Are Keras and TensorFlow the same thing?
I've been walking urubusservating a few TensorFlow tutorials lately and realized that error reduction models, interleaved mapping and normalization models, and the neural network layer models themselves are (almost) all in the Keras library, hence my question.
TensorFlow Tutorial I saw: here
1 answers
Keras is a high-level API built on top of TensorFlow (and can be used in Theano as well). It is more user-friendly and easy to use, compared with TF. There are differences, for example, if you want to make a prototype neural network quickly, use Keras. You can make one with a minimum of lines of code. Keras is highly modular, which makes it very easy. However, it makes TF much more flexible than Keras. TF also offers much more complex operations than Keras. TF has a specialized debugger, which can save working hours... to end:
If you're not doing some research work or developing some special kind of neural network, then go to Keras (trust me, I'm a fan of Keras !!). And it's very easy to create models quickly, even more complex models in Keras.
If you want more control over your Network and want to watch closely what happens to the network over time, TF is the choice right (although the syntax can give you nightmares at times). But since we all know that Keras has been integrated with TF, it is wiser to build your network using tf.keras and insert anything you want into the network using pure TensorFlow. In short: tf.keras + tf = everything you will need.