DEL – Deep‑Learning Library for Ada

Team 19 Senior Design Project

Mission – deliver first‑class, fully type‑safe deep‑learning primitives to the Ada ecosystem.

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📑 Table of Contents

1. Why DEL?
2. Prerequisites
3. Features
4. Quick Start
5. Repository Layout
6. Framework Architecture
7. Build & Install
8. Demos
9. Data Management
10. Model Management
11. Export & Interoperate

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🚀 Why DEL?

Ada powers mission‑critical software (avionics, rail, defence, medical) yet lacks a native deep‑learning stack.
Python‑based frameworks are powerful but introduce runtime GC, certification hurdles, and large footprints.

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🔧 Prerequisites

Component	Purpose	Source
Alire Package Manager	Dependency & build manager	Alire
Python 3.9+ (optional)	Runs demo/visual‑tools	python.org
VS Code + Ada Extension (optional)	IDE support	VS Code

System requirements – Windows / Linux / macOS • ≥ 4 GB RAM (8 GB recommended) • ≥ 500 MB disk space.

Install GNAT first, then run alr get alire && alr setup and verify with alr --version.

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✨ Del Library Features

Area	Module(s)	Highlights
Tensor Operations	del.ads/adb	Matrix operations, tensor manipulation, mathematical functions
Neural Layers	del-operators.*	Linear, ReLU, Sigmoid, SoftMax, HyperTanh with forward/backward propagation
Loss Functions	del-loss.*	Cross-Entropy, Mean Square Error, Mean Absolute Error
Optimization	del-optimizers.*	SGD with momentum and weight decay support
Model Management	del-model.*	Layer composition, training loop, inference pipeline
Data Handling	del-data.*	Dataset creation, loading, and manipulation
File Formats	del-json.*, del-yaml.*	Data import from JSON and YAML formats
Export Capabilities	del-export.*	Model state persistence with visualization grid
ONNX Support	del-onnx.*	Neural network interchange format import/export
Utilities	del-utilities.*	Dataset shuffling, accuracy metrics, array helpers

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🚀 Quick Start

# Clone & build
git clone https://github.com/Slamdir/del2.0.git
cd del2.0
alr build            

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🗂 Repository Layout

src/
  del.ads / del.adb         -- tensor core
  del-operators.*           -- algebra kernels
  del-model.*               -- model container & layers
  del-loss.*                -- loss functions
  del-optimizers.*          -- optimizers
  del-utilities.*           -- helpers (init, logging…)
  del-data.*                -- dataset helpers
  del-json.* / del-yaml.*   -- parsers
  del-export.*              -- flat‑file exporter
  del-onnx.*                -- ONNX emitter
demos/                      -- demo programs
del.gpr                     -- GNAT project file

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🏗 Framework Architecture

Module	Description
del-core	Core types & initialization (root package)
del-model	Neural‑network container & layer registry
del-operators	Activation & linear algebra kernels
del-loss	Loss‑function implementations
del-optimizers	Optimisation algorithms
del-data	Data loading & batching helpers
del-json / del-yaml	Lightweight parsers for datasets & configs
del-onnx	ONNX exporter
del-export	Flat JSON weight exporter
del-utilities	Misc helpers (initialisers, logging, CLI)

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🛠 Build & Install

With Alire (recommended)

alr build           # static build
# optional system‑wide install

🎓 Demos

# Generate synthetic classification data
python3 dev_tools/generator.py

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📂 Data Management

DEL ingests JSON & YAML datasets and experiment configs.

# sample YAML dataset
data:
  - [-86, -52]
  - [-100, -89]
labels:
  - 2
  - 4

-- Load data from file with automatic format detection
Model.Load_Data_From_File(
   Filename     => "data.yaml",
   Data_Shape   => (1, 2),
   Target_Shape => (1, 4));

-- Export model with predictions after training
Model.Export_To_JSON("output.json");

The library includes a Combine_Dataset_Samples function that can merge multiple training samples into a consolidated dataset, which is useful for creating batched training data from individual examples.

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🧰 Dev Tools

⚙️ Data Generator

To use this data generation tool, developers can run the Python script with command-line arguments to create 2D/ND grid patterns or 2D spiral patterns. The tool generates JSON files with coordinate data and optional labels, which are stored in a timestamped file within the project directory.

Quick Start

• View all parameters: python generator.py --help
• Generate a default spiral dataset: python generator.py -s 2 -q 1000

Key Options

Parameter	Description	Default
-s/--shape	1=Grid, 2=Spiral	2
-q/--quantity	Total data points	300
-d/--dimension	Data dimensions (2 for spirals)	2
-t/--types	Classes (grid) or spiral arms	3
-l/--lower	Minimum value for grid data	-1
-u/--upper	Maximum value for grid data	1
--no-labels	Disables label generation	Enabled

Spiral-Specific Parameters

• -n: Controls radial noise (default: 0.5)
• -r: Sets number of full rotations (default: 0.5)

Output Details

Generated JSON files are saved to demos/demo-data/[TIMESTAMP].json. Each file contains:

• data: Array of coordinate pairs (e.g., [[0.34, -0.12], [0.88, 0.24]])
• labels: Numeric class identifiers (omitted if --no-labels is used)

Grid Mode Notes

• Works in any dimensionality (adjust with -d)
• Labels use types^dimension classification logic for grid generation

🎥 Visualizer

🔁 Export & Interoperate

Format	Module	Notes
ONNX	del-onnx.*	Compatible with PyTorch, TensorFlow, ORT
JSON	del-export.*	Human‑readable weights, includes visualization grid
YAML	del-yaml.*	Config‑driven experiments