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.
Related
I'm learning TensorFlow and Keras. I'd like to try https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/, and it seems to be written in Keras.
Would it be fairly straightforward to convert code to tf.keras?
I'm not more interested in the portability of the code, rather than the true difference between the two.
The difference between tf.keras and keras is the Tensorflow specific enhancement to the framework.
keras is an API specification that describes how a Deep Learning framework should implement certain part, related to the model definition and training.
Is framework agnostic and supports different backends (Theano, Tensorflow, ...)
tf.keras is the Tensorflow specific implementation of the Keras API specification. It adds the framework the support for many Tensorflow specific features like: perfect support for tf.data.Dataset as input objects, support for eager execution, ...
In Tensorflow 2.0 tf.keras will be the default and I highly recommend to start working using tf.keras
At this point tensorflow has pretty much entirely adopted the keras API and for a good reason - it's simple, easy to use and easy to learn, whereas "pure" tensorflow comes with a lot of boilerplate code. And yes, you can use tf.keras without any issues, though you might have to re-work your imports in the code. For instance
from keras.layers.pooling import MaxPooling2D
Would turn into:
from tensorflow.keras.layers import MaxPooling2D
The history of Keras Vs tf.keras is long and twisted.
Keras: Keras is a high-level (easy to use) API, built by Google AI Developer/Researcher, Francois Chollet. Written in Python and capable of running on top of backend engines like TensorFlow, CNTK, or Theano.
TensorFlow: A library, also developed by Google, for the Deep Learning developer Community, for making deep learning applications accessible and usable to public. Open Sourced and available on GitHub.
With the release of Keras v1.1.0, Tensorflow was made default backend engine. That meant: if you installed Keras on your system, you were also installing TensorFlow.
Later, with TensorFlow v1.10.0, for the first time tf.keras submodule was introduced in Tensorflow. The first step in integrating Keras within TensorFlow
With the release of Keras 2.3.0,
first release of Keras in sync with tf.keras
Last major release to support other multi-backend engines
And most importantly, going forward, recommend switching the code from keras to Tensorflow2.0 and tf.keras packages.
Refer this tweet from François Chollet to use tf.keras.
That means,
Change Everywhere
From
from keras.models import Sequential
from keras.models import load_model
To
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import load_model
And In requirements.txt,
tensorflow==2.3.0
*Disclaimer: it might give conflicts if you were using an older version of Keras. Do pip uninstall keras in that case.
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.
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
Is it possible to switch between keras backend in same python program in which both backend specific functions are imported from the keras internal (tensorflow_backend.py, theano_backend.py and cntk_backend.py).
When I set os.environ[KERAS_BACKEND]='theano' I get errors from Keras tensorflow internal.
I need to get output from keras tensorflow and keras theano internal (from backend file)for same calculation in same program
My guess would be no. Keras is bound to only one session and switching the backend midway in program seems like no good idea at all.
If you are trying to compare specific function from the backend why not just load the backend frameworks directly and then run the code?
It sounds like you want to do a benchmarking from keras with different backends but keras is just an API so you can easily test the frameworks without it and return to it later, when you know which backend is better for you.
If you still try to do this: Would multiprocessing be a solution? You could import keras with tensorflow and keras with theano inside different processes.
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.