to\/log-directory<\/b><\/p>\n

Q9. What exactly do you know about Bias-Variance decomposition?<\/b><\/p>\n

It generally uses to decompose problems such as errors that occur during learning in different algorithms.<\/span> Bias keeps reducing if the data is to be made complex. Trading off the Variance and Bias are very essential to get results that are totally free from errors.\u00a0<\/span><\/p>\n

Q10. <\/b>\u00a0<\/span>How is k-means clustering different from KNN?<\/b><\/p>\n

It is an unsupervised learning algorithm used for clustering. On the other hand, the KNN is a structured clustering algorithm. They both share some similarities but users need to label the data in the KNN which is not required in k-means clustering.\u00a0<\/span><\/p>\n

TensorFlow Interview Questions and Answers for Freshers. Q- 1,2,4,6,8,9<\/strong><\/p>\n

TensorFlow Interview Questions and Answers for Experience. Q- 3,5,7,10<\/strong><\/p>\n

Q11. <\/b>What exactly Neural Networks are? What are the types of same you are familiar with?<\/b><\/p>\n

Basically a connection of processing elements which can very large or very small depending on the application, it deployed for. These elements called neurons and generally, two types of networks can be seen in this category. <\/span><\/p>\n

They are Artificial Neural Networks and Biological Neural Networks. The use of artificial neural networks is more common and generally, they are considered for creating machines which are equally powerful to human brains.<\/span><\/p>\n

Q12. How do you import Tensorflow?<\/b><\/p>\n

import TensorFlow as tf<\/span><\/p>\n

Q13. What are word embeddings used for and can they be used in TensorFlow?<\/b><\/p>\n

Word embeddings usually use in Natural Language Processing as a representation of words and they can use in TensorFlow where it also call as word2vec.<\/span>
\n<\/b><\/p>\n

Q14. Name the two models used in word embeddings?<\/b><\/p>\n

The Continuous Bag of Words (CBOW) model and the skip-gram model<\/span><\/p>\n

Q15. Write a code to start a simple session for the training?<\/b><\/p>\n

with tf.Session() as sess:<\/pre>\n
Q16. Explain the following example.<\/b><\/p>\n\n\n\n\n\n
for the epoch <\/span>in \u00a0\u00a0\u00a0\u00a0\u00a0<\/span>range<\/span>(training_epochs):<\/span><\/td>\n<\/tr>\n
<\/td>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span>for<\/span> (x, y) <\/span>in<\/span> zip<\/span>(train_X, train_Y):<\/span><\/td>\n<\/tr>\n
<\/td>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0sess.run(optimizer, <\/span>feed_dict<\/span>=<\/span>{X: x, Y: y})<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Here, the initializer is run and all the training data fit by running a loop for all the epochs<\/span><\/p>\n
Q17. How do you see the charts and graphs for your model and what is the URL?<\/b><\/p>\n
You can view the charts and graphs using TensorBoard by browsing to <\/span>https:\/\/localhost:6006<\/strong> in your browser.<\/span>
\n<\/b><\/p>\n
Q18. What is the confusion matrix?<\/b><\/p>\n
A confusing matrix comprising of discrete values where each column contains a set of samples that estimated to be a keyword in your training model.<\/span><\/p>\n
Q19. Describe the steps to configure a wide and deep model in TensorFlow?<\/b><\/p>\n