Keras vs. TensorFlow: Key Differences

Machine
learning
and

deep
learning
have
been
through
a
revolutionary
transition
in
the
last
decade.
Most
of
the
work
around
these
two
technologies
had
been
limited
to
research
labs.
However,
it
is
important
to
note
that
the
two
most
prominent
disciplines
of

artificial
intelligence
have
emerged
as
lifesavers
for
many
businesses. The
Keras
vs.
TensorFlow
debate
has
been
gaining
attention
as
they
are
the
most
popular
deep
learning
frameworks
that
can
help
machine
learning
and
deep
learning
engineers.
It
is
important
to
note
that
machine
learning
and
deep
learning
engineers
have
to
work
on
larger
data
sets
that
continue
expanding.

TensorFlow
and
Keras
can
help
developers
achieve
more
functionality
with
data
alongside
exercising
better
control
on
training

ML
models.
Let
us
explore
the
differences
between

TensorFlow
and
Keras
to
determine
the
best
pick
among
them.

Overview
of
Keras 

Keras
is
an
open-source,
high-level
neural
network
library
created
by
Google
engineer
Francois
Chollet.
The
primary
objective
of
creating
Keras
was
focused
on
improving
the
accessibility
of
deep
learning.
The
Keras
and
TensorFlow
difference
is
visible
in
the
fact
that
you
don’t
need
coding
expertise
to
develop
neural
networks
with
Keras.

It
offers
a
high-level
API
that
can
help
in
abstraction
of
complexities
associated
with
deep
learning.
On
top
of
that,
Keras
also
offers
pre-build
layers,
optimizers,
and
activation
functions
that
help
you
focus
on
designing
the
model
rather
than
worrying
about
technicalities.
Keras
has
been
developed
with
Python
programming
language,
thereby
helping
Python
programmers
learn
its
functionalities
with
ease. 

Before
you
compare
Keras
and
TensorFlow,
you
must
know
that
Keras
stands
out
for
its
simplicity
and
adaptability.
It
helps
you
explore
model
designs
and
enables
faster
prototyping
with
the
features
for
examining
performance
of
different
designs
as
you
adjust
hyperparameters.
Keras
has
been
instrumental
in
fuelling
advancements
in

natural
language
processing,
speech
recognition,
and
computer
vision.

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Overview
of
TensorFlow

TensorFlow
is
another
powerhouse
in
the
domain
of
deep
learning
and
AI.
It
is
the
most
renowned
deep
learning
framework,
primarily
because
it
was
created
by
Google
Brain
team.
Questions
like
“Is
Keras
the
same
as
TensorFlow?”
emerge
from
the
fact
that
both
of
them
are
open-source
libraries.

In
the
case
of
TensorFlow,
the
library
is
tailored
for
numerical
computation
tasks
that
leverage
data
flow
graphs.
TensorFlow
helps
in
conducting
computations
on
GPUs,
CPUs,
and
distributed
systems
by
using
nodes
to
represent
mathematical
operations.
The
edges
in
TensorFlow
represent
data
flow
channels. 

TensorFlow
helps
in
creating
different
ML
and
deep
learning
models.
It
helps
deep
learning
researchers
create
different
types
of
supervised
and
unsupervised
learning
algorithms.
Developers
can
use
TensorFlow
with
different
programming
languages
such
as
C++,
Java,
and
Python.
The
primary
objective
of
TensorFlow
revolves
around
training
ML
models
on
large
datasets
to
help
them
make
faster
decisions.
TensorFlow
has
emerged
as
a
trusted
tool
for
use
in
cases
such
as
image
recognition,
natural
language
processing,
recurrent
neural
networks,
and
word
embeddings.     

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What
are
the
Most
Notable
Differences
between
TensorFlow
and
Keras?

The
overview
of
TensorFlow
and
Keras
shows
that
both
of
them
are
similar
in
many
ways.
However,
the
Keras
and
TensorFlow
compatibility
is
significantly
limited,
as
there
are
multiple
differences
between
them.
Here
is
an
overview
of
some
of
the
notable
differences
between
TensorFlow
and
Keras.

• 
  Use
  Cases 

The
best
way
to
compare
TensorFlow
with
Keras
would
involve
finding
out
their
use
cases.
TensorFlow
is
more
flexible
and
has
robust
features
to
manage
and
process
large
data
sets.
It
is
useful
for
research
as
well
as
practical
applications
in
the
domain
of
AI.
Some
of
the

most
notable
use
cases
of
TensorFlow
include
image
recognition,
generative
models,
image
synthesis,
and
predictive
analytics. 

Convolutional
Neural
Networks
use
TensorFlow
for
better
performance
in
image
classification,
facial
recognition,
and
object
detection.
The
capabilities
of
TensorFlow
for
prediction
of
numerical
values
by
leveraging
historical
data
can
be
useful
for
regression
and
predictive
analytics
tasks.
On
top
of
that,
TensorFlow
can
power
GANs
that
help
create
realistic
videos,
synthesize
images,
augment
data,
and
improve
visual
effects. 

On
the
other
hand,
in
the
TensorFlow
vs.
Keras
comparison,
Keras
is
more
useful
for
different
machine
learning
tasks.
It
is
the
top
choice
of
developers
for
natural
language
processing
tasks,
reinforcement
learning,
and

transfer
learning.
Keras
helps
power

NLP
applications
such
as
language
translation,
sentiment
analysis,
and
text
classification.

