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Keras vs. tf.keras

3

$begingroup$

I'm a bit confused in choosing between Keras (keras-team/keras) and tf.keras (tensorflow/tensorflow/python/keras/) for my new research project.

There is a debate that Keras isn't owned by anyone, so people are happier to contribute in and it'll be much easier to manage the project in the future. ‬

On the other side, ‪tf.keras is owned by Google, so more rigorous test and maintenance. Moreover, it seems this is a better option for taking advantage of new features which are presenting in Tensorflow v.2.

So, to start a data science (machine learning) project (in the research phase), that both are okay at the beginning, which one do you choose?!‬

python deep-learning keras tensorflow

share|improve this question

edited Mar 21 at 20:25

moh

asked Mar 21 at 20:20

mohmoh

618117

$endgroup$

add a comment | 

3

$begingroup$

I'm a bit confused in choosing between Keras (keras-team/keras) and tf.keras (tensorflow/tensorflow/python/keras/) for my new research project.

There is a debate that Keras isn't owned by anyone, so people are happier to contribute in and it'll be much easier to manage the project in the future. ‬

On the other side, ‪tf.keras is owned by Google, so more rigorous test and maintenance. Moreover, it seems this is a better option for taking advantage of new features which are presenting in Tensorflow v.2.

So, to start a data science (machine learning) project (in the research phase), that both are okay at the beginning, which one do you choose?!‬

python deep-learning keras tensorflow

share|improve this question

edited Mar 21 at 20:25

moh

asked Mar 21 at 20:20

mohmoh

618117

$endgroup$

add a comment | 

$begingroup$

I'm a bit confused in choosing between Keras (keras-team/keras) and tf.keras (tensorflow/tensorflow/python/keras/) for my new research project.

There is a debate that Keras isn't owned by anyone, so people are happier to contribute in and it'll be much easier to manage the project in the future. ‬

On the other side, ‪tf.keras is owned by Google, so more rigorous test and maintenance. Moreover, it seems this is a better option for taking advantage of new features which are presenting in Tensorflow v.2.

So, to start a data science (machine learning) project (in the research phase), that both are okay at the beginning, which one do you choose?!‬

python deep-learning keras tensorflow

share|improve this question

edited Mar 21 at 20:25

moh

asked Mar 21 at 20:20

mohmoh

618117

$endgroup$

share|improve this question

edited Mar 21 at 20:25

moh

asked Mar 21 at 20:20

mohmoh

618117

share|improve this question

edited Mar 21 at 20:25

moh

asked Mar 21 at 20:20

mohmoh

618117

asked Mar 21 at 20:20

mohmoh

618117

asked Mar 21 at 20:20

mohmoh

618117

1 Answer
1

active

oldest

votes

1

$begingroup$

From Keras repo.:

Keras is a high-level neural networks API, written in Python and
capable of running on top of TensorFlow, CNTK, or Theano.

And

Before installing Keras, please install one of its backend engines:
TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend.

So Keras is a skin (an API). TensorFlow has decided to include this skin inside itself as tf.keras. 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. Therefore, I would suggest to go with tf.keras which keeps you involved with only one, higher quality repo. instead of two, which means less headache.

Which one do you choose?!

tf.keras‬.

share|improve this answer

edited Mar 21 at 22:38

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216

$endgroup$

add a comment | 

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1

$begingroup$

From Keras repo.:

Keras is a high-level neural networks API, written in Python and
capable of running on top of TensorFlow, CNTK, or Theano.

And

Before installing Keras, please install one of its backend engines:
TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend.

So Keras is a skin (an API). TensorFlow has decided to include this skin inside itself as tf.keras. 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. Therefore, I would suggest to go with tf.keras which keeps you involved with only one, higher quality repo. instead of two, which means less headache.

Which one do you choose?!

tf.keras‬.

share|improve this answer

edited Mar 21 at 22:38

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216

$endgroup$

add a comment | 

1

$begingroup$

From Keras repo.:

Keras is a high-level neural networks API, written in Python and
capable of running on top of TensorFlow, CNTK, or Theano.

And

Before installing Keras, please install one of its backend engines:
TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend.

So Keras is a skin (an API). TensorFlow has decided to include this skin inside itself as tf.keras. 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. Therefore, I would suggest to go with tf.keras which keeps you involved with only one, higher quality repo. instead of two, which means less headache.

Which one do you choose?!

tf.keras‬.

share|improve this answer

edited Mar 21 at 22:38

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216

$endgroup$

add a comment | 

$begingroup$

From Keras repo.:

Keras is a high-level neural networks API, written in Python and
capable of running on top of TensorFlow, CNTK, or Theano.

And

Before installing Keras, please install one of its backend engines:
TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend.

So Keras is a skin (an API). TensorFlow has decided to include this skin inside itself as tf.keras. 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. Therefore, I would suggest to go with tf.keras which keeps you involved with only one, higher quality repo. instead of two, which means less headache.

Which one do you choose?!

tf.keras‬.

share|improve this answer

edited Mar 21 at 22:38

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216

$endgroup$

share|improve this answer

edited Mar 21 at 22:38

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216

answered Mar 21 at 21:18

EsmailianEsmailian

1,976216