Pytorch vs TensorFlow vs Keras | Which is Better | Deep Learning Frameworks Comparison | Simplilearn
Summary
TLDRThis video offers a comprehensive comparison between TensorFlow, Keras, and PyTorch, three prominent machine learning frameworks. It begins with an introduction to each platform, highlighting TensorFlow's low-level capabilities and Keras' high-level API simplicity. The script delves into differences in API levels, speed, architecture, data handling, and ease of development and deployment. The conclusion suggests TensorFlow as the preferred choice for production due to its flexibility and deployment features, while Keras is recommended for beginners and PyTorch for research and smaller models.
Takeaways
- 📚 TensorFlow is a low-level, free and open-source software library by Google, designed for machine learning and deep neural networks.
- 🌐 Keras is a high-level deep learning API that provides a user-friendly interface for TensorFlow, enabling fast experimentation with neural networks.
- 🔧 PyTorch is a low-level API developed by Facebook, known for its flexibility and powerful capabilities in natural language processing and computer vision.
- 🔑 APIs can be categorized as low-level, offering detailed control, and high-level, offering simplicity and more functionality with less code.
- ⚡ TensorFlow is known for its high performance speed, while Keras is slower due to its abstraction layer on top of TensorFlow.
- 🛠️ TensorFlow's complex architecture can be challenging, whereas Keras offers a simpler, more approachable structure for beginners.
- 🔍 PyTorch, while powerful, has a steeper learning curve compared to Keras but is easier to debug and work with for complex tasks.
- 📈 TensorFlow excels in handling large datasets and offers robust deployment options like TensorFlow Serving for production environments.
- 🚀 Keras is ideal for small datasets and rapid development, providing a high level of abstraction that simplifies model implementation.
- 🛡️ PyTorch Mobile facilitates easy deployment on mobile devices, supporting an end-to-end workflow within the PyTorch ecosystem.
- 🏆 The recommendation leans towards TensorFlow for its wide usage in production and flexibility, despite PyTorch's advantages in research and development.
Q & A
What is TensorFlow?
-TensorFlow is a low-level software library created by Google for implementing machine learning models and solving complex numerical problems. It is a free, open-source software library primarily focused on training and inference of deep neural networks and is based on data flow and differential programming.
What are the key features of TensorFlow's architecture?
-TensorFlow's architecture is based on a computational graph where variables are called tensors and mathematical operations are called operators. It is complex and hard to interpret but offers amazing computational ability across platforms.
What is Keras and how does it relate to TensorFlow?
-Keras is a high-level deep learning API written in Python for easy implementation and computation of neural networks. It acts as an interface for the TensorFlow library, providing a user-friendly, modular, and extensible approach to solving machine learning problems with high iteration velocity.
How does Keras simplify the use of TensorFlow?
-Keras provides essential abstractions and building blocks for developing and shipping machine learning solutions, reducing the need to work with low-level operations such as tensor products and convolution. It is designed to enable fast experimentation with deep neural networks.
What is PyTorch and who developed it?
-PyTorch is a low-level API developed by Facebook for natural language processing and computer vision. It is an open-source machine learning library based on the Torch library and emphasizes flexibility, allowing deep learning models to be expressed in basic Python.
How does PyTorch differ from TensorFlow in terms of ease of use?
-PyTorch is easier than TensorFlow for beginners but still comparatively hard. It is not very easy to learn for those new to machine learning but is significantly more powerful than just plain Keras.
What is the main difference between a low-level API and a high-level API?
-A low-level API is more detailed, allowing for more control and manipulation of functions, while a high-level API is more generic and simple, providing more functionality with fewer commands. High-level APIs are generally easier to learn and implement models with.
Which framework is suggested for use in the script and why?
-The script suggests using TensorFlow due to its wide use in day-to-day production, extensions for deployment on servers and mobile devices, and the lack of Python overhead. It is preferred for companies working with deep learning models.
How does TensorFlow facilitate model deployment?
-TensorFlow facilitates model deployment through TensorFlow Serving, a flexible, high-performance serving system for machine learning models designed for production environments. It allows for easy deployment of new algorithms and experiments while maintaining the same server architecture and APIs.
What is the role of tensors in TensorFlow?
-In TensorFlow, tensors are multi-dimensional arrays with a uniform type. They are immutable, meaning their contents cannot be updated but rather a new tensor must be created. Tensors represent the variables in the computational graph.
How does PyTorch Mobile simplify the deployment process for mobile devices?
