Category: Keras Interview Question

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…

 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.

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()…

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,…

 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,…

Some of the examples include: Firstly, neural networks don’t process raw data, like text files, encoded JPEG image files, or CSV files. They process vectorized & standardized representations. Secondly, text files need to be read into string tensors, then split into words. Finally, the words need to be indexed and turned into integer tensors. Thirdly,…

François Chollet, He is currently working as an AI Researcher at Google

is a high-level neural networks API, capable of running on top of Tensorflow, Theano, and CNTK. It enables fast experimentation through a high-level, user-friendly, modular, and extensible API. Keras can also be run on both CPU and GPU.Keras was developed and is maintained by Francois Chollet and is part of the Tensorflow core, which makes…

Keras models accept three types of inputs: Firstly, NumPy arrays, just like Scikit-Learn and many other Python-based libraries. This is a good option if your data fits in memory. Secondly, TensorFlow Dataset objects. This is a high-performance option that is more suitable for datasets that do not fit in memory and that are streamed from…