Neural: A Neural Network Programming Language

Simplify deep learning development with a powerful DSL, cross-framework support, and built-in debugging

⚠️ BETA STATUS: Neural-dsl is under active development—bugs may exist, feedback welcome! Not yet recommended for production use.

📋 Table of Contents

• Overview
• Pain Points Solved
• Key Features
• Installation
• Quick Start
• Debugging with NeuralDbg
• Why Neural?
• Documentation
• Examples
• Contributing
• Community
• Support

Overview

Neural is a domain-specific language (DSL) designed for defining, training, debugging, and deploying neural networks. With declarative syntax, cross-framework support, and built-in execution tracing (NeuralDbg), it simplifies deep learning development whether via code, CLI, or a no-code interface.

Pain Points Solved

Neural addresses deep learning challenges across Criticality (how essential) and Impact Scope (how transformative):

Criticality / Impact	Low Impact	Medium Impact	High Impact
High			- Shape Mismatches: Pre-runtime validation stops runtime errors. - Debugging Complexity: Real-time tracing & anomaly detection.
Medium		- Steep Learning Curve: No-code GUI eases onboarding.	- Framework Switching: One-flag backend swaps. - HPO Inconsistency: Unified tuning across frameworks.
Low	- Boilerplate: Clean DSL syntax saves time.	- Model Insight: FLOPs & diagrams. - Config Fragmentation: Centralized setup.

Why It Matters

• Core Value: Fix critical blockers like shape errors and debugging woes with game-changing tools.
• Strategic Edge: Streamline framework switches and HPO for big wins.
• User-Friendly: Lower barriers and enhance workflows with practical features.

Feedback

Help us improve Neural DSL! Share your feedback: Typeform link.

Features

• YAML-like Syntax: Define models intuitively without framework boilerplate.
• Shape Propagation: Catch dimension mismatches before runtime.
  • ✅ Interactive shape flow diagrams included.
• Multi-Framework HPO: Optimize hyperparameters for both PyTorch and TensorFlow with a single DSL config (#434).
• Multi-Backend Export: Generate code for TensorFlow, PyTorch, or ONNX.
• Training Orchestration: Configure optimizers, schedulers, and metrics in one place.
• Visual Debugging: Render interactive 3D architecture diagrams.
• Extensible: Add custom layers/losses via Python plugins.
• NeuralDbg: Built-in Neural Network Debugger and Visualizer.
• No-Code Interface: Quick Prototyping for researchers and ean ducational, accessible tool for beginners.

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NeuralDbg: Built-in Neural Network Debugger

NeuralDbg provides real-time execution tracing, profiling, and debugging, allowing you to visualize and analyze deep learning models in action.

✅ Real-Time Execution Monitoring – Track activations, gradients, memory usage, and FLOPs.

✅ Shape Propagation Debugging – Visualize tensor transformations at each layer. ✅ Gradient Flow Analysis – Detect vanishing & exploding gradients. ✅ Dead Neuron Detection – Identify inactive neurons in deep networks. ✅ Anomaly Detection – Spot NaNs, extreme activations, and weight explosions. ✅ Step Debugging Mode – Pause execution and inspect tensors manually.

Installation

Prerequisites: Python 3.8+, pip

Option 1: Install from PyPI (Recommended)

# Install the latest stable version
pip install neural-dsl

# Or specify a version
pip install neural-dsl==0.2.5  # Latest version with HPO optimizer fixes

Option 2: Install from Source

# Clone the repository
git clone https://github.com/Lemniscate-world/Neural.git
cd Neural

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Quick Start

1. Define a Model

Create a file named mnist.neural with your model definition:

network MNISTClassifier {
  input: (28, 28, 1)  # Channels-last format

  layers:
    Conv2D(filters=32, kernel_size=(3,3), activation="relu")
    MaxPooling2D(pool_size=(2,2))
    Flatten()
    Dense(units=128, activation="relu")
    Dropout(rate=0.5)
    Output(units=10, activation="softmax")

  loss: "sparse_categorical_crossentropy"
  optimizer: Adam(learning_rate=0.001)
  metrics: ["accuracy"]

  train {
    epochs: 15
    batch_size: 64
    validation_split: 0.2
  }
}

2. Run or Compile the Model

# Generate and run TensorFlow code
neural run mnist.neural --backend tensorflow --output mnist_tf.py

# Or generate and run PyTorch code
neural run mnist.neural --backend pytorch --output mnist_torch.py

3. Visualize Architecture

neural visualize mnist.neural --format png

This will create visualization files for inspecting the network structure and shape propagation:

