I am currently trying to quantize a bert-classifier model but am running into an error, I was wondering if this is even supported at the moment or not? For clarity I am asking if quantization is supported on the BERT Classifier super class in the tensorflow-model-garden? Thanks in advance for the help!
Quantizing the standard BERT classifier is probably not a good way to go, if you are interesting in running a BERT-like model on a resource constrained edge device (like a mobile phone). For your specific question, I believe the answer is 'no, quantization of the standard BERT is not supported.' However, a better answer is probably to use one of the smaller BERT-type models that have been created for the edge use case, such as MobileBERT:
https://github.com/google-research/google-research/tree/master/mobilebert
The above link includes scripts for fine-tuning and then converting to TF Lite format in order to run on device.
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I have trained an image classification model using pytorch.
Now, I want to move it from research to production pipeline.
I am thinking of using TensorFlow extended. I have a very noob doubt that will I'll be able to use my PyTorch trained model in the TensorFlow extended pipeline(I can convert the trained model to ONNX and then to Tensorflow compatible format).
I don't want to rewrite and retrain the training part to TensorFlow as it'll be a great overhead.
Is it possible or Is there any better way to productionize the PyTorch trained models?
You should be able to convert your PyTorch image classification model to Tensorflow format using ONNX, as long as you are using standard layers. I would recommend doing the conversion and then look at both model summaries to make sure they are relatively similar. Also, do some tests to make sure your converted model handles any particular edge cases you have. Once you have confirmed that the converted model works, save your model as a TF SavedModel format and then you should be able to use it in Tensorflow Extended (TFX).
For more info on the conversion process, see this tutorial: https://learnopencv.com/pytorch-to-tensorflow-model-conversion/
You could considering using the torchX library. I haven't use it yet, but it seems to make it easier to deploy models by creating and running model pipelines. I don't think it has the same data validation functionality that Tensorflow Extended has, but maybe that will be added in the future.
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.
I am a CNTK user. I use AlexNet but would like a more compact NN -- so SqueezeNet seems to be of interest. Or does someone have some other suggestion? How do CNTK users deploy when size matters? Does somebody have a CNTK implementation of SqueezeNet?
The new ONNX model format now has several pretrained vision models, including one for SqueezeNet. You can download the model and load it into CNTK:
import cntk as C
z = C.Function.load(<path of your ONNX model>, format=C.ModelFormat.ONNX)
You can find tutorials for importing/exporting ONNX models in CNTK here.
SqueezeNet is a good choice for a small network with the possibility of a good accuracy. Take a look at DSDSqueezeNet for even better accuracy.
However, if it does not need to be as small as SqueezeNet you also could take a look at MobileNet or NasNet Mobile. These networks may be bigger, but they provide state of the art performance in the task of image classification.
Unfortunately, I do not have an CNTK implementation of SqueezeNet, but maybe a pretrained CNTK model, which you can reuse and finetune using Transfer Learning is all what you are looking for. In this case I can recommend you MMdnn, a conversion tool, which allows to convert a existing pretrained Caffe network to the CNTK model format. In this issue you can find a step by step guide for SqueezeNet.
I would not know of a method for especially small deployment, but you have basically two choices when it comes to saving your model: The standard CNTK model format and the new ONNX format which CNTK is going to support or does already. Till now, I could not try it myself, but maybe it offers a smaller size for the same network.
Since the CNTK model format is already saving the model in binary, I would not expect high improvements anyway, for any format. Anyway, compressing the model definitively could be an option, if size is very important.
This is a newbie question for the tensorflow experts:
I reading lot of data from power transformer connected to an array of solar panels using arduinos, my question is can I use tensorflow to predict the power generation in future.
I am completely new to tensorflow, if can point me to something similar I can start with that or any github repo which is doing similar predictive modeling.
Edit: Kyle pointed me to the MNIST data, which I believe is a Image Dataset. Again, not sure if tensorflow is the right computation library for this problem or does it only work on Image datasets?
thanks, Rajesh
Surely you can use tensorflow to solve your problem.
TensorFlow™ is an open source software library for numerical
computation using data flow graphs.
So it works not only on Image dataset but also others. Don't worry about this.
And about prediction, first you need to train a model(such as linear regression) on you dataset, then predict. The tutorial code can be found in tensorflow homepage .
Get your hand dirty, you will find it works on your dataset.
Good luck.
You can absolutely use TensorFlow to predict time series. There are plenty of examples out there, like this one. And this is a really interesting one on using RNN to predict basketball trajectories.
In general, TF is a very flexible platform for solving problems with machine learning. You can create any kind of network you can think of in it, and train that network to act as a model for your process. Depending on what kind of costs you define and how you train it, you can build a network to classify data into categories, predict a time series forward a number of steps, and other cool stuff.
There is, sadly, no short answer for how to do this, but that's just because the possibilities are endless! Have fun!
Are there any resnet implementations in tensorflow? I came across a few (e.g. https://github.com/ry/tensorflow-resnet, https://github.com/xuyuwei/resnet-tf) but these implementations have some bugs (e.g. see the Issues section on the respective github page). I am looking to train imagenet using resnet and looking for tensorflow implementations.
There are some (50/101/152) in tensorflow:models/slim.
The example notebook shows how to get a pre-trained inception running, res-net is probably no different.
I implemented a cifar10 version of ResNet with tensorflow. The validation errors of ResNet-32, ResNet-56 and ResNet-110 are 6.7%, 6.5% and 6.2% respectively. (You can modify the number of layers easily as hyper-parameters.)
I tried to be friendly with new ResNet fan and wrote everything straightforward. You can run the cifar10_train.py file directly without any downloads.
https://github.com/wenxinxu/resnet_in_tensorflow
I implemented Resnet by use of ronnie.ai and keras. Both of tool are great.
While ronnie is more easy from scratch.