Top 30 TensorFlow.js Interview Questions with Answers

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Here are 30 TensorFlow.js interview questions along with their answers:

1. What is TensorFlow.js?

Answer: TensorFlow.js is an open-source JavaScript library developed by Google that allows training and deploying machine learning models directly in the browser or on Node.js.

2. What are the advantages of using TensorFlow.js?

Answer: Some advantages of TensorFlow.js include:

Allows running machine learning models in the browser without the need for server-side processing.

  • Enables building interactive and real-time applications.
  • Provides a unified platform for both training and deployment.
  • Seamless integration with existing JavaScript libraries and frameworks.

3. How does TensorFlow.js differ from TensorFlow?

Answer: TensorFlow is a machine learning framework primarily used for Python, while TensorFlow.js is a JavaScript library specifically designed for running machine learning models in the browser or on Node.js.

4. How can you install TensorFlow.js?

Answer: You can install TensorFlow.js using npm (Node Package Manager) by running the command: npm install @tensorflow/tfjs.

5. How can you load a pre-trained model in TensorFlow.js?

Answer: You can load a pre-trained model in TensorFlow.js using the tf.loadLayersModel() function, which fetches the model’s architecture and weights from a specified URL.

6. What are tensors in TensorFlow.js?

Answer: Tensors are the fundamental data structures in TensorFlow.js. They represent multi-dimensional arrays and are used for storing and manipulating numerical data in TensorFlow.js.

7. How can you create a tensor in TensorFlow.js?

Answer: You can create a tensor in TensorFlow.js using the tf.tensor() function, which accepts an array-like object as input and returns a new tensor.

8. Explain the concept of the computational graph in TensorFlow.js.

Answer: The computational graph in TensorFlow.js represents the series of mathematical operations that are applied to tensors. It defines the flow of data and computations in a TensorFlow.js model.

9. How can you train a machine learning model in TensorFlow.js?

Answer: To train a machine learning model in TensorFlow.js, you need to define the model architecture, compile it with an optimizer and loss function, provide training data, and use the function to start the training process.

10. What is transfer learning in TensorFlow.js?

Answer: Transfer learning is a technique in machine learning where a pre-trained model is used as a starting point for a new task. In TensorFlow.js, you can leverage pre-trained models to extract useful features from data and build models with higher accuracy and efficiency.

11. How can you perform transfer learning in TensorFlow.js?

Answer: In TensorFlow.js, you can perform transfer learning by freezing some or all of the layers of a pre-trained model, replacing the final layers with new ones, and retraining only the new layers using your custom dataset.

12. What are the different types of layers available in TensorFlow.js?

Answer: TensorFlow.js provides various layer types, including Dense layers, Convolutional layers, Recurrent layers, and more. Each layer type is designed for specific types of data and tasks.

13. How can you visualize the training progress in TensorFlow.js?

Answer: TensorFlow.js provides a callback called that can be used to visualize the training progress, such as loss and accuracy metrics, using the TensorFlow.js visualization library (tfvis).

14. What is data augmentation, and how can you perform it in TensorFlow.js?

Answer: Data augmentation is a technique used to artificially increase the size of a training dataset by applying random transformations to the existing data. In TensorFlow.js, you can perform data augmentation by applying various image transformations like rotation, scaling, and flipping.

15. How does the Python API work with TensorFlow?

Answer: Python is the primary language when it comes to working with TensorFlow. TensorFlow provides an ample number of functionalities when used with the API, such as:

  • Automatic checkpoints
  • Automatic logging
  • Simple training distribution
  • Queue-runner design methods

16. What are some of the APIs outside of the TensorFlow project?

Answer: Following are some of the APIs developed by Machine Learning enthusiasts across the globe:

  • TFLearn: A popular Python package
  • TensorLayer: For layering architecture support
  • Pretty Tensor: Google’s project providing a chaining interface
  • Sonnet: Provides a modular approach to programming

17. What are TensorFlow loaders?

Answer: Loaders are used in TensorFlow to load, unload, and work with servable objects. The loaders are primarily used to add algorithms and data into TensorFlow for working.