• architecture.png: Visual representation of your model
• shape_propagation.html: Interactive tensor shape flow diagram
• tensor_flow.html: Detailed tensor transformations

4. Debug with NeuralDbg

neural debug mnist.neural

Open your browser to http://localhost:8050 to monitor execution traces, gradients, and anomalies interactively.

5. Use the No-Code Interface

neural --no_code

Open your browser to http://localhost:8051 to build and compile models via a graphical interface.

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🛠 Debugging with NeuralDbg

🔹 1️⃣ Start Real-Time Execution Tracing

python neural.py debug mnist.neural

Features: ✅ Layer-wise execution trace ✅ Memory & FLOP profiling ✅ Live performance monitoring

🔹 2️⃣ Analyze Gradient Flow

python neural.py debug --gradients mnist.neural

Detect vanishing/exploding gradients with interactive charts.

🔹 3️⃣ Identify Dead Neurons

python neural.py debug --dead-neurons mnist.neural

🛠 Find layers with inactive neurons (common in ReLU networks).

🔹 4️⃣ Detect Training Anomalies

python neural.py debug --anomalies mnist.neural

Flag NaNs, weight explosions, and extreme activations.

🔹 5️⃣ Step Debugging (Interactive Tensor Inspection)

python neural.py debug --step mnist.neural

🔍 Pause execution at any layer and inspect tensors manually.

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Why Neural?

Feature	Neural	Raw TensorFlow/PyTorch
Shape Validation	✅ Auto	❌ Manual
Framework Switching	1-line flag	Days of rewriting
Architecture Diagrams	Built-in	Third-party tools
Training Config	Unified	Fragmented configs

🔄 Cross-Framework Code Generation

Neural DSL	TensorFlow Output	PyTorch Output
Conv2D(filters=32)	tf.keras.layers.Conv2D(32)	nn.Conv2d(in_channels, 32)
Dense(units=128)	tf.keras.layers.Dense(128)	nn.Linear(in_features, 128)

Benchmarks

Task	Neural	Baseline (TF/PyTorch)
MNIST Training	1.2x ⚡	1.0x
Debugging Setup	5min 🕒	2hr+

Documentation

• DSL Documentation

Explore advanced features:

• Custom Layers Guide
• ONNX Export Tutorial
• Training Configuration
• NeuralDbg Debugging Features

Examples

Explore common use cases in examples/ with step-by-step guides in docs/examples/:

• MNIST Classifier Guide
• Sentiment Analysis Guide
• Transformer for NLP Guide

🕸 Architecture Graphs

Note: You may need to zoom in to see details in these architecture diagrams.

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Contributing

We welcome contributions! See our:

• Contributing Guidelines
• Code of Conduct
• Roadmap

To set up a development environment:

git clone https://github.com/Lemniscate-world/Neural.git
cd Neural
pip install -r requirements-dev.txt  # Includes linter, formatter, etc.
pre-commit install  # Auto-format code on commit

Star History

Support

If you find Neural useful, please consider supporting the project:

• ⭐ Star the repository: Help us reach more developers by starring the project on GitHub
• 🔄 Share with others: Spread the word on social media, blogs, or developer communities
• 🐛 Report issues: Help us improve by reporting bugs or suggesting features
• 🤝 Contribute: Submit pull requests to help us enhance Neural (see Contributing)

Repository Status

This repository has been cleaned and optimized for better performance. Large files have been removed from the Git history to ensure a smoother experience when cloning or working with the codebase.

Community

Join our growing community of developers and researchers:

• Discord Server: Chat with developers, get help, and share your projects
• Twitter @NLang4438: Follow for updates, announcements, and community highlights
• GitHub Discussions: Participate in discussions about features, use cases, and best practices

Building the future of neural network development, one line of DSL at a time.