Important Keras Questions for Cracking Deep Learning Interviews

Devashree Madhugiri 22 Feb, 2023
7 min read


Keras is a deep learning framework that developers and researchers widely use for building and training neural networks. If you are a data science aspirant interested in finding a deep-learning job, you’ll likely be asked about Keras during an interview.

In this article, we’ve prepared a list of the top 11 interview questions related to Keras and its functionalities.

Learning Objectives

  • Learn the basics of Keras, including its advantages over other deep-learning libraries.
  • Understand different Keras models and their respective advantages, including sequential and functional models.
  • Explore techniques for improving neural network performance, such as activation functions, and regularization techniques, such as dropout.
  • Optimize training with techniques like choosing the optimal batch size and early stopping.
  • Learn about transfer learning and how it can be used in Keras to improve model performance.
  • Understand the impact of data augmentation on model performance.

This article was published as a part of the Data Science Blogathon.

Table of Contents

Q1. How is Keras different than other deep learning frameworks, such as tensorflow and pytorch?


Keras is a popular open-source deep learning library written in Python for creating neural networks and other machine learning models. It is a high-level API developed by François Chollet. A key advantage of Keras is its user-friendliness, making it a popular choice for beginners and experts. Keras is a high-level framework built on top of other deep learning libraries like TensorFlow or PyTorch. It provides an easy-to-use interface for creating neural networks, while TensorFlow and PyTorch are low-level frameworks that require more coding.

Keras can be easily installed using the pip package manager. To install keras, we can run the following command in our command prompt or terminal.

pip install keras

Q2. What is the difference between sequential and functional models?

Keras provides two ways to define a neural network model: Sequential and Functional. The sequential model in Keras is a linear stack of layers executed in order. In contrast, the functional model allows for more complex topologies with multiple inputs and outputs and shared layers.

Keras Models

While the sequential model is easier to use and understand, the functional model can handle more complex use cases. It is also more customizable, allowing for the creation of more specialized layers and the incorporation of non-linear activations and loss functions.

Q3. What are activation functions? How do they affect the model performance?

Activation functions in Keras are an essential component of neural networks, as they introduce non-linearity into the model, which is critical for capturing complex relationships between inputs and outputs. Keras provides many
activation functions, including ReLU, sigmoid, tanh, and many more.

Keras Activation Functions

Choosing the correct activation function when designing a neural network is essential, as it significantly impacts the model’s performance. For example, ReLU is often used for hidden layers, as it is computationally efficient and provides good performance for most use cases. On the other hand, sigmoid is often used for output layers, providing a probability-like output that can be easily interpreted.

Q4.  How do the convolutional layers and pooling layers work?

In Keras, convolutional layers are a type of layer where most of the computation happens in convolutional neural networks. These layers apply a convolution operation to the input, a mathematical operation that takes two inputs (a matrix and a kernel) and produces an output (a matrix).

Pooling layers in Keras are used to reduce the dimensionality of the data while retaining important information. Pooling layers are classified into two types: average pooling andmaximum pooling. Average pooling takes the average value
from each pool, whereas max pooling takes the maximum value from each pool.

Q5. Why do we use dropout, and how does it work?

Dropout is a deep learning technique widely used to prevent overfitting, which occurs when a model gets highly trained on training data and fails to perform well on unseen data.

Dropout in Keras

Dropout is a Keras layer that randomly removes a particular fraction of inputs during training. Dropout’s main goal is to keep neurons from becoming overly specialized and to encourage the network to acquire more broad properties. When inputs are dropped out at random, the network is forced to learn multiple representations of the same data, which can improve its overall performance when dealing with new data.

Q6. How can we decide the optimal batch size for training a Neural Network?

The batch size is a hyperparameter that determines the number of samples processed before the model is updated during training. Choosing the right batch size can significantly impact the training process and the model performance.

In general, larger batch sizes can result in faster training times but may also lead to overfitting or poorer generalization performance. On the other hand, smaller batch sizes can lead to slower training times but may result in better generalization performance.

