D E L T A - > https://tinyurl.com/4u4c2a8j
🧠NeuralForge — Deep Learning Framework for Unity
Production-ready framework for neural network inference, training, and deployment in Unity. Built for AI-driven games and realtime intelligent systems.
✨ Features Neural Networks: Dense, convolutional, recurrent layers with multiple activations
GPU Acceleration: Compute shader backend for realtime performance
Model Import: ONNX, TensorFlow, PyTorch support
Training Pipeline: In-editor training with visual monitoring
Unity Integration: Component-based, sensor inputs, AI controllers
Visualization: Tensor heatmaps, training dashboards, network inspection
📦 Requirements Unity 2022.3 LTS+
Compute Shader support
4GB+ VRAM recommended
🛠Installation Package Manager → Add from Git URL:
text https://github.com/yourusername/neural-forge.git 🚀 Quick Start csharp // Create network var network = gameObject.AddComponent(); network.Architecture = new Layer[] { new DenseLayer(128, Activation.ReLU), new DenseLayer(64, Activation.ReLU), new DenseLayer(10, Activation.Softmax) };
// Train var trainer = network.GetTrainer(); yield return trainer.TrainAsync(trainingData, epochs: 100);
// Predict Tensor output = network.Predict(input); int predictedClass = output.ArgMax(); 🧩 Components Core: NeuralNetwork, TensorProcessor, ModelImporter Sensors: VisionSensor, AudioFeatureExtractor, LidarPointCloud AI: PolicyAgent, BehaviorPredictor, UtilityNetwork
🎮 Examples Smart NPC Behavior:
csharp Tensor context = GatherEnvironmentContext(); Tensor actionScores = decisionMaker.Evaluate(context); AIAction bestAction = SelectAction(actionScores); Style Transfer:
csharp Tensor stylized = styleTransferModel.Predict(content); TensorUtils.TensorToTexture(stylized, outputArt); 🔧 Performance csharp // GPU acceleration NetworkConfig.EnableGPUInference = true;
// Model quantization var quantizedModel = network.Quantize(QuantizationType.Int8); 🧪 Samples Image Classification (MNIST)
Reinforcement Learning (CartPole)
Style Transfer
Smart NPCs
Procedural Generation
🛣 Roadmap Transformer architectures
Mobile deployment
Cloud training integration
Federated learning
📜 License
MIT © 2026 NeuralForge Contributors