I'm really confused about the purpose of DeepMind's Sonnet library for TensorFlow. As far as I can tell from the documentation, it seems to do essentially what Keras does (flexible functional abstractions). Can someone tell me what the advantage of Sonnet is?
There isn't much difference between them. They are both:
High-level object oriented libraries that bring about abstraction when developing neural networks (NN) or other machine learning (ML) algorithms.
Built on top of TensorFlow (with the addition of Theano for Keras).
So why did they make Sonnet? It appears that Keras doesn't seem to suit the needs of DeepMind. So DeepMind came up with Sonnet, a high-level object oriented programming library built on top of TensorFlow to address its research needs.
Keras and Sonnet are both trying to simplify deep reinforcement learning, with the major difference being Sonnet is specifically adapted to the problems that DeepMind explores.
The main advantage of Sonnet, from my perspective, is you can use it to reproduce the research demonstrated in DeepMind's papers with greater ease than keras, since DeepMind will be using Sonnet themselves. Aside from that advantage, it's just yet another framework with which to explore deep RL problems.
Related
I have been using TensorRT and TensorFlow-TRT to accelerate the inference of my DL algorithms.
Then I have heard of:
JAX https://github.com/google/jax
Trax https://github.com/google/trax
Both seem to accelerate DL. But I am having a hard time to understand them. Can anyone explain them in simple terms?
Trax is a deep learning framework created by Google and extensively used by the Google Brain team. It comes as an alternative to TensorFlow and PyTorch when it comes to implementing off-the-shelf state of the art deep learning models, for example Transformers, Bert etc. , in principle with respect to the Natural Language Processing field.
Trax is built upon TensorFlow and JAX. JAX is an enhanced and optimised version of Numpy. The important distinction about JAX and NumPy is that the former using a library called XLA (advanced linear algebra) which allows to run your NumPy code on GPU and TPU rather than on CPU like it happens in the plain NumPy, thus speeding up computation.
Is there any equivalent/alternate library to fastai in tensorfow for easier training and debugging deep learning models including analysis on results of trained model in Tensorflow.
Fastai is built on top of pytorch looking for similar one in tensorflow.
The obvious choice would be to use tf.keras.
It is bundled with tensorflow and is becoming its official "high-level" API -- to the point where in TF 2 you would probably need to go out of your way not using it at all.
It is clearly the source of inspiration for fastai to easy the use of pytorch as Keras does for tensorflow, as mentionned by the authors time and again:
Unfortunately, Pytorch was a long way from being a good option for part one of the course, which is designed to be accessible to people with no machine learning background. It did not have anything like the clear simple API of Keras for training models. Every project required dozens of lines of code just to implement the basics of training a neural network. Unlike Keras, where the defaults are thoughtfully chosen to be as useful as possible, Pytorch required everything to be specified in detail. However, we also realised that Keras could be even better. We noticed that we kept on making the same mistakes in Keras, such as failing to shuffle our data when we needed to, or vice versa. Also, many recent best practices were not being incorporated into Keras, particularly in the rapidly developing field of natural language processing. We wondered if we could build something that could be even better than Keras for rapidly training world-class deep learning models.
Why don't tutorials from TensorFlow use classes?
I.e. no OOP practices are used in all tutorials from TensorFlow.
Is it something conceptual? Is it matter of taste? Does something block the models from using OOP?
I would really appreciate if someone can give me at least basic explanation.
You can definitely use classes with Tensorflow (as this question shows).
As to why they're not used in the tutorials, I can only guess the choice was made to avoid adding another layer of complexity to understand the basic principles of Tensorflow. Understanding the graph, how TF variables relate to Python variables and TF scoping is hard enough without classes.
Nothing prevent you from using classes to implement your models. Have a look to this very nice post on how you could structure your TensorFlow models in an Object Oriented way in Python.
I am newbie in Tensorflow
Actually, I am testing some example in Tensorflow web-site, and I start to understand some features of the framwork, but what I don't understand is how I can design my architecture, I mean number of layers, type of Layer "conv, pool...", and if it is necessery to do that, because there are many predifined architectures like AmexNet,
Thanks,
I would strongly recommend working through their hands on tutorial, depending on if you have previous ML experience (https://www.tensorflow.org/get_started/mnist/pros) or not (https://www.tensorflow.org/get_started/mnist/beginners). The questions you are asking are answered in there.
The question on using predefined architectures or self defined depends on your use case. If you want to do something easy like classifying if there is only a car in the scene or not a more shallow architecture might work better, because it is faster and a more deep one is overkill. However most architectures are similar to the ones already defined in literature.
Another question that arises naturally, while talking about pre defined architecture is about transfer learning / fine tuning. Often pre defined architectures are already learned on some big dataset (mostly ImageNet) and already perform really well out of the box for many tasks. With little training data it makes a lot of sense to use this. With lots of training data it can hinder your progress though.
I want to study on the research of deep learning, but I don't know which framwork should I choice between TensorFlow and PaddlePaddle. who can make a contrast between the two frameworks? which one is better? especially in the running efficiency of CPU
It really depends what you are shooting for...
If you plan on training, CPU is not going to work well for you. Use colab or kaggle.
Assuming you do get a GPU, it depends if you want to focus on classification or object detection.
If you focus on classification, Keras is probably the easiest to work with or pytorch if you want some advanced stuff and to be able to change things.
If you plan on object detection, things are getting complicated... Inference is reasonably easy but training is complicated. There are actually 4 platforms you should consider:
Tensorflow - powerful but very difficult to work with. If you do not use Keras (and for OD you usually can't), you need to preprocess the dataset into tfrecords and it is a pain. The OD Api has very cryptic messages and it is very sensitive to the combination of tf version and api version. On the other hand, cool models like efficientdet are more or less easy to use.
MMdetection - very powerful framework, has lots of advanced models and once you understand how to work with it, you can easily work with and of the models it supports. Downside is that some models are slow to arrive (efficientdet, for example)
paddlepaddle - if you know Chinese, this should work ok, maybe. The documentation is a bit behind and usually requires lots of improvisation. Basically it is similar to mmdetection just with a few unique models and a few missing models.
detectron2 - I didn't work with this one, but it seems to support only a few models.
You probably need first to define for yourself what do you want to do and then choose.
Good luck!
It is not that trivial. Some models run faster with one kind of framework others with another. Furthermore, it depends on the hardware as well. See this blog. If inference is your only concern, then you can develop your model in any of the popular frameworks like TensorFlow, PyTorch, etc. In the end convert your model to ONNX format and benchmark its performance with DNN-Bench to choose the best inference engine for your application.