We can select an optimal batch size using different factors, such as dataset size, model complexity, and the available hardware resources like CPU, GPU, RAM. We can experiment with a recommended batch size of 32 or 64 and try with larger or smaller batch sizes to find the optimal value.

Q7. How can we compile and make predictions using a neural network?

Compiling a neural network in Keras requires us to define the optimizer, loss function, and metrics used during training. This can be done using the compile() function, which uses different arguments like an optimizer, loss function,
metrics, etc. For example:

  loss = 'categorical_crossentropy', 
  optimizer = 'sgd', 
  metrics = ['accuracy']

Making predictions using a neural network in Keras involves using the predict() function, which takes in the input data as an argument. The function will return the predicted output values. For example:

ypred = model.predict(X)

Q8. How can we monitor the performance of a model during training?

In Keras, the performance of a model can be monitored during training using callbacks. Callbacks are functions that are called during the training process at defined time spans (e.g., at the end of each epoch).

Keras provides several built-in callbacks that can be used to monitor the performance of a model, like the ModelCheckpoint callback, which saves the model weights after each epoch, and the ReduceLROnPlateau callback, which reduces the learning rate if the validation loss does not improve for a certain number of epochs.

callback = [

In addition to the built-in callbacks, we can define custom callbacks in Keras, which can be used to perform different tasks. For example, logging the performance of the model to a file, stopping the training process early if the model overfits, or
sending an email notification upon completion of the training process.

Q9. What is early stopping in a neural network? How can we implement it?

Early stopping is a technique that can help prevent overfitting and optimize the performance of a model during training. This technique involves monitoring the model’s performance on a validation dataset and stopping the training process early if the performance stops improving. In Keras, the EarlyStopping callback can be used to implement early stopping during training.

callback = [

The EarlyStopping callback can be added to a model’s fit() method, allowing the user to select a monitoring metric and a patience value. The patience value determines the number of epochs the model should wait before stopping the training process. Early stopping can help ensure that the model is trained for the optimal number of epochs while minimizing the risk of overfitting or underfitting.

Q10. Explain transfer learning. How can we use it to improve the performance of a model?

The Transfer learning technique uses a pre-trained model to build a new model. By using the pre-trained weights and architecture of an existing model, transfer learning reduces the amount of data and the model training time
necessary to achieve a good model fit.

We can use transfer learning in Keras by importing a pre-trained model and adding further layers on top of it. The newly added layers can then be trained on a new dataset. For example, with Keras, we can apply transfer learning to fine-tune
pre-trained models such as VGG16 or ResNet for image classification tasks.

Q11. What is data augmentation? How can we implement it?

Data augmentation technique is used to improve the model performance, especially in cases where the dataset is small or imbalanced. It involves generating new training data by applying random transformations to existing data. With new training data, data augmentation can help the model generalize to new data and reduce the risk of overfitting.


We can implement data augmentation using the ImageDataGenerator() class, which provides several built-in transformations like rotation, scaling, and flipping, etc. For example:

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(


In conclusion, it briefly covers relevant topics for anyone interested in using this deep-learning library for their projects. The questions provide a comprehensive understanding of Keras basics, the advantages of different models and activation functions, the importance of regularization techniques like dropout, and how to optimize hyperparameters to improve model performance. It includes critical techniques like data augmentation, transfer learning, and early stopping. Whether you’re just starting with Keras or an experienced user, these interview questions will help deepen your knowledge and help in better interview preparation. The key takeaways from these questions are:

  • It is a popular deep-learning library that provides multiple benefits to users.
  • Understanding the differences between Sequential and Functional models and activation functions is crucial for building effective neural networks with Keras.
  • Regularization techniques like dropout can help prevent overfitting in models.
  • Choosing the optimal batch size and hyperparameters affects the model performance.
  • Data augmentation and transfer learning are helpful techniques for improving model performance.
  • Early stopping can help prevent overfitting and speed up training.
  • Importance of monitoring the model’s performance.

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