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.
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 two models, model A in Tensorflow 2.0 and model B in Pytorch 1.3. Model A's output is B's input. I'd like to train the two models end-to-end.
Is it possible to do without porting one of the models to the other framework?
I believe this is impossible to jointly train models in Tensorflow and Pytorch. Those two frameworks use very different backend architectures to calculate the loss and do backpropagation, so they are incompatible with each other for training deep learning models.
A more detailed question ought to be which Tensorflow model and which Pytorch are you using in your problem. With the development of the deep learning community, more and more basic deep learning algorithms have various versions of implementations and support both Pytorch and Tensorflow. It seldom happens that you can only find a unique implementation in either Pytorch and Tensorflow. Just try to find corresponding implementation and join them together!
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.
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.