I would like to create a custom Tensorflow topology, specifically a custom Tensorflow topology out of a binary encoding of a neural network. I have attached a picture of what I mean by 'binary encoding of a neural network' below.
Binary Encoding of ANN (Source: Yao99)
Unfortunately am I only familiar with how to use Tensorflow using complete layers, mostly via the Keras API, though I don't know how to create the whole topology in a custom way from scratch.
I don't require a complete solution, but would already highly appreciate links to tutorials on how to create such custom topologies from scratch. The final translation on how to map the binary encoding to the custom graph creation I can do myself. Unfortunately am I unable to find resources for such custom topologies online. Thank you for your help!
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I'm looking into training an object detection network using Tensorflow, and I had a look at the TF2 Model Zoo. I noticed that there are noticeably less models there than in the directory /models/research/models/, including the MobileDet with SSDLite developed for the jetson xavier.
To clarify, the readme says that there is a MobileDet GPU with SSDLite, and that the model and checkpoints trained on COCO are provided, yet I couldn't find them anywhere in the repo.
How is one supposed to use those models?
I already have a custom-trained MobileDetv3 for image classification, and I was hoping to see a way to turn the network into an object detection network, in accordance with the MobileDetv3 paper. If this is not straightforward, training one network from scratch could be ok too, I just need to know where to even start from.
If you plan to use the object detection API, you can't use your existing model. You have to choose from a list of models here for v2 and here for v1
The documentation is very well maintained and the steps to train or validate or run inference (test) on custom data is very well explained here by the TensorFlow team. The link is meant for TensorFlow version v2. However, if you wish to use v1, the process is fairly similar and there are numerous blogs/videos explaining how to go about it
I used CreateML do train a new custom ObjectDector.
Everything worked well so far.
Now I am just wondering, what kind of Network is trained in the background?
Is it something like YOLO or Mobilenet?
I did not found anything on the official documentation:
https://developer.apple.com/documentation/createml#overview
There are two options:
TinyYOLOv2
Using transfer learning. This uses a built-in feature extractor model (VisionFeaturePrint.Objects). This is available with Create ML in Xcode 12.
I am new to machine learning models and data science libraries. I wanted to use the Hidden Markov model for statistical data prediction on the fly which read the data from kafka and builds the model which is used to predict the data during the run-time and do the same for continous stream always.
Currently i can see only Tensorflow hidden markov model implementation in tensorflow python (tensorflow_probability distribution). Is their any other library available which can help me acheive the above scenario
Suggestions can involve the libraries of JAVA and python
Please feel free to add any resource links that can help me to understand the usage of tensorflow for hidden markov model
this might be a nice place to start: https://hmmlearn.readthedocs.io/en/latest/tutorial.html
Other alternatives, I found, are
Java:
Mallet library and it's extention GRMM in particular.
Python:
Pommegranate with it's HMM support.
Having said that, TensorFlow is much better known active and supported library, in my impression. I'd try that first.
I'm searching a library that would support Hierarchical HMMs (HHMM). That would probably require some tweaking into one of the listed ones.
My goal is to test out Google's BERT algorithm in Google Colab.
I'd like to use a pre-trained custom model for Finnish (https://github.com/TurkuNLP/FinBERT). The model can not be found on TFHub library. I have not found a way to load model with Tensorflow Hub.
Is there a neat way to load and use a custom model with Tensorflow Hub?
Fundamentally: yes. Everyone can create the kind of models that TF Hub hosts, and I hope authors of interesting models do consider that.
For TF1 and the hub.Module format tailored to it, see
https://www.tensorflow.org/hub/tf1_hub_module#creating_a_new_module
For TF2 and its revised SavedModel format, see
https://www.tensorflow.org/hub/tf2_saved_model#creating_savedmodels_for_tf_hub
That said, a sophisticated model like BERT requires a bit of attention to export it with all bells and whistles, so it helps to have some tooling to build on. The BERT reference implementation for TF2 at https://github.com/tensorflow/models/tree/master/official/nlp/bert comes with an open-sourced export_tfhub.py script, and anyone can use that to export custom BERT instances created from that code base.
However, I understand from https://github.com/TurkuNLP/FinBERT/blob/master/nlpl_tutorial/training_bert.md#general-info that you are using Nvidia's fork of the original TF1 implementation of BERT. There are Hub modules created from the original research code, but the tooling to that end has not been open-sourced, and Nvidia doesn't seem to have added their own either.
If that's not changing, you'll probably have to resort to doing things the pedestrian way and get acquainted with their codebase and load their checkpoints into it.
Is it possible to use different feature extractor with SSD meta-architecture in Tensorflow's Object Detection API? I know that .config files for mobilenets and inception are provided but is it possible to use a different architecture like AlexNet or VGG?
It's possible but with a little bit of work, as explained here, you should read this page for detailed explanation and links to examples.
In short, you'll need to create a custom FasterRCNNFeatureExtractor class, corresponding to VGG or AlexNet (it may require a bit of knowledge about these, for instance the amount of subsampling invovled). In this class, you'll code how your data should be preprocessed, how to retrieve the 1st and 2nd stage features in it (typically how is the last convolutional layer called), and how to load it.
Then you'll need to register your feaure extractor (tell the object detection API that it exists) by modifying the file object_detection/builders/model_builder.py.
Finally you should be able to make a config file with your custom feature extractor, et voilà !