I am new to deep learning on jupyter-notebook. I compiled this first cell and got this reply. It says "TensorFlow backend". Is this an error?
No it's not an error. Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras.
At this time, Keras has three backend implementations available: the TensorFlow backend, the Theano backend, and the CNTK backend.
In your case it is TensorFlow backend.
Related
import tensorflow as tf
from tensorflow import keras
from keras import backend as K
What is the reason behind using the command—>
from keras import backend as K
What does it do? I would appreciate it if anyone explains it the simple way so that it does not get complicated in the mind.
You can find more information on what Keras backend actually is here or here.
In simpler terms to understand what Keras backend actually is
Keras is a model-level library that provides high-level building blocks for developing deep learning models. Keras does not provide low-level operations such as tensor multiplication and convolution. Instead, it relies on a specialized, well-optimized tensor library that serves as Keras' "backend engine". Instead of choosing one single tensor library and tying your Keras implementation to that library, Keras handles the problem in a modular way, allowing you to seamlessly connect multiple different backend engines to Keras.
Keras backend will allow you to write custom code or in a particular case a new "Keras module" for your use case that can support Theano and/or Tensorflow both. Like instead of tf.placeholder() you could write keras.backend.placeholder() which will work across both the libraries mentioned earlier.
I have lots of code and models developed in Keras, where "keras" is actually tf.keras from Tensorflow 1.12. I wonder if I install Tensorflow 2.0, will my code just work as is without changes?
Yes, your code should work without any change.
Just remember that tf.keras is a specific implementation of the Keras API specification, but the high-level API is always the same.
There could be some differences between tf.keras and keras, but obly some extra method added to the base API, no removal or breaking changes.
Hence, if your code uses only the high-level API of Keras, without any extra-operation from the backend, then you can be sure that replacing keras with tf.keras won't break anything.
As Keras becomes an API for TensorFlow, there are lots of old versions of Keras code, such as https://github.com/keiserlab/keras-neural-graph-fingerprint/blob/master/examples.py
from keras import models
With the current version of TensorFlow, do we need to change every Keras code as?
from tensorflow.keras import models
You are mixing things up:
Keras (https://keras.io/) is a library independent from TensorFlow, which specifies a high-level API for building and training neural networks and is capable of using one of multiple backends (among which, TensorFlow) for low-level tensor computation.
tf.keras (https://www.tensorflow.org/guide/keras) implements the Keras API specification within TensorFlow. In addition, the tf.keras API is optimized to work well with other TensorFlow modules: you can pass a tf.data Dataset to the .fit() method of a tf.keras model, for instance, or convert a tf.keras model to a TensorFlow estimator with tf.keras.estimator.model_to_estimator. Currently, the tf.keras API is the high-level API to look for when building models within TensorFlow, and the integration with other TensorFlow features will continue in the future.
So to answer your question: no, you don't need to convert Keras code to tf.keras code. Keras code uses the Keras library, potentially even runs on top of a different backend than TensorFlow, and will continue to work just fine in the future. Even more, it's important to not just mix up Keras and tf.keras objects within the same script, since this might produce incompatabilities, as you can see for example in this question.
Update: Keras will be abandoned in favor of tf.keras: https://twitter.com/fchollet/status/1174019423541157888
I am newbie on deep learning and it happens to me to confuse between Keras and tensorflow. knowing that tensorflow is a framework and Keras is a library, what is the difference between using these two deep learning tools.
Keras purposes is to use a framework in backend like Tensorflow, Theano or CNTK in an easier way.
For example, create a simple convolutional model under Tensorflow can be hard.
While create the same model under keras is very instinctive.
The difference between Tensorflow/Theano/CNTK and Keras is the following :
Keras is a framework who use the functions of Tensorflow/Theano/CNTK.
So Keras needs one of them to do something.
Tensorflow/Theano/CNTK or other like coffee can do everything by themselves.
But, often, it's harder to develop a model with them.
I see that there are many similar functions between tensorflow and keras like argmax, boolean_mask...I wonder why people have to use keras as backend along with tensorflow instead of using tensorflow alone.
Keras is not a backend, but it is a high-level API for building and training Neural Networks. Keras is capable of running on top of Tensorflow, Theano and CNTK. Most of the people prefer Keras due to its simplicity compared to other libraries like Tensorflow. I recommend Keras for beginners in Deep Learning.
A Keras tensor is a tensor object from the underlying backend (Theano,
TensorFlow or CNTK), which we augment with certain attributes that
allow us to build a Keras model just by knowing the inputs and outputs
of the model.
Theano vs Tensorflow
Tensorflow is necessary if you wish to use coremltools. Apple has promised support for architectures created using Theano but I haven't seen it yet.
Keras will require unique syntax sugar depending on the backend in use. I like the flexibility of Tensorflow input layers and easy-access to strong Google neural networks.