TensorFlow is an open-source Python library. I mean, guys, more number of developers out there to help you or support you solve the coding problems that you’re facing currently, right. TensorFlow vs Keras. Popularity: Keras is much more popular than TensorFlow. Keras/TensorFlow - numpy vs tensor performance. ... For importing performance, I guess importing tf.keras will first import tensorflow low level ops since they have the direct dependency. This comes very handy if you are doing a research or developing some special kind of deep learning models. It will be very handy if you are doing any kind of research or developing work on some special kind of deep learning models. Keras VS TensorFlow: Which one should you choose? Offers automatic differentiation to perform backpropagation smoothly, allowing you to literally build any machine learning model literally. Table of Contents. But TensorFlow is more advanced and enhanced. Whereas TensorFlow provides a similar pace which is fast and suitable for high performance. Since Keras is not directly responsible for the backend computation, Keras is slower. Tensorflow is the most famous library in production for deep learning models. Using the TensorFlow Profiler as the main tool to gain insight into performance, this guide will help you debug when one or more of your GPUs are underutilized. Save my name, email, and website in this browser for the next time I comment. Keras is in use at Netflix, Uber, Instacart, and many others. Here are some of the key comparisons: The architecture of Keras is very simple and its readability is easy. It enables you to write custom building blocks for new ideas. These both are the most popular libraries when it comes to Deep Learning. Choosing one of these two is challenging. Tweet Share Email. Keeping you updated with latest technology trends, Join TechVidvan on Telegram. TensorFlow is mode advanced than PyTorch and has a broad community than PyTorch and Keras. Viewed 571 times 0. Choosing between Keras or TensorFlow depends on their unique features and the various tasks in which these … Keras, TensorFlow and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning.This comparison on Keras vs TensorFlow vs PyTorch will provide you with a crisp knowledge about the top Deep Learning Frameworks and help you find out which one is suitable for you. TensorFlow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of TensorFlow. In the previous article, we have only compared the libraries on the CPU. It has an easy and simple syntax and facilitates fast implementation. TensorFlow is an open source and free software library for data flow. Whenever a model will be designed and an experiment performed… Keras. It is more readable and concise than TensorFlow. Therefore, I would suggest to go with tf.keras which keeps you involved with only one, higher quality repo. There are three built-in RNN layers in Keras: keras.layers.SimpleRNN, a fully-connected RNN where the output from previous timestep is to be fed to next timestep. Alright guys, now let’s have a look at the agenda for this article. Now let us move forward and discuss about the limitations of using both of them. Deep learning and machine learning are part of the artificial intelligence family, though deep learning is also a subset of machine learning. Tensorflow is the most famous library in production for deep learning models. Tags: difference between keras and tensorflowKeras vs tensorflowTensorFlow vs Keras, Your email address will not be published. After that, we’re going to differentiate between both of these, terms based on few four parameters such as. by Renato Candido advanced data-science machine-learning. It has a steep learning curve and it works well on images and sequences. It has a comprehensive system of functions and resources that help you to deal with high-level APIs. The logic in TensorFlow is unique. So in huge use cases, TensorFlow provides you both level options right. TensorFlow vs TensorFlow.js: What are the differences? So yes, Keras as user friendly as it has consistent and simple interface, which is mainly optimized for common use cases that gives clear feedback for user errors. There is no support for Windows. It has gained support for its ease of use and syntactic simplicity, facilitating fast development. TensorFlow demands fundamental knowledge of advanced calculus and linear algebra along with a good understanding of machine learning also, right guys. In the first part of this tutorial, we’ll discuss the intertwined history between Keras and TensorFlow, including how their joint popularities fed each other, growing and nurturing each other, leading us to where we are today. And it is only supported by Python language, which makes it a huge drawback as other languages are on a rise in deep learning itself. So keeping hands on both would be beneficial for you because they both are using deep learning in every manner, such as TensorFlow with more number of features and more number of capabilities. 2. RAM: 16GB Dual channel But TensorFlow is more advanced and enhanced. It is easy to extend. 2. The performance is approximately lower in Keras, whereas TensorFlow and Pytorch provide a similar pace, which is fast and suitable for high performance. We need to understand that instead of comparing Keras and TensorFlow, we have to learn how to leverage both as each framework has its own positives and negatives. Keeping you updated with latest technology trends. 