Product Features
No Coding
Advanced algorithms
Model Testing
Model Deployment
Design assistant
Reporting
No Coding
ANNHUB is specifically designed for engineers who do not have a profound knowledge of machine learning and programming skills to design a proper machine learning model in a few simple steps
Design AI model
LOAD DATASET
LOAD DATASET INTO ANNHUB
CONFIGURE
CONFIGURE A NEURAL NETWORK STRUCTURE
TRAIN
TRAIN A NEURAL NETWORK
EVALUATE
EVALUATE THE TRAINED NEURAL NETWORK
EXPORT
EXPORT TRAINED NEURAL NETWORK MODEL FOR DEPLOYMENNT
TRAIN
ANNHUB separates dataset into a training set, validation set, and test set. During the training process, the training set is used to train a neural network by optimizing its cost function while the validation set is used to prevent the over-fitting issue. If Bayesian Neural Network is used, regularization will help to prevent over-fitting. The training is finished when stopping criteria are met.
EVALUATE
ANNHUB provides evaluation techniques such as ROC curves, confusion matrix, regression curves to evaluate the performance of the trained neural network on both training set, validation set, and test set. These ROC curves provide essential information that helps the machine learning design process.
Users can load a completely new dataset into ANNHUB to evaluate the performance of the trained model before deciding to deploy it into real-time application
EXPORT
ANNHUB supports exporting trained neural network model into a weight file. This weight file can then be loaded into different programming environments such as LabVIEW, LabVIEW NXG, LabWindow CVI, C/C++/C#, Android, iOS and Arduino using Application Programming Interface (API) provided by ANS Center. By using an appropriate programming environment, the trained model can easily be deployed into any real-time applications.
LOAD DATASET
ANNHUB supports dataset in comma-separated values (CSV) format. The outputs are identified by keywords "output, target, class." Each row in the csv file is equivalent to a data sample.
CONFIGURE
ANNHUB supports various activation types for both hidden layer and output layer. Both pre-processing and post-processing methods are provided to normalize dataset inputs and outputs. Designers can use different cost functions, together with training parameters to customize a neural network architecture that suits their applications. ANNHUB also autosuggests the suitable neural network architecture based on input data.
TRAIN
ANNHUB separates dataset into a training set, validation set, and test set. During the training process, the training set is used to train a neural network by optimizing its cost function while the validation set is used to prevent the over-fitting issue. If Bayesian Neural Network is used, regularization will help to prevent over-fitting. The training is finished when stopping criteria are met.
EVALUATE
ANNHUB provides evaluation techniques such as ROC curves, confusion matrix, regression curves to evaluate the performance of the trained neural network on both training set, validation set, and test set. These ROC curves provide essential information that helps the machine learning design process.
Users can load a completely new dataset into ANNHUB to evaluate the performance of the trained model before deciding to deploy it into real-time application
EXPORT
ANNHUB supports exporting trained neural network model into a weight file. This weight file can then be loaded into different programming environments such as LabVIEW, LabVIEW NXG, LabWindow CVI, C/C++/C#, Android, iOS and Arduino using Application Programming Interface (API) provided by ANS Center. By using an appropriate programming environment, the trained model can easily be deployed into any real-time applications.
LOAD DATASET
ANNHUB supports dataset in comma-separated values (CSV) format. The outputs are identified by keywords "output, target, class." Each row in the csv file is equivalent to a data sample.
CONFIGURE
ANNHUB supports various activation types for both hidden layer and output layer. Both pre-processing and post-processing methods are provided to normalize dataset inputs and outputs. Designers can use different cost functions, together with training parameters to customize a neural network architecture that suits their applications. ANNHUB also autosuggests the suitable neural network architecture based on input data.
TRAIN
ANNHUB separates dataset into a training set, validation set, and test set. During the training process, the training set is used to train a neural network by optimizing its cost function while the validation set is used to prevent the over-fitting issue. If Bayesian Neural Network is used, regularization will help to prevent over-fitting. The training is finished when stopping criteria are met.