-PyTorch Mobile allows a seamless process from training to deployment by staying entirely within the PyTorch ecosystem. It provides an end-to-end workflow that simplifies the research to production environment for mobile devices and supports privacy-preserving features via federated learning techniques.
Outlines
🤖 Introduction to Machine Learning Frameworks
This paragraph introduces the video's focus on comparing three machine learning platforms: TensorFlow, Keras, and PyTorch. It outlines the structure of the video, which includes a brief introduction to each platform, a comparison based on various criteria such as API level, speed, architecture, data sets, debugging, ease of deployment, and development, and concludes with a recommendation on which framework to use. TensorFlow is described as a low-level library by Google for implementing machine learning models, emphasizing its symbolic math and data flow approach. Keras is introduced as a high-level API for neural networks, simplifying TensorFlow's operations. PyTorch is mentioned as Facebook's low-level API for NLP and computer vision tasks, highlighting its flexibility and Python interface.
🔍 Differences Among TensorFlow, Keras, and PyTorch
The second paragraph delves into the distinctions between the three frameworks. It discusses the concept of API levels, with TensorFlow offering both high and low-level APIs, Keras being a high-level API that simplifies TensorFlow operations, and PyTorch as a low-level API. The paragraph compares their speeds, with TensorFlow and PyTorch being faster due to their low-level nature, while Keras is slower due to its additional abstractions. The complexity of their architectures is also explored, with TensorFlow being challenging to use and PyTorch being less readable than Keras. The paragraph further covers their capabilities with data sets and debugging, ease of development, and deployment, highlighting TensorFlow's robustness in production environments and PyTorch's ease of deployment with i Torch Mobile.
🏆 Choosing the Right Framework for Machine Learning
In the final paragraph, the video script wraps up with recommendations on which framework to use based on the discussed criteria. It acknowledges TensorFlow's various levels of abstraction that facilitate deep learning implementation and debugging. Keras is praised for its simplicity and user-friendliness, making it ideal for beginners. PyTorch is recognized for its preference among teachers and its speed, despite having lower GPU utilization. The video concludes by suggesting TensorFlow as the preferred option for production due to its wide usage, deployment extensions, and visualization features. It also touches on the suitability of Keras for beginners and TensorFlow for research work due to its flexibility, ending the video with a prompt to visit the Simply Learn website for more information and to subscribe to their YouTube channel for further learning.
Mindmap
Keywords
💡Keras
💡TensorFlow
💡PyTorch
💡API
💡Tensor
💡Computational Graph
💡Ease of Development
💡Deployment
💡Debugging
💡Data Flow
💡High-Level API
Highlights
Introduction to TensorFlow, Keras, and PyTorch platforms.
Explanation of TensorFlow as a low-level library for machine learning created by Google.
TensorFlow's focus on training and inference of deep neural networks.
Description of TensorFlow's data flow and differential programming.
Definition and role of tensors in TensorFlow.
Overview of Keras as a high-level deep learning API for neural networks.
Keras as an interface for TensorFlow, simplifying operations.
Introduction to PyTorch as a low-level API developed by Facebook.
PyTorch's emphasis on flexibility and use in natural language processing and computer vision.
Differences in API levels between TensorFlow, Keras, and PyTorch.
Comparison of computational speeds among the three platforms.
Analysis of the complexity and ease of use in TensorFlow's architecture.
Keras's simpler architecture and ease of development for beginners.
PyTorch's complexity and suitability for advanced users and researchers.
Discussion on data set handling and debugging in TensorFlow, Keras, and PyTorch.
Ease of deployment with TensorFlow Serving and other platforms.
PyTorch Mobile's role in simplifying deployment on mobile devices.
Recommendation of TensorFlow for production environments and Keras for beginners.
PyTorch's preference for researchers and smaller scale models.
Final recommendation based on the needs of the user and the specific use case.