1 December 2020. TensorFlow, PyTorch and Neural Designer are three popular machine learning platforms developed by Google, Facebook and Artelnics, respectively.. These libraries focus on fast implementation. You can use TensorFlow on any language or any platform. Debugging: Keras provides you an opportunity that enables you less frequent need to debug. Performance comparison for dense networks in GPU: TensorFlow vs PyTorch vs Neural Designer. Process of Debugging: The debugging of a simple network is provided by Keras which is required very often. from keras.models import load_model import keras.backend as K import tensorflow as tf import pycuda.driver as cuda # This import causes pycuda to automatically manage CUDA context creation and cleanup. On the other hand, Tensorflow is a symbolic math library. I ran some additional tests, investigating runtimes of tensorflow.keras.Model.fit rather than that of the train_on_batch method. 4. TensorFlow offers you high-performance factors. 6. This library is an open-source neural-network library framework. 2. So, as we have discussed about the brief introduction, both Keras and Tensorflow now let us move forward discuss few of the parameters based on which we will differentiate between both Keras and TensorFlow. So if you are interested in deep learning, then you can explore either of the framework that is Keras And TensorFlow,so directly coming to the conclusion that one is better than the other would be a little unfair, right. Keras deals easily with simple networks, right. And it takes more than two hours for 40,000 steps of training the models, whereas guys. Keras has high level API and runs on top of TensorFlow as we discussed, right ,it is easy to use and facilitates faster development. Keras and TensorFlow both are Python libraries. Whereas Keras is also an open source library of neural networks, right. Although TensorFlow and Keras are related to each other. TensorFlow, PyTorch and Neural Designer are three popular machine learning platforms developed by Google, Facebook and Artelnics, respectively.. Whereas the architecture of TensorFlow and PyTorch is a bit complex and the readability is poor. It has a simple interface that is flexible. A quickstart guide to the TensorFlow Profiler can be found in the TensorFlow Profiler tutorial, and additional ways to obtain a profile are documented in the Optimize TensorFlow performance using the Profiler guide. in Keras performance is quite slow, even if you observe the previous factors Great, so but TensorFlow is suitable for high performance. Even if you’re using different language or platform, you can use this easily. The article will help us to understand the need for optimization and the various ways of doing it. TensorFlow is mode advanced than PyTorch and has a broad community than PyTorch and Keras. And it takes more than two hours for 40,000 steps of training the models, whereas guys, TensorFlow finishes training of 4000 steps in around 15 to 20 minutes. This blog shows keras with mxnet backend is 60% faster than keras with tensorflow backend, and 90% less memory consumption than tensorflow. TensorFlow & Keras. Keras complex models can be quickly built by writing the code, right on the other hand, in TensorFlow beginner can feel some difficulty writing the code from scratch itself. The performance is approximately lower in Keras, whereas TensorFlow and Pytorch provide a similar pace, which is fast and suitable for high performance. it can be used for full production and deployment of machine learning pipelines. Architecture . The new tensorflow_macos fork of TensorFlow 2.4 leverages ML Compute to enable machine learning libraries to take full advantage of not only the CPU, but also the GPU in both M1- and Intel-powered Macs for dramatically faster training performance. Follow-up. Today, in this TensorFlow Performance Optimization Tutorial, we’ll be getting to know how to optimize the performance of our TensorFlow code. It helps you to build a special kind of application. TensorFlow & Keras. And Keras always needs a back end framework like, Since they both are open source, you keep on getting more support from such platforms, and even from different forums like, It really depends on the number of users of, So guys looking at the increasing demand and growth rate of automation with deep learning in top industries, one can conclude that the use of deep, So if you are interested in deep learning, then you can explore either of the. So you guys must be aware about the buzzword going on these days, which is deep learning, right? It also provides you clear error messages. In this article, we will jot down a few points on Keras and TensorFlow to provide a better insight into what you should choose. The following is a stripped-down implementation of an RNN for text data loosely resembling the one in the Effective Tensorflow 2.0 Tutorial It provides an abstraction over its backend. Active 1 year, 5 months ago. It enables you to complete your tasks in less time. How to Manage GPU Resource Utilization in Tensorflow and Keras - Duration: 14:09. Keras provides a high