LabVIEW
[TDF] Announcing LabVIEW Deep Learning module - HAIBAL
Dear Community,
We are proud to announce the next release of LabVIEW Deep Learning module - HAIBAL 😎
The HAIBAL project is structured in the same way as Keras.
The project consists of more than 3000 VIs including, all is coded in LabVIEW native:😱😱😱
- 16 activations (ELU, Exponential, GELU, HardSigmoid, LeakyReLU, Linear, PReLU, ReLU, SELU, Sigmoid, SoftMax, SoftPlus, SoftSign, Swish, TanH, ThresholdedReLU), nonlinear mathematical function generally placed after each layer having weights.
- 84 functional layers/layers (Dense, Conv, MaxPool, RNN, Dropout, etc…).
- 14 loss functions (BinaryCrossentropy, BinaryCrossentropyWithLogits, Crossentropy, CrossentropyWithLogits, Hinge, Huber, KLDivergence, LogCosH, MeanAbsoluteError, MeanAbsolutePercentage, MeanSquare, MeanSquareLog, Poisson, SquaredHinge), function evaluating the prediction in relation to the target.
- 15 initialization functions (Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, Random,Uniform, TruncatedNormal, VarianceScaling, Zeros), function initializing the weights.
- 7 Optimizers (Adagrad, Adam, Inertia, Nadam, Nesterov, RMSProp, SGD), function to update the weights.
Currently, we are working on the full integration of Keras in compatibility HDF5 file and will start soon same job for PyTorch. (we are able to load model from and will able to save model to in the future – this part is important for us).
Well obviously, Cuda is already working if you use Nvidia board and NI FPGA board will also be – not done yet.
We also working on the full integration on all Xilinx Alveo system for acceleration.
User will be able to do all the models he wants to do; the only limitation will be his hardware. (we will offer the same liberty as Keras or Pytorch) and in the future our company could propose Harware (Linux server with Xilinx Alveo card for exemple --> https://www.xilinx.com/products/boards-and-kits/alveo.html All full compatible Haibal !!!)
About the project communication:
The website will be completely redone, a Youtube channel will be set up with many tutorials and a set of known examples will be offered within the library (Yolo, Mnist, etc.).
For now, we didn’t define release date, but we thought in the next July (it’s not official – we do our best to finish our product but as we are a small passionate team (we are 3 working on it) we do our best to release it soon).
This work is titanic and believe me it makes us happy that you encourage us in it. (it boosts us). In short, we are doing our best to release this library as soon as possible.
Still a little patience …
Youtube Video :
This exemple is a template state machine using HAIBAL library.
It show a signal (here it's Cos) and the neural network during his training has to learn to predict this signal (here we choose 40 neurones by layers, 5 layers, layer choose is dense).
This template will be proposed as basic example to understood how we initialize, train and use neural network model. This kind of "visualisation exemple" is inspired from https://playground.tensorflow.org/ help who want to start to learn deep learning.
Youssef Menjour
Certified LabVIEW Architect (CLA)
Dear Alexder,
The HAIBAL toolbox will propose to the user to make his own architecture / training / prediction natively on LabVIEW.
Of course we will propose natively numerous exemple like Yolo, Minst, VGG ... (that user can directly use and modify it)
As our toolkit is new, we made the choice to be also fully compatible with Keras. This means that if you already have a model trained on Keras, it will be possible to import it on HAIBAL. This will also open our library to thousands of models available on the internet. (all import will traduct on HAIBAL native labVIEW code as editable by users)
In this case, you will have two choices;
1 - use it on labVIEW (predict / train)
2 - Generate all the native architecture equivalent to HAIBAL (as you can see on the video) in order to modify it as you wish.
HAIBAL it's more 3000 VIs, it represente a huge work and we not yet finished. We hope to release the first version this summer (with Cuda) and hope NI-FPGA optimisation to speed up inference. (Open CL and all Xilinx FPGA compatibilities will also come during 2022/2023)
We are building actually our website and our youtube channel.
The teams will propose tutorials (youtube/git hub) and news (website) to give visibilities for users.