Transcripts
hello everyone and welcome to this video
on keras versus tensorflow versus pie
torch so what can you expect from this
video what's in it for you
first we will introduce you to each of
these platforms tensorflow keras and
pytorch in brief
then we will look at how these platforms
differ from each other based on certain
criterias such as level of api speed
architecture data sets and debugging
ease of deployment and ease of
development and finally we will wrap up
this video by seeing which framework you
should use so let's get started before
we can see the differences between these
platforms we first need to know what
exactly each of these platforms are
let's start with tensorflow
what is tensorflow tensorflow is a
low-level software library which is
created by google to help implement
machine learning models and to solve
complex numerical problems
tensorflow is nothing but a free and
open source software library for machine
learning it can be used across a range
of tasks but has a particular focus on
training and inference of deep neural
networks
tensorflow is a symbolic math library
based on data flow and differential
programming it is used for both research
and production at google
what do you mean by data flow it
basically means that we perform
calculations by converting every element
into graphical form the variables of the
graph are called tensors and
mathematical operations are called
operators
here in the computational graph shown
you can see that x y and 2 are the
variables they will also be called
tensors and division multiplication and
addition are the operators
this graph basically shows us the
calculation that is going to occur in a
machine learning model
where x and y are going to be divided
and y and 2 are going to be multiplied
the results of these two calculations
are then going to be added to give us
the final output i told you that x y and
2 are also called tensors
what exactly are tensors tensors are
multi-dimensional arrays with a uniform
type all tensors are immutable like
python numbers and strings which means
that you cannot update the contents of a
tensor you can only create a new tensor
next let's look at the api keras
what exactly is keras keras is a high
level deep learning api written in
python for easy implementation and
computation of neural networks keras is
an open source software library that
provides a tensorflow interface for
artificial neural networks
keras acts as an interface for the
tensorflow library which means that it
runs on top of tensorflow
up until version 2.3 keras supported
multiple back-ends
including tensorflow microsoft cognitive
toolkit
as of version 2.4 only tensorflow is
supported
as a version 2.4 only tensorflow is
supported
designed to help enable fast
experimentation with deep neural
networks in it focuses on being user
friendly modular and extensible
keras is a high level api of tensorflow
an approachable highly productive
interface for solving machine learning
problems with the focus on modern deep
learning it provides essential
abstractions and building blocks for
developing
and shipping machine learning solutions
with high iteration velocity keras does
not perform its own low level operations
such as tensor products and convolution
it relies on back end engines for that
even though keras supports multiple
back-end engines its primary back-end
engine is tensorflow and its primary
supporter is google which means that
keras acts as nothing more but a wrapper
class around tensorflow theano cntk
blade ml or mxnet
which are low level apis next we will
look at pi torch what exactly is pytoch
pytoch is a low level api which is
developed by facebook for natural
language processing and computer vision
it is a more powerful version of numpy
it is an open source machine learning
library based on the torch library used
for applications such as computer vision
and natural language processing
primarily developed by facebook's ai
research lab it is free and open source
software released under the modified bsd
license although the python interface is
a more polished and the primary focus of
development pytorch also has a c plus
plus interface python is a widely liked
language because it is easy to
understand and write pytorch emphasizes
flexibility and allows deep learning
models to be expressed in basic python
pytorch is mainly used for natural
language processing
and for computer vision
now let's move on to the differences
between tensorflow keras and pytorch
the first difference that we'll be
looking at is called level of api there
are two main types of apis a low level
api and a high level api api stands for
application programming interface
a low level application programming
interface is generally more detailed and
allows you to have
more detailed control to manipulate
functions within them on how to use and
implement them
while a high level api is more generic
and simple and provides more
functionality with one command
statements than a lower level api
high level interfaces are comparatively
easier to learn and to implement the
models using them
they allow you to write code in a
shorter amount of time and to be less
involved with the details
in this case tensorflow is a high and
low level api
pure tensorflow is a low level api while
tensorflow wrapped in keras is a high
level api
keras in itself is a high level api
which uses multiple low-level apis as a
back-end and simplifies the operation of
these low-level apis
pi torch is a low level api
the next criteria that we'll be looking
at is speed
tensorflow is very fast and is used for
high performances
keras is slower as it works on top of
tensorflow not only does it have to wait
for tensorflow to finish implementation
it then starts its own implementation
meanwhile pi torch works at the same
speed as tensorflow as both of them are
both low level apis
now keras is a wrapper class for
tensorflow and has added abstraction
functionalities on top of tensorflow
which make it slower than tensorflow and
pi torch in computation speed both
tensorflow and pi torch are almost equal
and in development speed keras is faster
as it has built-in functionalities which