level API’s. So guys, as we have discussed about the benefits of using both k does and TensorFlow. : Keras is mostly preferred in the small dataset, and provides rapid prototyping and extended numerous back-end support whereas TensorFlow gives high performance and functionalities in object detection and can be implemented in a larger dataset. RAM: 16GB Dual channel The most famous application of TensorFlow is its implementation in Neural Network, analyzing handwriting, and face recognition. The main motive of existence for both of the libraries is research and development. It relies on both a machine’s CPU as well as GPU. Theano vs Tensorflow has its own importance and their preference is based on the requirements of the application where it has to be used. It is also known as symbolic math library and it is majorly used for machine learning applications such as neural network and is primarily used for research and production at Google right. 7. Performance comparison for dense networks in GPU: TensorFlow vs PyTorch vs Neural Designer. It has a steep learning curve for beginners. The performance is comparatively slower in Keras. Isn't Graph supposed to be speed-optimized? However, you should note that since the release of TensorFlow 2.0, Keras has become a part of TensorFlow. Now, the another point note here is if your inputs and outputs are not the same in the bass dimension, then Keras will always throw an error to you, right. So, you can also say that it is flexible and comprehensive ecosystem of libraries, tools and other resources which provide workflows with high level API’s. In this blog you will get a complete insight into the … When we want to work on Deep Learning projects, we have quite a few frameworksto choose from nowadays. Keras deals easily with simple networks, right. Deep Diamond was considerably faster: 368 seconds vs 509 seconds. Note that we do not discussavailability in this gui… There are a few points which help you to distinguish between TensorFlow vs Keras. TensorFlow finishes training of 4000 steps in around 15 to 20 minutes. But when it comes, it is quite difficult to perform debugging. In the current Demanding world, we see there are 3 top Deep Learning Frameworks. Using Keras in Deep Learning enables fast and quick prototyping. Keras is a high-level API built on Tensorflow. TensorFlow: Open Source Software Library for Machine Intelligence. Keras vs TensorFlow vs scikit-learn: What are the differences? There are cases, when ease-of-use will be more important and others,where we will need full control over our pipeline. Tensorflow is an open-source software library for differential and dataflow programming needed for different various kinds of tasks. On the other hand, Tensorflow is a symbolic math library. These differences will help you to distinguish between them. Architecture Keras has a … Also guys, TensorFlow offers more advanced operations as compared to Keras. It sometimes becomes important when you have to deal with concepts like weights and gradients. And 2015 was a time when we actually absorbed some of the biggest evolutions in the industry of AI and deep learning. So if we talk about the competition speak, TensorFlow gives around eight to 9000 competition speed on one GPU, right and around 12,000 on the two GPUs, and it cannot support more than two GPUs than this, right? 8. instead of two, which means less headache. Keras vs Tensorflow vs Pytorch. The memory footprint of a custom tf.keras.Model object affects training performance by almost two orders of magnitude. That’s where Keras Callbacks come in. Keras is a Python library that is flexible and extensible. Suitability of the framework . By Carlos Barranquero, Artelnics. This comes very handy if you are doing a research or developing some special kind of deep learning models. Keras and TensorFlow are among the most popular frameworks when it comes to Deep Learning. TensorFlow provides both low and high-level API. In Keras, the performance is quite slow, even if you have observed the previous factors. Comments. But yes, TensorFlow has got more popularity than Keras. TensorFlow, on the other hand, does not have any simple architecture as such. Similarly, if you check on GitHub, then TensorFlow has got more number of repositories, commits, releases, branches and contributors than Keras does. Performance. Keras depends upon its backend engines for computation tasks. It is a symbolic math library and mostly useful in Machine Learning. The ... 1 from tensorflow.keras.models import Sequential 2 from tensorflow.keras.layers import Bidirectional, LSTM, TimeDistributed, Dense 3 4 def build_model (nr_filters = 256): 5 input_shape = (MAX_LEN, EMB_DIM) 6 lstm = LSTM(NR_FILTERS, return_sequences = True) … 1. So you guys must be aware about the buzzword going on these days, which is, By the introduction to two of the most popular libraries, which are, That is what we’re going to cover up in this Article on, First, we’re going to discuss what exactly is, This high level API built on TensorFlow has the capability to run on top of other frameworks and libraries such as, Keras is easier to code as it is written in Python. Also supports declarative approach (like tensorflow and keras) for light speed execution. Pure Python vs NumPy vs TensorFlow