I am currently using keras as a deep learning library on top of tensorflow. I just want to know is there any other library which is more efficient/easier than keras.
I can't say which is more efficient or easier but MXNet exists, has interfaces to most languages (python, scala, R, C++, perl, Julia), and the code looks fairly similar to keras.
Update: I didn't see caffe 2 but I think this is quite separate to the original caffe.
Finally I've not fully had time to check out H2O's offering in deep water which is an alternative to Keras but still sits on top of TF/MXNet/Caffe
I reccomend pytorch. You can find good tutorials here.
Keras is a high-level API that can be used on top of
Theano. You can use each of the low-level APIs but the problem of those is that you can get complicated if you design very deep nets whilst dealing with
Keras is much easier. Consequently,
Keras is designed for accelerating deep nets' designing.
Keras is opensource like the underlying libraries it comes for and I guess its project is not for
TensorFlow itself has a high-level API, namely
TFLearn. I cannot say which is better but the point is that try to master one of them perfectly.
It seems that
plaidML Keras backend is also available which enables training on AMD graphics.
You can try PyTorch. It offers much more manual controls and tweaking and it's pure python ie no functional API that's why it is used in research fields whereas Keras is most easy and robust. I use Keras with a backend as plaidml which enables me to train my neural network models on AMD GPU (RX 560x).