Keras Interview Questions

Checkout Vskills Interview questions with answers in Keras to prepare for your next job role. The questions are submitted by professionals to help you to prepare for the Interview.    

Q.1 What do you understand by Keras?
Keras is the high-level API of TensorFlow 2 and is an approachable, highly-productive interface for solving machine learning problems.
Q.2 What is the use of keras?
Keras allows users to productize deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. It also allows use of distributed training of deep-learning models on clusters of Graphics processing units (GPU) and tensor processing units (TPU).
Q.3 What is keras and TensorFlow?
Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. Keras is built in Python which makes it way more user-friendly than TensorFlow.
Q.4 Why keras is used in Python?
Keras is an API designed which offers consistent and simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
Q.5 Is Keras a Python library?
Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow. It was developed to make implementing deep learning models as fast and easy as possible for research and development.
Q.6 Is Keras good for research?
The Keras results are reliable enough to use as a benchmark for these purposes, and improving on them isn't considered.
Q.7 Why is TensorFlow used?
It is an open source artificial intelligence library, using data flow graphs to build models. It allows developers to create large-scale neural networks with many layers. TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation
Q.8 What is keras API?
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.
Q.9 What is CNN classifier?
Convolutional Neural Network (CNN) is a type of deep neural network primarily used in image classification and computer vision applications. A deep neural network can have two or more hidden layers. A convolutional neural network has convolution layers followed by a fully connected neural network.
Q.10 What is CNN model?
CNN is a type of deep learning model for processing data that has a grid pattern, such as images, which is inspired by the organization of animal visual cortex and designed to automatically and adaptively learn spatial hierarchies of features, from low- to high-level patterns.
Q.11 Is Keras sequential CNN?
Every Keras model is either built using the Sequential class, which represents a linear stack of layers, or the functional Model class, which is more customizeable. We'll be using the simpler Sequential model, since our CNN will be a linear stack of layers.
Q.12 What is ReLU in CNN?
The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero. The rectified linear activation is the default activation when developing multilayer Perceptron and convolutional neural networks.
Q.13 What is keras JSON file?
Once we execute keras, we could see the configuration file is located at home directory inside and it is .keras/keras. json.
Q.14 What is CNN in machine learning?
In deep learning, a convolutional neural network (CNN/ConvNet) is a class of deep neural networks, most commonly applied to analyze visual imagery. In mathematics convolution is a mathematical operation on two functions that produces a third function that expresses how the shape of one is modified by the other.
Q.15 What is conv layer?
Convolutional layers are the major building blocks used in convolutional neural networks. A convolution is the simple application of a filter to an input that results in an activation. The result is highly specific features that can be detected anywhere on input images.
Q.16 What is CNN filter?
In Convolutional Neural Networks, Filters detect spatial patterns such as edges in an image by detecting the changes in intensity values of the image. High pass filters are used to enhance the high-frequency parts of an image.
Q.17 Why is CNN used?
CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.
Q.18 What is dropout layer?
The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Note that the Dropout layer only applies when training is set to True such that no values are dropped during inference.
Q.19 What is Max pooling?
Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer.
Q.20 What is RELU in Python?
Relu or Rectified Linear Activation Function is the most common choice of activation function in the world of deep learning. Relu provides state of the art results and is computationally very efficient at the same time.
Q.21 What is Keras backend?
Keras is a model-level library, providing high-level building blocks for developing deep learning models. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the “backend engine” of Keras
Q.22 What is Keras sequential API?
The sequential API allows you to create models layer-by-layer for most problems. It is limited in that it does not allow you to create models that share layers or have multiple inputs or outputs. In this tutorial, you will discover how to use the more flexible functional API in Keras to define deep learning models.
Q.23 What is feature map?
The feature map is the output of one filter applied to the previous layer. A given filter is drawn across the entire previous layer, moved one pixel at a time. Each position results in an activation of the neuron and the output is collected in the feature map.
Q.24 What is padding in CNN?
Padding basically extends the area of an image in which a convolutional neural network processes. The kernel/filter which moves across the image scans each pixel and converts the image into a smaller image. Adding padding to an image processed by a CNN allows for a more accurate analysis of images.
Q.25 What is kernel in neural network?
In Convolutional neural network, the kernel is nothing but a filter that is used to extract the features from the images. The kernel is a matrix that moves over the input data, performs the dot product with the sub-region of input data, and gets the output as the matrix of dot products.
Q.26 What is difference between kernel and filter?
A “Kernel” refers to a 2D array of weights. The term “filter” is for 3D structures of multiple kernels stacked together. For a 2D filter, filter is same as kernel. But for a 3D filter and most convolutions in deep learning, a filter is a collection of kernels.
Q.27 What is a kernel size?
The kernel size here refers to the widthxheight of the filter mask. The max pooling layer, for example, returns the pixel with maximum value from a set of pixels within a mask (kernel). That kernel is swept across the input, subsampling it.
Q.28 What is difference between CNN and RNN?
The main difference between CNN and RNN is the ability to process temporal information or data that comes in sequences, such as a sentence for example. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series
Q.29 What is flatten in CNN?
Flattening is converting the data into a 1-dimensional array for inputting it to the next layer. We flatten the output of the convolutional layers to create a single long feature vector. And it is connected to the final classification model, which is called a fully-connected layer.
Q.30 What is K function keras?
It returns Tensorflow source code, which is actually quite short. The K.function() return a Function object which, when called, evaluates the outputs and updates using the inputs.
Q.31 What is input in Keras?
Input() is used to instantiate a Keras tensor. A Keras tensor is a symbolic tensor-like object, which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model.
Q.32 What is a layer in Keras?
Layers are the basic building blocks of neural networks in Keras. A layer consists of a tensor-in tensor-out computation function (the layer's call method) and some state, held in TensorFlow variables (the layer's weights).
Q.33 What is kernel size in keras?
The kernel size of a keras convolution layer is defined as height x width: kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window.
Q.34 What is LSTM layer?
A Stacked LSTM architecture can be defined as an LSTM model comprised of multiple LSTM layers. An LSTM layer above provides a sequence output rather than a single value output to the LSTM layer below. Specifically, one output per input time step, rather than one output time step for all input time steps
Q.35 What are units in keras?
Units defines the output shape i.e. the shape of the tensor that is produced by the layer and that will be the input of the next layer. Dense layers have the output based on the units.
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