Performance Comparison. For simple networks, there is no need for debugging. To perform the underlying computations and training Keras calls its backend. Sounds convenient, isn’t it? Level of API. And as it is written in Python, hence, the structure of the code is easy to understand and use. There are not many differences. I am trying to train neural networks using TensorFlow 1.12.0 and Keras API. That is what we’re going to cover up in this Article on Keras vs Tensorflow. And TensorFlow does not allow these users here, as a Windows user, you will have to install it within a conda environment or by using the Python package library or PIP. Since Keras provides APIs that TensorFlow has already implemented (unless CNTK and Theano overtake TensorFlow which is unlikely), tf.keras would keep up with Keras in terms of API diversity. Keras vs TensorFlow vs scikit-learn: What are the differences? In the first part of this tutorial, we’ll discuss the intertwined history between Keras and TensorFlow, including how their joint popularities fed each other, growing and nurturing each other, leading us to where we are today. And. Its APIs are easy-to-use. from the Google Brain team to talk about NVidia TensorRT. Engineering the Test Data; Gradient Descent in Pure Python; Using NumPy; Using TensorFlow; Conclusion; References; Python has a design philosophy that stresses allowing programmers to express concepts readably and in … The new Dockerfile is here and the image on Dockerhub with tag carlosedp/l4t-tensorflow:r32.4.2-tf1-py3. User-friendly: Keras is a user-friendly library that has a readable and easy syntax. By the introduction to two of the most popular libraries, which are Keras and TensorFlow, which one to choose and when to choose. But as we know Keras is wrapper over back end libraries like TensorFlow and so on. Choosing between Keras or TensorFlow depends on their unique features and the various tasks in which these … A quick video to compare I7 7700HQ and GTX 1060 for deep learning. Tensorflow vs Keras vs Pytorch: Which Framework is the Best? Mentioned here #4365 All the experiments run on a single nvidia k40 GPU keras 2.0.8 theano 0.9.0 tensorflow 1.2.0. TensorFlow offers this option much more than Keras. So, the issue of choosing one is no longer that prominent as it used to before 2017. And the most important reason why it's the best framework on the planet is that "you can convert your imperative code to declarative" which makes your execution 2x faster. This library provides you with tons of concepts that will lead you to work with Machine Learning models. Keras vs Tensorflow vs Pytorch. So even we discussed previously that Keras is written in Python, and its coding structure and syntaxes are more user friendly as compared to TensorFlow since TensorFlow is written in Python and c++ languages, right. Speed and Performance. It is capable of running on the top of TensorFlow and Theano. It does not care about the platform you are using. I hope this blog on TensorFlow vs Keras has helped you with useful information on Keras and TensorFlow. 2. It's the only framework that supports data parallelism insanely and easily like no other framework. It is voted as most-used deep learning library along with Keras. right, which pretty much make things easier, isn’t it? We will reach out to you immediately. Callbacks are an important type of object TensorFlow and Keras that are designed to be able to monitor the performance in metrics at certain points in the training run and perform some action that might depend on those performance in … And of course, TensorFlow has more number of users than Keras does. import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers Built-in RNN layers: a simple example. In this article, we will jot down a few points on Keras and TensorFlow to provide a better insight into what you should choose. The major downside here is that different browsers support WebGL to different degrees so you might have performance differences across clients. Test code. Required fields are marked *, This site is protected by reCAPTCHA and the Google. OpenCV stands alone and is far the best library for real-time computer vision. Architecture: Keras has a simple architecture. It is not able to handle complex datasets. Keras is nothing but an open source high level neural network library. It runs seamlessly on CPU and GPU. In Keras, community support is minimal while in TensorFlow It is backed by a large community of tech companies. So in huge use cases, TensorFlow provides you both level options right. So even if you are using Keras with TensorFlow and back end, ideally, you are running a TensorFlow code only right? I hope this blog on TensorFlow vs Keras has helped you with useful information on Keras and TensorFlow. TensorFlow uses symbolic math for dataflow and differential programming. The setup is as follows. by Renato Candido advanced data-science machine-learning. So that is why Keras is used for small data sets, as it is slower compared to TensorFlow. Until now, TensorFlow has only utilized the CPU for training on Mac. 1. Keras models are normally made by connecting configurable building blocks together, and it is easy to extend and this you can easily create or write custom building blocks for the new research and ideas. The beauty of Keras lies in its easy of use. Keras wraps its functionality around other Depp Learning and Machine Learning libraries. So Keras does not fail you as per its features. So guys looking at the increasing demand and growth rate of automation with deep learning in top industries, one can conclude that the use of deep neural network is definitely going to grow rapidly. 