The load() function is used to pre-load a model from a saved entity easily.

18. What makes TensorFlow advantageous over other libraries?

Answer: Following are some of the benefits of TensorFlow over other libraries:

  • Pipelines: data is used to build efficient pipelines for text and image processing.
  • Debugging: tfdbg is used to track the state and structure of objects for easy debugging.
  • Visualization: TensorBoard provides an elegant user interface for users to visualize graphs.
  • Scalability: It can scale Deep Learning applications and their associated infrastructure easily.

19. What are TensorFlow abstractions?

Answer: TensorFlow contains certain libraries used for abstraction such as Keras and TF-Slim. They are used to provide high-level access to data and model life cycles for programmers using TensorFlow. This can help them easily maintain clean code and also reduce the length of the code exponentially.

20. How training and testing are performed?

Answer: We build each model with the help of a dataset that is either created or imported. The dataset is divided into two portions: training and testing. Training data contains most of the values so that system gets apprehended by all types of cases.

Training data contains very few values compared to training. It tests the system for unknown values for better learning.

21. What are the activation functions?

Answer: The function which imparts the non-linear property to the output of the summation function is the activation function. The expected output represents the probability hence it restrains the value between 0 and 1.

22. What are loaders?

Answer: It manages the life cycle of the servable. The loader API enables an infrastructure that is independent of data or product use cases and algorithms. Then we standardize the loader’s API for loading and unloading of servable.

23. Which Dashboards are supported by TensorFlow?

Answer: TensorFlow supports the following Dashboards:

  • Scalar Dashboard
  • Histogram Dashboard
  • Distributor Dashboard
  • Image Dashboard
  • Projector
  • Graph Explorer
  • Audio Dashboard
  • Text Dashboard

24) How does TensorFlow use Python API?

Answer: Python is the primary language when it comes to TensorFlow and its development. It is the first and most recognizable language supported by TensorFlow and still supports most of the features. It seems like the functionality of TensorFlow was initially defined in Python and later moved to C++.

25) What are the APIs used inside the TensorFlow project?

Answer: Most of the APIs inside TensorFlow are based on Python language. They have low-level options for the users such as tf. manual or tf.nn.relu used to build Neural Network Architecture. These APIs are also used to design a deep neural network having higher levels of abstraction.

26) What are the APIs used outside the TensorFlow project?

Answer: There are a few APIs used outside the TensorFlow project, which are:

  • TFLearn: TFLearn provides a high-level API that makes neural network building and training fast and easy. This API is fully compatible with Tensorflow. Its API can be indicated as tf.contrib.learn.
  • TensorLayer: TensorLayer is a TensorFlow-based deep learning and reinforcement learning library. It is designed for researchers and engineers. It provides an extensive collection of customizable neural layers/functions which are crucial for building real-world AI applications.
  • PrettyTensor: Pretty Tensor delivers high-level builder API for TensorFlow. It offers thin wrappers on Tensors so that you can easily build multi-layer neural networks.
  • Pretty Tensor provides a set of objects that behave like Tensors. It also supports a chainable object syntax to define neural networks and other layered architectures in TensorFlow quickly.
  • Sonnet: Sonnet is a library that is built on top of TensorFlow for creating complex neural networks. It is a part of Google’s DeepMind project, which features a modular approach.

27. How do we create tensors from Python objects?

Answer: We can create tensors such as numpy arrays and lists with the help of Python objects. We can easily perform it using tf.convert_to_tensor() operation.

28. What are the variables in TensorFlow?

Answer: Variables in TensorFlow are also known as tensor objects. These objects hold the values which can be modified during the execution of the program.

A TensorFlow variable is the best way which is used to represent a shared, persistent state manipulated by the program.

29. What is the lifetime of a variable?

Answer: A variable is created when we first run the tf.Variable.initializer operation for that variable in a session. It gets destroyed when tf.Session. close is processed.

30. What is the ReLU layer?

Answer: Rectified Linear Unit Layer acts as an activation layer that activates the function having a value above a specific unit. It replaces the negative values in an image with zero, defining a linear relationship of the variable with the input. It makes the input invariant to noise; hence it is known as subsampling.

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