Top 8 Interview Questions on TensorFlow

Q: List Some Advantages of TensorFlow.

A: TensorFlow has various advantages, some of which are listed below:

• TensorFlow is open-source, and it has a massive community.
• A user can leverage a certain accelerator (GPU/TPU) based on the requirement. Moreover, it enables parallelism for distributed computing.
• It features a flexible platform that is quite adaptable.
• It offers support for queues, asynchronous computations, and threads.
• TensorFlow also provides a specialized debugger called “tfdbg,” which helps track the internal structure and the state of the TensorFlow graphs while training and debugging.
• TF offers a tool called TensorBoard, which helps visualize the TF graphs.
• The module tf.data helps build effective pipelines for text/image/audio applications.
• The TensorFlow framework helps in scaling up the DL applications. It works equally well on a mobile device as on a complex machine.
• Numerous programming languages, including Python, C++, JavaScript, etc., are compatible with the TF framework.
• Since Google backs it, it receives frequent updates and is capable of putting on a remarkable performance.
• Auto-differentiation feature is present in this.

Q: List a Few Limitations of TensorFlow.

A: Apart from the advantages listed above, the TensorFlow framework has some disadvantages too, which are listed below:

• Despite all of the advantages of TensorFlow, Windows users can only access a specific set of its functionalities. This isn’t the case with Linux users, who have access to a vast range of capabilities.
• Support for Open Computing Language (OpenCL) is not available yet.
• TensorFlow receives regular updates, making it necessary for a user to periodically uninstall and reinstall it to keep up with and incorporate its most recent updates.
• Homophones are often used as names of TF contents, making it difficult for users to recall and use. Confusion arises because one name is used for several different things.
• Even though TensorFlow makes the code smaller and more user-friendly, it also adds a layer of complexity. Each code needs a certain platform for its execution, increasing dependency.
• When it comes to offering symbolic loops for indefinite sequences, TensorFlow lags.

Q: How Would You Determine the TensorFlow Version Using Python Code?

A: 

import tensorflow as tf
print(tf.__version__)

Q: What are Tensors? Could You Explain Scalar, Vector, and Matrix in Terms of  Tensor?

A: Tensors are multi-dimensional arrays with a uniform datatype and are immutable, i.e., we can’t update their content. These are very much like arrays; however, they are of higher dimensions.

Tensors represent a wide variety of data, like images, audio, text, etc., in numeric form. One can quickly get tensors if one is familiar with the NumPy arrays. TensorFlow offers methods for creating tensor functions and computing their derivatives automatically. This is one key aspect differentiating tensors from the Numpy arrays.

For training the machine learning model, the input data in the numerical form (i.e., feature vectors – list of objects) is passed. These feature vectors serve as the initial input for creating a tensor.

Scalar, Vector, Matrix in terms of Tensor:

 

Figure 1: Pictorial illustration of Scalar, Vector, Matrix, and Tensor (Source: hadrienj.github.io)

1. Scalar:  A scalar is a rank-0 tensor that holds a single value and has no axes.

import tensorflow as tf
value1 = 4
scalar = tf.constant(value1)

print(scalar)

>> Output:

2. Vector: Vector is a rank-1 tensor, more like a list of values, and has one axis.

value2 = [2.0, 3.0, 4.0]
vector = tf.constant(value2)
print(vector)

>> Output:

3. Matrix: A matrix is a “rank-2” tensor with two axes.

value3 = [[1, 2],
          [3, 4],
          [5, 6]]
matrix = tf.constant(value3)

print(matrix)

Q: How Would You Check the Datatype of a Tensor?

Answer: To answer this, we will be utilizing the tensor we used in the above question and dtype to determine the output’s data type.

value3 = [[1, 2],
          [3, 4],
          [5, 6]]

matrix = tf.constant(value3)

print(matrix.dtype)

>> Output: tf.int32

Hence, the data type of the given tensor is tf.int32.