Pytorch vs TensorFlow vs Keras | Which is Better | Deep Learning Frameworks Comparison | Simplilearn

Summary

This 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

00:00

🤖 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.

05:01

🔍 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.

10:02

🏆 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.

Keywords

💡Keras

Keras is a high-level deep learning API written in Python, designed for easy implementation and computation of neural networks. It acts as an interface for the TensorFlow library, simplifying the operation of low-level APIs and providing a user-friendly, modular, and extensible environment for developing machine learning solutions. In the video, Keras is presented as an approachable and highly productive interface for beginners in machine learning, focusing on modern deep learning and enabling fast experimentation.

💡TensorFlow

TensorFlow is a low-level software library created by Google for implementing machine learning models and solving complex numerical problems. It is an open-source, free software library that is particularly focused on training and inference of deep neural networks. The video explains that TensorFlow is a symbolic math library based on data flow and differential programming, which is used for both research and production, and it allows for the creation of computational graphs where tensors and operators define the calculations in a machine learning model.

💡PyTorch

PyTorch is a low-level API developed by Facebook for natural language processing and computer vision. It is described as a powerful version of NumPy and an open-source machine learning library that emphasizes flexibility, allowing deep learning models to be expressed in basic Python. The video script positions PyTorch as a preferred deep learning API for researchers and teachers, known for its ease of use and powerful capabilities, but notes that it may not be as widely used in production as TensorFlow.

💡API

API stands for Application Programming Interface, which is a set of rules and protocols for building software applications. In the context of the video, APIs are differentiated into low-level and high-level APIs. Low-level APIs offer detailed control and manipulation of functions, while high-level APIs are simpler, providing more functionality with less code. The video discusses how TensorFlow can be used as both a low-level and high-level API, with Keras serving as a high-level API on top of TensorFlow, and PyTorch as a low-level API.

💡Tensor

In the video, tensors are defined as multi-dimensional arrays with a uniform type, and they are immutable, meaning their contents cannot be updated but only replaced with new tensors. Tensors are the variables in TensorFlow's computational graphs and are central to the library's data flow programming model. The concept of tensors is crucial for understanding how TensorFlow processes and performs calculations in machine learning models.

💡Computational Graph

A computational graph in the video is described as a graphical representation of the calculations that occur in a machine learning model. It consists of nodes (tensors) and operations (operators) that define the flow of data and the sequence of operations to be performed. TensorFlow uses computational graphs to represent and execute the operations in a machine learning model, which is a key aspect of its data flow programming approach.

💡Ease of Development

The ease of development refers to how simple or complex it is to write and implement code using a particular framework or library. The video script discusses that TensorFlow can be challenging due to its complex architecture and concepts like computational graphs, while Keras provides a higher level of abstraction making it easier to use, especially for beginners. PyTorch is noted as being easier than TensorFlow but still relatively complex compared to Keras.

💡Deployment

Deployment in the context of the video refers to the process of putting a machine learning model into a production environment. TensorFlow is highlighted for its ease of deployment through TensorFlow Serving, which is a high-performance serving system for machine learning models. Keras models can be deployed using TensorFlow Serving or Flask, while PyTorch uses Torch Mobile for deployment, emphasizing the flexibility and ease of deployment across different platforms.

💡Debugging

Debugging is the process of finding and resolving errors or issues in code. The video explains that TensorFlow's complex nature makes debugging difficult, whereas Keras, with its simpler architecture, requires less frequent debugging. PyTorch is noted as being easier to debug than TensorFlow, which is an important consideration when choosing a framework for machine learning development.

💡Data Flow

Data flow, as mentioned in the video, is a concept where calculations are performed by converting every element into a graphical form within a computational graph. This approach is central to how TensorFlow operates, allowing for the representation of complex numerical problems and machine learning models in a visual and structured manner.

💡High-Level API

A high-level API, as discussed in the video, is a more generic and simple interface that provides more functionality with fewer commands than a low-level API. High-level APIs are easier to learn and implement, allowing for faster development and less involvement with the underlying details. Keras is presented as a high-level API that simplifies the use of TensorFlow, making it more accessible for users.

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

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