Keras Interview Questions and Answers
Question - 11 : - Explain the process of training a CNN.
Answer - 11 : -
The process for training a CNN for classifying images consists of the following steps −
Data Preparation In this step, we center-crop the images and resize them so that all images for training and testing would be of the same size. This is usually done by running a small Python script on the image data.
Model Definition In this step, we define a CNN architecture. The configuration is stored in .pb (protobuf) file.
Solver Definition In this, we define the solver configuration file. The solver does the model optimization.
Model Training In this, we use the built-in Caffe utility to train the model. The training may take a considerable amount of time and CPU usage. After the training is completed, Caffe stores the model in a file, which can, later on, be used on test data and final deployment for predictions.
Question - 12 : - What do you know about Data preprocessing with Keras?
Answer - 12 : - Once your data is in the form of string/int/float NumpPy arrays, or a Dataset object (or Python generator) that yields batches of string/int/float tensors, it is time to preprocess the data. This can mean: Firstly, Tokenization of string data, followed by token indexing. Secondly, Feature normalization. Thirdly, Rescaling the data to small values. In general, input values to a neural network should be close to zero — typically we expect either data with zero-mean and unit-variance, or data in the [0, 1] range.
Question - 13 : - Explain the process of debugging your model with eager execution.
Answer - 13 : - If you write custom training steps or custom layers, you will need to debug them. The debugging experience refers to an integral part of a framework and with Keras, the debugging workflow is designed with the user in mind. However, by default, Keras models are compiled to highly optimized computation graphs that deliver fast execution times. That means that the Python code you write is not the code you are actually executing. This introduces a layer of indirection that can make debugging hard. Further, it is better to perform debugging in a step-by-step manner. You want to be able to sprinkle your code with a print() statement to see what your data looks like after every operation, you want to be able to use pdb. You can achieve this by running your model eagerly. With eager execution, the Python code you write is the code that gets executed. Simply pass run_eagerly=True to compile():
Question - 14 : - What is a Keras Tuner?
Answer - 14 : - Keras Tuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. In this, you can easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms for finding the best hyperparameter values for your models. Further, Keras Tuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.
Question - 15 : - Explain the role of multiple GPUs in Keras.
Answer - 15 : - Keras has built-in industry-strength support for multi-GPU training and distributed multi-worker training, via the tf.distribute API. However, if you have multiple GPUs on your machine, you can train your model on all of them by: Firstly, creating a tf.distribute.MirroredStrategy object. Secondly, creating and compiling your model inside the strategy’s scope. Lastly, calling fit() and evaluate() on a dataset as usual.
Question - 16 : - Describe the installation & compatibility of Keras.
Answer - 16 : - Keras comes packaged with TensorFlow 2 as tensorflow.keras. However, to start using Keras, simply install TensorFlow 2. Keras/TensorFlow is compatible with: Python 3.5–3.8 Ubuntu 16.04 or later Windows 7 or later macOS 10.12.6 (Sierra) or later.
Question - 17 : - What is AutoKeras?
Answer - 17 : - AutoKeras refers to an AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The purpose of AutoKeras is to make machine learning accessible for everyone. It provides high-level end-to-end APIs such as ImageClassifier or TextClassifier to solve machine learning problems in a few lines, as well as flexible building blocks to perform architecture search.
Question - 18 : - How can we create Keras models?
Answer - 18 : - There are three ways to create Keras models: Firstly, by the Sequential model. This is very straightforward (a simple list of layers), but is limited to single-input, single-output stacks of layers. Secondly using the Functional API. This is an easy-to-use, fully-featured API that supports arbitrary model architectures. This is the Keras “industry strength” model. Lastly, by Model subclassing. Here you implement everything from scratch on your own. Use this if you have complex, out-of-the-box research use cases.
Question - 19 : - Define the following terms: TensorFlow Cloud, TensorFlow.js and TensorFlow Lite.
Answer - 19 : - TensorFlow Cloud is managed by the Keras team at Google that refers to a set of utilities to help you run large-scale Keras training jobs on GCP with very little configuration effort. Running your experiments on 8 or more GPUs in the cloud should be as easy as calling model.fit(). TensorFlow.js is TensorFlow’s JavaScript runtime, capable of running TensorFlow models in the browser or on a Node.js server, both for training and inference. It natively supports loading Keras models, including the ability to fine-tune or retrain your Keras models directly in the browser. TensorFlow Lite is a runtime for an efficient on-device inference that has native support for Keras models. Deploy your models on Android, iOS, or on embedded devices.
Question - 20 : - Describe the steps for creating a deep Learning Project in Python with Keras.
Answer - 20 : -
- Load Data The first step is for defining the functions and classes. In this, we will use the NumPy library to load our dataset and we will use two classes from the Keras library to define our model.
- Define Keras Model Models in Keras are defined as a sequence of layers. Here, we will create a Sequential model and add layers one at a time until we are happy with our network architecture. However, ensure that the input layer has the right number of input features. This can be specified when creating the first layer with the input_dim argument and setting it to 8 for the 8 input variables.
- Compile Keras Model After defining the model, compile it. However, compiling the model uses the efficient numerical libraries under the covers (the so-called backend) such as Theano or TensorFlow. The backend automatically chooses the best way to represent the network for training and making predictions to run on your hardware, such as CPU or GPU or even distributed. During compiling, specify some additional properties required when training the network. And, also specify the loss function to use to evaluate a set of weights, the optimizer is used to search through different weights for the network and any optional metrics we would like to collect and report during training.
- Fit Keras Model After defining and compiling it is ready for efficient computation. Now, execute the model on some data. We can train or fit our model on our loaded data by calling the fit() function on the model. However, training occurs over epochs and each epoch is split into batches. Epoch: One pass through all of the rows in the training dataset. Batch: One or more samples are considered by the model within an epoch before weights are updated. You must know that one epoch is comprised of one or more batches, depending on the chosen batch size and the model is fit for many epochs.
- Evaluate Keras Model After training the neural network on the entire dataset and we can examine the performance of the network on the same dataset. However, we can evaluate your model on your training dataset using the evaluate() function on your model and pass it the same input and output used to train the model. Further, the evaluate() function will return a list with two values. The first will be the loss of the model on the dataset and the second will be the accuracy of the model on the dataset.