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Deep Learning Toolkit (System) by Ngene - Toolkit for LabVIEW Download

Dep Learning Toolkit for LabVIEW

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Version	1.4.0.57
Released	Jun 14, 2018
Publisher	Ngene
License	Ngene Custom
LabVIEW Version	LabVIEW>=0
Operating System	Windows
Project links	Homepage

Description

Empower LabVIEW with Deep Learning
Deep Learning Toolkit for LabVIEW is a high-level API providing possibilities to build, configure, visualize, train, analyze and deploy Deep Learning based systems. The toolkit is mainly implemented 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).

Note: This installer requires LabVIEW and VI Package Manager to be run with Administrator privileges.

Main Features:
Create, configure, train and deploy Deep Neural Networks in LabVIEW
Visualize network topology and display common metrics (memory footprint, FLOPs)
API to debug and analyze networks
Save trained networks (configuration and weights) and load for deployment
NI's Real Time target support for deployment
Ready to run real world examples

Supported Layers:
Input (1D, 3D)
Augmentation - Implemented in Input3D
Fully Connected - FC
Convolutional - Conv3D
Batch Normalization
Activation
Pooling (MaxPool, AvgPool)
DropOut (1D, 3D)
SoftMax
Region (for YOLO v2)

Activation types:
Sigmoid
Hyperbolic Tangent
ReLU
Leaky ReLU

Solver (Optimization Algorithm):
Stochastic Gradient Descend (SGD) based Backpropagation algorithm with Momentum

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

Examples:
Examples are available to demonstrate the use of the toolkit on:
1. MNIST_Classifier_MLP(Train).vi - training the deep neural network for image classification task in handwritten digit recognition problem (based on MNIST database) 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. YOLO_Object_Detection(Cam).vi automatically building and loading pretrained network for object detection based on YOLO (You Only Look Once) architecture.

Development License Activation Instructions
For Toolkit's developemnt license activation please call Third Party Add-on Activation Wizard (Help>>Add-Ons..) from 32 bit version of LabVIEW, as there is a known issue in 64 bit LabVIEW 2017 version

Release Notes

1.4.0.57 (Jun 14, 2018)

V1.4.0.57
1.Added augmentation functionality for 1-dimensional data implemented in Input1D layer.
2.Improved Conv3D and FC layer's performance.
3.Fixed augmentation errors in Input3D layer.

1.3.3.54 (May 31, 2018)

V1.3.3.54
1. Performance Improvements.
2. Removed Augmentation layer.
3. Added augmentation functionality into Input3D layer.
4. Added training support for datasets with different dimensionality:
a) 1-dimensional input -> 1-dimensional output
b) 3-dimensional input -> 1-dimensional output
c) 3-dimensional input -> 3-dimensional output
5. Added API for checking dataset compliance (i.e. input and output dimensions) with a built network
6. Improved Error Handling at SoftMax Layer creation.
7. Conv3D now supports DropOut3D as the input layer.
8. MaxPool and AvgPool layers now support non square inputs.
9. Added global MaxPool and AvgPool functionality.
10. Fixed metrics calculation for FC layer. Now number of params includes number of biases as well.
11. Improved Error Handling for checking dataset compliance with the built network.
12. Fixed a bug when writing a Region layer into configuration file.
13. YOLO Example. Now detected bounding boxes are provided in more convenient way for further processing.
14. YOLO Example. Now custom labels can be provided to be shown on the display.

1.3.2.51 (May 11, 2018)

V1.3.2.50
1. Performance Improvements.
2. Removed Augmentation layer.
3. Added augmentation functionality into Input3D layer.
4. Added training support for datasets with different dimensionality:
a) 1-dimensional input -> 1-dimensional output
b) 3-dimensional input -> 1-dimensional output
c) 3-dimensional input -> 3-dimensional output
5. Added API for checking dataset compliance (i.e. input and output dimensions) with a built network
6. Improved Error Handling at SoftMax Layer creation.
7. Conv3D now supports DropOut3D as the input layer.
8. MaxPool and AvgPool layers now support non square inputs.
9. Added global MaxPool and AvgPool functionality.
10. Fixed metrics calculation for FC layer. Now number of params includes number of biases as well.
11. Improved Error Handling for checking dataset compliance with the built network.
12. Fixed a bug when writing a Region layer into configuration file.
13. YOLO Example. Now detected bounding boxes are provided in more convenient way for further processing.
14. YOLO Example. Now custom labels can be provided to be shown on the display.

1.3.2.50 (May 05, 2018)

V1.3.2.50
1. Performance Improvements.
2. Removed Augmentation layer.
3. Added augmentation functionality into Input3D layer.
4. Added training support for datasets with different conventionalities:
a) 1-dimensional input -> 1-dimensional output
b) 3-dimensional input -> 1-dimensional output
c) 3-dimensional input -> 3-dimensional output
5. Added API for checking dataset compliance (i.e. input and output dimensions) with a built network
6. Improved Error Handling at SoftMax Layer creation.
7. Conv3D now supports DropOut3D as the input layer.
8. MaxPool and AvgPool layers now support non square inputs.
9. Added global MaPool and AvgPool functionality
10. Fixed metrics calculation for FC layer. Now number of params includes number of biases as well.
11. Improved Error Handling for checking dataset compliance with the built network.
12. Fixed a bug when writing a Region layer into configuration file

1.2.0.42 (Feb 08, 2018)

V1.2.0.41
1. Added support for deployment on NI’s RT targets.
2. Added API to get/set layer weights.
3. Added API to get layer outputs/activations.
4. Added API to get next layer.
5. Optimized weight initialization process.
6. Error Handling: Check input layer type at layer creation.
7. Error Handling: Check input dimensions at creating Conv3D layer.
8. Error Handling: Check input dimensions at creating Pool layer.
9. Error Handling: Check input data dimensions when setting Input3D layer outputs.
10. Fixed the bug for the case when neural network trained on non-square images.
11. Fixed error in get_next_layer.vi.
12. Added get layer data API.
13. Added warning at get layer data if not proper type of layer of routed at the input.
14. Error Handling: Check input dimensions at creating Conv3D layer.
15. Fixed a bug get new minibatch for dataset indexing when set for random sampling.
16. Updated instructions in MNIST examples.

V1.1.0.38
1. Added new examples - MNIST_Classifier_CNN(Train).vi, MNIST_Classifier(Deploy).vi. See description for more information
2. Updated help file
3. Fixed help file location in the installer
4. Corrected toolkit Vis' default values
5. Fixed bug when creating some layers (SoftMax, DropOut, DropOut3D) from configuration file.
6. Fixed errors generated by NN_Destroy.vi when empty network is provided at the input
7. Now probability set in DropOut layers will be coerced into (0..1) and warning will be generated if probability value set out of this range.
8. Fixed the issue of propagating warning through the toolkit VIs.
9. Added deployment licensing functionality
10. Included Ngene License Manager Installer in the toolkit installer
11. Other bugfixes

Show all release notes (+4 more releases)

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Versions

1.4.0.57 (Jun 14, 2018)

1.3.3.54 (May 31, 2018)

1.3.2.51 (May 11, 2018)

1.3.2.50 (May 05, 2018)

1.2.0.42 (Feb 08, 2018)

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