Keras Interview Questions and Answers
Question - 21 : - Explain the classes in a Keras Tuner API.
Answer - 21 : - Firstly, the Hyperparameters class is used for specifying a set of hyperparameters and their values, to be used in the model building function. Secondly, the Tuner subclasses corresponding to different tuning algorithms are called directly by the user to begin the search or to get the best models. Thirdly, the Oracle subclasses are the core search algorithms, receiving model evaluation results from the Tuner and providing new hyperparameter values. Lastly, the HyperModel subclasses are predefined search spaces for certain model families like ResNet and XceptionNet.
Question - 22 : - Define Cross-Validation.
Answer - 22 : - Cross-validation is a method for examining a machine learning model and testing its performance. CV is mostly used in applied ML tasks. It helps in comparing and selecting an appropriate model for the specific predictive modeling problem. However, it is easy to understand, easy to implement, and it tends to have a lower bias than other methods used to count the model’s efficiency scores.
Question - 23 : - What is Artificial Neural Network?
Answer - 23 : - Artificial Neural Network (ANN) is at the very core of Deep Learning an advanced version of Machine Learning techniques. ANNs are versatile, adaptive, and scalable, making them appropriate for tackling large datasets and highly complex Machine Learning tasks such as image classification (e.g., Google Images), speech recognition (e.g., Apple’s Siri), video recommendation (e.g., YouTube), or analyzing sentiments among customers (e.g. Twitter Sentiment Analyzer).
Question - 24 : - Describe the algorithm steps used to cross-validate a model.
Answer - 24 : -
- At first, divide the dataset into two parts: one for training, other for testing
- Then , training the model on the training set
- Validating the model on the test set
- Lastly, repeating 1-3 steps a couple of times. This number depends on the CV method that you are using
Question - 25 : - Define Cross-Validation in Deep Learning.
Answer - 25 : - Cross-validation in Deep Learning (DL) can be tricky because most of the CV techniques need training the model at least a couple of times. However, in deep learning, you would normally tempt to avoid CV because of the cost associated with training k different models. Rather than doing k-Fold or other CV techniques, you might use a random subset of your training data as a hold-out for validation purposes. For example, Keras’s deep learning library enables you to pass one of two parameters for the fit function that performs training. This covers: Firstly, validation_split: percentage of the data that should be held out for validation Secondly, validation_data: a tuple of (X, y) which should be used for validation. This parameter overrides the validation_split parameter which means you can use only one of these parameters at once. And, the same method is used in other DL frameworks such as PyTorch and MxNet. They also suggest giving the dataset into three parts: training, validation, and testing.
Question - 26 : - Explain the k-Fold cross-validation technique with the algorithm.
Answer - 26 : -
k-Fold CV is a method that minimizes the disadvantages of the hold-out method. k-Fold introduces a new way of splitting the dataset which helps to overcome the “test only once bottleneck”. The algorithm of the k-Fold technique:
- Firstly, select a number of folds – k. Usually, k is 5 or 10 but you can select any number which is less than the dataset’s length.
- Secondly, divide the dataset into k equal (if possible) parts (they are called folds)
- Then, select k – 1 folds which will be the training set. The remaining fold will be the test set
- Fourthly, train the model on the training set. On each iteration of cross-validation, you must train a new model independently of the model trained on the previous iteration
- Then, validate on the test set After that, save the result of the validation
- Now, repeat steps 3 – 6 k times.
- Every time use the remaining fold as the test set.
In the end, you should have validated the model on every fold that you have. Lastly, for having the final score average the results that you got on step 6.
Question - 27 : - Name the types of cross-validation methods.
Answer - 27 : -
Some of the cross-validation methods are:
- Hold-out K-folds
- Leave-one-out
- Leave-p-out
- Stratified K-folds
- Repeated K-folds
- Nested K-folds
Question - 28 : - Explain Image Data Augmentation in Keras?
Answer - 28 : -
Image data augmentation is a method that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. However, training deep learning neural network models on more data can result in more skillful models, and the augmentation techniques can create variations of the images that can improve the ability of the fit models to generalize what they have learned to new images. And, the Keras deep learning neural network library provides the capability to fit models using image data augmentation via the ImageDataGenerator class.
Question - 29 : - What is an imbalance classification?
Answer - 29 : - An imbalanced classification problem is an example of a classification problem in which the distribution of examples over the known classes is biased or skewed. The distribution can range from a slight bias to a severe imbalance where there is one example in the minority class for hundreds, thousands, or millions of examples in the majority class or classes. Further, this creates a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. This results in models that have poor predictive performance, specifically for the minority class.
Question - 30 : - Name the main types of data augmentation techniques for image data.
Answer - 30 : -
- Image shifts via the width_shift_range and height_shift_range arguments.
- The image flips via the horizontal_flip and vertical_flip arguments.
- Image rotations via the rotation_range argument Image brightness via the brightness_range argument.
- Image zoom via the zoom_range argument.