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Deep Learning by Ngene - Toolkit for LabVIEW Download

Deep Learning Toolkit for LabVIEW

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ReleasedMar 30, 2023
Publisher Ngene
License Ngene Custom
LabVIEW VersionLabVIEW>=20.0
Operating System Windows
Project links Homepage   Documentation   Repository  


Empowering LabVIEW with Deep Learning
DeepLTK is a Deep Learning Toolkit for LabVIEW providing high-level API to build, configure, visualize, train, analyze and deploy Deep Neural Networks within LabVIEW. The toolkit is completely developed in LabVIEW and does not have any outer dependencies, which simplifies the installation, development, deployment and distribution of toolkit based applications and systems (particularly, can be easily deployed on NI's Real Time targets).

Main Features
Create, configure, train, and deploy deep neural networks (DNNs) in LabVIEW
Accelerate training and deployment of DNNs on GPUs
Save trained networks and load for deployment
Visualize network topology and common metrics (memory footprint, computational complexity)
Deploy pre-trained networks on NI's LabVIEW Real-Time target for inference
Speed up pre-trained networks by employing network graph optimization utilities
Analyze and evaluate network's performance
Start with ready-to-run real-world examples
Accelerate inference on FPGAs (with help of DeepLTK FPGA Add-on)

Supported Layers:
Input (1D, 3D)
Augmentations: Noise, Flip(Vertical, Horizontal), Brightness, Contrast, Hue, Saturation, Shear, Scale(Zoom), Blur, Move.
Fully Connected - FC
Convolutional - Conv2D
Convolutional Advanced - Conv2D_Adv
ShortCut (Residual)
Batch Normalization
Activations: Linear(None), Sigmoid, tanh(Hyperbolic Tangent), ReLU(Rectified Linear Unit), LReLU(Leaky ReLU)
Pooling (MaxPool, AvgPool, GlobalMax, GlobalAvg)
DropOut (1D, 3D)
SoftMax (1D, 3D)
YOLO_v2 (object detection)

Activation types:
Hyperbolic Tangent
Leaky ReLU

Solver (Optimization Algorithm):
Stochastic Gradient Descend (SGD) based Backpropagation algorithm with Momentum and Weight decay
Adam - Stochastic gradient descent method which is based on adaptive estimation of first-order and second-order moments.

Loss Functions:
MSE - Mean Squared Error
Cross Entropy (LogLoss)
Object Detection (YOLO_v2)

Examples are available to demonstrate the applications of the toolkit in:
1. - training the deep neural network for image classification task in handwritten digit recognition problem (based on MNIST database) on 1 dimensional dataset using MLP (Multilayer Perceptron) architecture
2. MNIST_Classifier_CNN(Train).vi - training the deep neural network for image classification task in handwritten digit recognition problem using CNN (Convolutional Neural Network) architecture
3. MNIST_Classifier(Deploy).vi - deploying pretrained network by automatically loading network configuration and weights files generated from the examples above.
4. MNIST(RT_Deployment) project - deployment of pretrained model on NI's Real Time targets.
5. YOLO_Object_Detection(Cam).vi - automatically building and loading pretrained network for object detection based on YOLO (You Only Look Once) architecture.
6. Object_Detection project - demonstrates training of neural network for object detection on simple dataset.

Release Notes (Mar 30, 2023)

This is a minor update which does not break backward compatibility with v6.x.x versions of the toolkit.

1.Added ReLU6 activation function.
2.Removed the requirement for using Conditional Disable Symbol ("NNG") for enabling GPU acceleration.
2.1.Removed GPU specific examples.

Bug Fixes
1.Fixed a bug in batch normalization.
2.Fixed a bug in the calculation of the MSE loss.
3.Fixed a bug in mAP metric calculations for object detection.
4.Increased the maximum number of layers in the network from 100 to 500.
5.Other minor bug fixes

Other Updates
1. Updated help file.
2. Added link to examples on GitHub in example instructions.

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