Developers
can
use
the
Keras
API
to
implement
reinforcement
learning
algorithms
that
can
power
autonomous
systems
and
robotics.
Another
important
use
of
Keras
is
transfer
learning,
which
helps
in
creating
pre-trained
models
that
you
can
fine-tune
for
specific
tasks. 

• 
  Important
  Features 

The
next
crucial
point
for
comparison
between
TensorFlow
and
Keras
is
the
outline
of
important
features
that
differentiate
the
frameworks.
You
might
assume
that
the
Keras
and
TensorFlow
differences
would
turn
in
favor
of
TensorFlow
with
its
diverse
features.
TensorFlow
offers
a
wide
range
of
features
such
as
a
powerful
computation
engine,
TensorBoard
visualization,
deep
learning
environment
AutoGraph,
and
eager
execution.

It
also
includes
a
collection
of
pre-trained
models
in
the
TensorFlow
Hub
that
can
help
in
using
existing
models
for
different
tasks
such
as
NLP
and
image
recognition.
TensorBoard
visualization
and
AutoGraph
are
the
most
valuable
features
of
TensorBoard
for
machine
learning
and
deep
learning
workloads. 

Keras
also
has
a
collection
of
powerful
features
that
make
it
a
worthy
competitor
to
TensorFlow.
Some
of
the
notable
core
features
of
Keras
include
modular
building
blocks,
API
streamlining,
flexible
model
development,
and
better
GPU
and
TPU
acceleration.
You
can
find
a
collection
of
pre-defined
model
components
and
neural
network
layers
in
Keras
that
help
you
build
complex
deep
learning
architectures. 

Keras’
concise
and
intuitive
API
ensures
better
accessibility
and
usability.
Most
importantly,
Keras
API
offers
flexibility
when
choosing
a
backend
that
aligns
with
the
needs
of
developers.
On
top
of
it,
the
vibrant
community
of
Keras
includes
developers
and
researchers.
The
community
can
help
you
access
comprehensive
documentation,
a
technical
support
network,
and
tutorials.     

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• 
  Model
  Development 

Another
notable
factor
that
helps
differentiate
TensorFlow
from
Keras
is
its
capabilities
for
model
development.
TensorFlow
offers
fine-grained
control,
while
Keras
offers
simplicity
in
construction
of
models.
The
Keras
vs.
TensorFlow
comparison
draws
attention
to
the
high-precision
tools
available
with
TensorFlow
for
AI
architectures.

Compared
to
Keras,
TensorFlow
offers
a
lower-level
methodology
that
helps
developers
with
flexibility
for
model
development
from
scratch.
TensorFlow
offers
complete
control
over
deep
customization
and
fine-tuning
AI
models,
thereby
crafting
them
to
your
needs. 

Keras
does
not
shy
away
in
terms
of
capabilities
for
model
development
as
compared
to
TensorFlow.
It
can
serve
as
a
comprehensive
AI
toolkit
for
machine
learning
and
deep
learning
development.
The
modular
building
blocks
work
as
pre-assembled
layers
of
machine
learning
and
deep
learning
architectures.
You
can
combine
the
layers
to
create
neural
network
models
tailored
according
to
your
requirements.

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• 
  Ease
  of
  Usability 

The
Keras
vs
Tensorflow
comparison
also
rounds
up
on
the
element
of
usability.
TensorFlow
serves
as
a
flexible
AI
workshop
that
is
completely
under
your
control.
Compared
to
Keras,
TensorFlow
offers
an
experience
that
is
similar
to
a
skilled
artist
working
with
high-quality
tools. 

The
Keras
and
TensorFlow
comparison
suggests
that
you
have
a
comprehensive
toolbox
for
precise
development
and
training
of
neural
networks.
TensorFlow
offers
a
wide
range
of
opportunities
for
deep
learning
and
is
the
best
choice
for
developers
seeking
more
control
over
the
model
development
and
training
process.

Keras
is
a
tough
competitor
to
TensorFlow
for
its
user-friendly
and
straightforward
nature.
It
can
serve
as
a
friendly
guide
for
developers
in
the
domain
of
deep
learning.
The
high-level
API
of
Keras
is
simple
and
easy
to
understand
with
a
friendly
interface.
You
can
create
and
train
neural
networks
by
using
Keras
with
a
few
lines
of
code.
Therefore,
it
serves
as
the
ideal
pick
for
beginners
who
dive
into
deep
learning
of
its
modular
architecture
and
simple
syntax. 

Here
is
an
overview
of
the
differences
between
TensorFlow
and
Keras. 

Final
Words 

The
TensorFlow
vs.
Keras
debate
ultimately
rounds
up
on
one
crucial
factor,
i.e.,
your
requirements.
You
can
arrive
at
a
specific
conclusion
regarding
the
choice
of

machine
learning
and
deep
learning
frameworks
according
to
the
desired
use
case.
It
is
important
to
choose
from

TensorFlow
and
Keras
with
your
objectives
in
mind.
Both
frameworks
are
powerful
resources
for
AI
researchers
and
developers
with
distinct
sets
of
features.
Enroll
in

AI
Certification
to
learn
more
about
the
functionalities
and
advantages
of
the
two
frameworks
to
pick
the
right
choice
for
your
next
AI
project.