can significantly reduce your
development time
the next difference is on the
architecture
tensorflow is not very easy to use and
even though it provides keras as a
framework that makes it work easier
tensorflow still has a very complex
architecture which is hard to use
meanwhile keras has a simpler
architecture and is easier to use it
provides a high level of abstraction
which makes implementation of programs
in keras significantly easier pie touch
on the other hand also has a complex
architecture and the readability is less
when compared to keras tensorflow uses
computational graphs which makes it very
complex and hard to interpret but it has
amazing computational ability across
platforms
pie touch is a little hard for beginners
but is really good for computer vision
and deep learning purposes
data sets and debugging
tensorflow works with large data sets
due to its high execution speed and
debugging is really hard in tensorflow
due to its complex nature
meanwhile keras only works with very
small data sets as its speed of
execution is low
programs do not require frequent
debugging in keras as they are
relatively simpler and pi torch can
manage high level tasks in higher
dimension data sets and is easier to
debug than both qrs and tensorflow
next we'll be looking at ease of
development
as we said before tensorflow works with
many
hard concepts such as computational
graphs and tensors which means that
writing code in tensorflow is very hard
it is generally used by people when they
are doing research work and really need
very specific functionalities
keras on the other hand provides a high
level of abstraction which makes it very
easy to use it is best for people who
are just starting out with python and
machine learning pytorch is easier than
tensorflow but is still comparatively
hard than keras it is not very easy to
learn for beginners but is significantly
more powerful than just plain keras ease
of deployment tensorflow is very easy to
deploy as it uses tensorflow serving
tensorflow serving is a flexible high
performance serving system for machine
learning models designed for production
environments tensorflow serving makes it
easy to deploy new algorithms and
experiments while keeping the same
server architecture and apis
tensorflow serving provides
out-of-the-box integration with
tensorflow models but can be easily
extended to serve other types of models
and data
in keras model deployment can be done
with either tensorflow serving or flask
which makes it relatively easy but not
as easy as you as it would be with
tensorflow and pie torch
pytorch uses i torch mobile which makes
deployment easy but again for tensorflow
deployment is way easier as tensorflow
serving can update your machine learning
back end on the fly without
the user even realizing there's a
growing need to execute ml models on
edge devices to reduce latency preserve
privacy and enable new interactive use
cases in the past engineers used to
train models separately they would then
go through a multi-step error-prone and
often complex process to train the
models for execution on a mobile device
the mobile runtime was often
significantly different from the
operations available during training
leading to inconsistent developer and
eventually user experience
all of these frictions have been removed
by pytorch mobile by allowing a seamless
process to go from training to
deployment by staying entirely within
the pytorch ecosystem it provides an
end-to-end workflow that simplifies the
research to production environment for
mobile devices
in addition it paves the way for privacy
preserving features via federated
learning techniques
at the end of the day the question that
really matters is which framework should
you use keras tensorflow or pytoch
now tensorflow has implemented various
levels of abstraction to make
implementation of deep learning and
neural networks easy this has also made
debugging easier keras is simple and
easy but not as fast as tensorflow it is
more user friendly than any other deep
learning api however and is easier to
learn for beginners
pytorch on the other hand is the
preferred deep learning api for teachers
but it is not as widely used in
production as tensorflow is it is faster
but it has lower gpu utilization
at the end of the day
the framework that we would suggest that
you use is tensorflow
why
while pytorch may have been the
preferred deep learning library for
researchers tensorflow is much more
widely used in day-to-day production
pytorch's ease of use combined with the
default ego execution mode for easier
debugging predestines it to be used for
fast hacky solutions and smaller scale
models
but tensorflow's extensions for
deployment on both servers and mobile
devices combined with the lack of python
overhead makes it the preferred option
for companies that work with deep
learning models in addition the
tensorflow board visualization features
offers a nice way of showing the inner
workings of your model to say your
customers
meanwhile between tensorflow and keras
the main difference isn't in performance
tensorflow is a bit faster due to less
overhead but also the level of control
you would like keras is much easier to
start with than plain tensorflow but if
you want to do something with keras that
doesn't come out of the box will be
harder to implement that tensorflow on
the other hand allows you to create any
arbitrary computational graph providing
much more flexibility so if you're doing
more research type of work tensorflow is
the sure route to go due to the
flexibility that it provides this brings
us to the end of this video on keras
versus tensorflow versus pie torch we
hope that this video was useful to you
on your journey to learning more about
deep learning to learn more about deep
learning and related topics you can
check out the simply learn website which
is linked in the description below to
keep learning with fun interactive
videos do subscribe to the simply learn
channel thank you for watching and keep
learning
hi there if you like this video
subscribe to the simply learn youtube
channel and click here to watch similar
videos turn it up and get certified
click here
5.0 / 5 (0 votes)