NNetDesigner

NNetDesigner – Intuitive Neural Network Design, Training & Deployment

NNetDesigner provides an intuitive and visual platform for designing, training, and validating neural networks without the need for coding.  Its graphical interface enables users to easily construct and modify neural networks, offering a clear visual representation of the model’s structure.  Networks can be built from scratch or adapted from standard templates, with the flexibility to fine-tune activation functions, training parameters, and layer configurations at an individual or group level.

Seamless Data Integration & Pre-Processing

NNetDesigner supports CSV, image, and waveform data, providing built-in pre-processing tools to condition data for training. Users can normalize data, compute derivatives, apply FFTs, and extract labels from file names or data fields. Image data can be resized and padded to ensure compatibility across varying resolutions and aspect ratios, making it easy to work with diverse datasets.

Flexible Network Architecture & Customization

Along with standard nodes, convolutional layers, transformers, and mathematical operations can be incorporated, making NNetDesigner suitable for a wide range of applications, including computer vision, audio processing, and structured data analysis.  Supported activation functions include ReLU, Sigmoid, Tanh, Shockley, Gaussian, MaxPool, and Multiply, all of which can be customized per node or convolution.  Training parameters such as learning rate and momentum can also be adjusted on a per-layer or per-node basis, allowing for precise performance tuning.

Real-Time Training Monitoring & Validation

Users can track training progress through the training output graph and detailed status window, which displays iterations, error rates, training time, and best performance metrics.  Validation data can be selected as a random sample from the training set or provided separately.  During or after training, users can compare network output against actual data in a dedicated output window and export results to CSV for further analysis.

Interactive Testing & Deployment

For structured data, NNetDesigner allows users to manually enter test values or process large test datasets by reading input from CSV files and saving results with network-generated outputs.  Trained networks can be exported as C code, making them deployable in embedded systems, Windows, and Linux environments.

Optimized Performance & Scalability

NetDesigner’s modular framework ensures smooth adaptability across different applications, whether for AI research, industrial automation, signal processing, or real-time embedded systems.

With its powerful yet accessible approach, NNetDesigner empowers users to design and deploy sophisticated neural networks with ease—bridging the gap between concept and real-world implementation.

Prerequisites:

NNetDesigner is a 64-bit program and needs 64-bit Windows

NNetDesigner has no explicit minimum limitations on memory or processing speed.  However, the program will load all train and validation data into memory for training.  The user needs to be cognizant of data set sizes and available memory when setting up a net to train.  NNetDesigner will track usage and abort training if available memory gets too low.

Likewise regarding processor speed, there are no explicit limits.  Small nets with small data sets will train quickly, while large nets with large datasets will take longer and processing speed will be more of a factor.