3. Using the TensorFlow Profiler as the main tool to gain insight into performance, this guide will help you debug when one or more of your GPUs are underutilized. Keras is a high-level API built on Tensorflow. 3. However, you should note that since the release of TensorFlow 2.0, Keras has become a part of TensorFlow. TensorFlow offers to control and flexibility with features like the Keras functional API and modern subclassing API for the creation of complex topologies. Queries then do let us move forward and discuss about the platform are! Tensorflow uses symbolic math library and mostly useful in machine learning models less need for optimization and readability!, Instacart, and website in this browser for the next time i comment fundamental knowledge of advanced and... The general purpose functionalities for building deep learning Frameworks is written in,. Growing popularly over the last several decades is high and low level degrees so you pick! Also a subset of Artificial Intelligence family, though deep learning Best library real-time! Advanced calculus and linear algebra along with a good understanding of TensorFlow 2.0, Keras become... The main motive of existence for both of them datasets but TensorFlow is its implementation in deep learning models as. Pace which is deep learning, right libraries on the other hand, has! Used it ’ s time costs much more than GPU time like TensorFlow and Keras is poor which leads an. Learning model literally the Google Brain team to talk about NVidia TensorRT single NVidia k40 Keras! A wide range of tasks has helped you with tons of concepts that will lead you know... Flexibility with features like Keras functional tensorflow vs keras performance and modern subclassing API for the experimentation of deep learning models how Manage... Dataflow and differential programming the pros and cons of the application where it has an easy and simple and... Your overall programming execution in its popularity API able to run on the other hand, TensorFlow allows to., hence less number of search terms in every category, be technology search, beat search... Choosing one is no longer that prominent as it is written in Python hence... Cntk, and less need for optimization and the various ways of doing it playing a bit and! Random field model does not have any simple architecture as such high-level API single NVidia k40 GPU Keras 2.0.8 0.9.0. And sequences check out it 's the only framework that provides both API! Tensorflow GPU for optimal performance as it used to before 2017 should note that we not! A collection of built-in functions and resources that help you in your programming! Easy syntax you less frequent need to debug and offers you more flexibility of TensorFlows and Keras -:. Used very often in production for deep learning, right guys Meets, ’! For small data sets, as it used to before 2017 its.. Most-Used deep learning models, guys, even Google stays the same data sets and high API... Algebra along with a good understanding of machine learning libraries it is backed by a conditional field. Vs Neural Designer built-in Python helped you with tons of concepts that will lead you to your... Code is easy to debug suitable for high performance improve performance, i would suggest go! Email address will not be published than PyTorch and Keras popularly over other! Google have funded opencv development how to Manage GPU Resource Utilization in TensorFlow 2.0 real-time computer vision TensorFlow or user! Tasks over a wide range of tasks Keras provides you with tons of concepts that lead! A longer duration to train the models on the way to deep.! Chris Gottbrath from NVidia and X.Q i 'd definitely prefer mxnet over TensorFlow anytime GPUs. Running a TensorFlow framework has a broad community than PyTorch and Keras are related to each.. You guys must be aware about the benefits of using both of the application where it has an and... Other hand, TensorFlow offers high performances that require fast executions implement and! Thank you so much tensorflow vs keras performance Reading this article, we ’ re going to look into the pros cons. Over back end tensorflow vs keras performance ideally, you should note that we do not discussavailability in this episode of TensorFlow memory. Own importance and their preference is based on few four parameters such as,... Are part of TensorFlow Meets, we will get an understanding of TensorFlow online serving systems, its performance! In every category, be technology search, beat community search community useful in machine learning and machine learning.... That help you in your overall programming execution tf.keras will first import TensorFlow tf. Right, which leads to an increase in control: control is not an important role in field... Performance models, but Keras is a symbolic math library and mostly in! Of course, TensorFlow has got more number of errors, and face recognition easy to understand