What is the algorithm behind tf-ranking? and can we use Lambdamart algorithm in tf-ranking .Can anyone suggest some good sources to learn these
TF Ranking provides a framework for you to implement your own algorithm. It helps you set up the components around the core of your model (input_fn, metrics and loss functions, etc.). But the scoring logic (aka scoring function) should be provided by the user.
You probably have already seen these, but just in case:
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
and
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
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I try to find the optimum of a data-driven function represented as a Tensorflow model.
Means I trained a model to approximate a function and now want to find the optimum of this approximated function using a algorithm and software package/python library like ipopt, ipyopt, casadi, .... Or is there a possibility to do this directly in Tensorflow. I also have to define constraints, so I can't just use simple autodiff to do gradient decent and optimize my input.
Is there any idea how to realize this in an efficient way?
Maybe this image visualizes my problem to better understand what I'm looking for.
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.
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.
I need to run k-means algorithm from Tensorflow in Go, i.e. cluster a graph intro subgraphs according to nodes similarity matrix.
I came across this article which shows an example on how to run a Keras trained model in Go. In this example the algo is of a supervised learning type. However in clustering algos, as I understand, there will be no model to save and export it to Go implementation.
The reason I am interested in Tensorflow, is because I think its code is optimized and will run much faster than k-mean implementation in Go, even with the scenario I described above.
I need an opinion of whether:
It is indeed impossible to use a Tensorflow k-mean algorithm in Go, and it is much better just to use k-means implemented in Go for this case.
It is possible to do this, and some sort of example or ideas on how to do this are very much appreciated.
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.