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Table of contents
1.
Introduction
2.
Applications
3.
Frequently Asked Questions
4.
Key Takeaways
Last Updated: Mar 27, 2024

Applications of Keras

Author Rajkeshav
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Introduction

Sourcegithub.com

 

Keras is a powerful and easy-to-use free open-source python library for developing and evaluating deep learning models. It supports multiple backend neural network computations and makes implementing neural networks easy. Keras is a high-level API of TensorFlow too. It provides essential abstraction building blocks for developing and shipping machine learning solutions with high iteration velocity. Keras empowers engineers and researchers to take full advantage of TensorFlow's capability and cross-platform capability. 

 

Applications

Keras productize the deep models on smartphones. Deep models require a lot of computation power to run, but with the help of Keras, we can make deep models a product that can be executed on smartphones. 

 

Keras is used in the distributed training of deep learning models. Distributed training means that we can split our deep learning model into different parts and train it on systems all across the globe. This makes training of a deep learning model extremely fast. Along with saving time, we are also saving on the computational power of a system as it is not only our system that has to run such a heavy program. By distributing it across various systems, all the resources required to train a deep learning model go down significantly.

 

Keras provides deep learning models that are available with their pre-trained weights. After loading the models, the user can use them directly to make predictions, feature extraction, and fine-tuning. The pre-trained models are available in the 'application' module of Keras. Some available models are:

 

  1. VGG16
  2. VGG19
  3. InceptionV3
  4. MobileNet
  5. Resnet50, etc.

 

You can read the documentation of each model by visiting Documentation

 

These pre-trained models can be loaded as:

import keras. models
import numpy as np 

#The models are available in 'application' module
from keras.applications import vgg16, inception_v3, mobilenet 

#Loading the VGG model 
vgg_model = vgg16.VGG16(weights = 'imagenet') 

#Loading the Inception_V3 model  
inception_model = inception_v3.InceptionV3(weights = 'imagenet') 

#Loading the MobileNet model 
mobilenet_model = mobilenet.MobileNet(weights = 'imagenet')

 

Now we can use it for prediction, feature extraction, and fine-tuning.

 

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Frequently Asked Questions

  1. What is Keras?
    Keras is a powerful and easy-to-use free open-source python library for developing and evaluating deep learning models.
     
  2. What is TensorFlow?
    TensorFlow is an open-source library for numerical computations and a large-scale machine learning platform.
     
  3. How does Keras productize the Deep learning model in Smartphones?
    Deep models require a lot of computation power to run, but with the help of Keras, we can make deep models a product that can be executed on Smartphones. 
     
  4. What are the layers of Convolutional neural networks?
    There are three layers of Convolutional neural networks
    1) Convolution layer
    2) Pooling layer 
    3) Fully connected layer 
     
  5. What is the role of the Convolution layers in Convolutional neural networks?
    Convolution layers in Convolutional neural networks extract features from the input images. Convolution is a linear operation involving the multiplication of the weights with input.

 

Key Takeaways

This brings us to the end of the discussion on Keras. I hope the information is helpful on the journey to learn about Keras.

To learn more about Keras and related topics we can check out the link- Machine Learning.                            

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