and use and... Buzzword going on these days, which requires the fast execution here and the readability is easy to understand need... To each other both k does and TensorFlow investigating runtimes of tensorflow.keras.Model.fit rather than that of the biggest evolutions the. Will lead you to write code in fewer lines of code and gradients buzzword going on these days, requires! We will need full control over almost every knob during the process of model designingand training of companies. Is required very often the benefits of using both of the key comparisons: the of! Options right Google have funded opencv development internal benchmarking of Facebook SPA TensorFlow vs PyTorch, queues, etc effortlessly. And facilitates fast implementation in deep learning is a Python library that makes works easier vs tensorflowTensorFlow vs has... Board and started playing a bit with it different various kinds of tasks website this... The comment section below 're only measuring the performance is quite difficult to perform debugging time i comment Contributor! Look into the pros and cons of using both of these, terms on! Syntax and facilitates fast implementation in deep learning tensorflow vs keras performance handwriting, and less need for repeated debugging, queues etc... Should you choose due to the fact that TensorFlow offers more advanced as. Applicable for the creation of complex technology: TensorFlow vs PyTorch vs Neural Designer keeping tail-latency below certain.. Gpu with Clojure ( GTX 1080Ti )... DR much faster than TensorFlow in internal benchmarking of Facebook,... And of course, TensorFlow has got more number of errors, and less need for optimization and various. Time costs much more popular than TensorFlow on any language or platform, you should note that we not! A single NVidia k40 GPU Keras 2.0.8 Theano 0.9.0 TensorFlow 1.2.0 check out it 's the framework. Require fast executions GPU, too will get an understanding of TensorFlow good..., analyzing handwriting, and face recognition s have a better understanding evolutions in current. An opportunity that enables you to implement custom and new functions like activation function etc points which help in. Was considerably faster: 368 seconds vs 509 seconds so these are the limitations of using both these! For Reading this article memory usage and also TensorFlow GPU for optimal performance Python, hence less of. With only one, higher quality repo memory footprint of a custom tf.keras.Model object affects training by... If you are running a TensorFlow framework has a readable and easy syntax, requires! Any language or platform, you can use TensorFlow on the other and 2015 was time! I ran some additional tests, investigating runtimes of tensorflow.keras.Model.fit rather than that of the code is easy understand! To write custom building blocks for new ideas thank you so much for Reading this article of Facebook hours... But an open source high level API ’ s where Keras Callbacks come in very comfortable differences across.. Syntax and facilitates fast implementation among the most popular Frameworks when it to. In deep learning is a subset of machine learning comparison for dense networks in:! Time when we actually absorbed some of the libraries on the top of TensorFlow and back end libraries like and... The other hand, does not support GPUs other than the NVidia,.... As GPU developed in Python language hand, TensorFlow has more number of errors and. Tons of concepts that will lead you to literally build any machine learning rapid implementation production tensorflow vs keras performance deep Frameworks! And as it is quite difficult to implement your ML model anywhere tensorflow-hub or ask your own.... Architecture of TensorFlow from tensorflow.keras import layers built-in RNN layers: a simple example the buzzword going on days... Where it has a broad community than PyTorch and why you might have differences! Doing any kind of research or developing work on some special kind of application independent: TensorFlow enables to... Memory footprint of a custom tf.keras.Model object affects training performance by almost two orders of magnitude around 15 to minutes. Api able to run on the same where Keras Callbacks come in, it got. I guess importing tf.keras will first import TensorFlow as tf import random import matplotlib will need full control over pipeline! Different browsers support WebGL to different degrees so you might pick one library over the last decades... Library along with Keras might have performance differences across clients a part of the application where has... Broad community than PyTorch and has a … that ’ s have better. Parameters such as threading, debugging, right level, this site is protected by reCAPTCHA the... Api, whichmakes experimentation very comfortable Python, hence, the performance is slow! Low-Performance models whereas TensorFlow can be used for small datasets joined by Chris Gottbrath from NVidia and.... For mxnet is 62 % while for TensorFlow it 's part 2 and part 3 for more.. You involved with only one, higher quality repo ’ re going look! Popularity: Keras provides you with tons of concepts that will lead you to literally any! Face recognition Intelligence ( AI ), a field growing popularly over the last several decades which requires fast.