NeuralFlow-Core

NeuralFlow-Core is a high-performance, lightweight deep learning framework designed for efficiency and flexibility. It provides a seamless interface for building and training complex neural networks, with a focus on speed and scalability.

Key Features

• High Performance: Optimized for both CPU and GPU execution, ensuring fast training and inference.
• Lightweight Architecture: Minimal dependencies and a clean codebase for easy integration and customization.
• Flexible API: Intuitive Python and C++ APIs for building diverse neural network architectures.
• Scalability: Designed to handle large-scale datasets and complex models with ease.
• Extensibility: Easily add new layers, activation functions, and optimization algorithms.

Getting Started

Prerequisites

• Python 3.7+
• C++ Compiler (GCC 7+, Clang 5+, or MSVC 2017+)
• CMake 3.10+
• (Optional) CUDA 10.0+ for GPU acceleration

Installation

git clone https://github.com/FunctionFlow1/NeuralFlow-Core.git
cd NeuralFlow-Core
mkdir build && cd build
cmake ..
make -j$(nproc)
sudo make install

Usage Example (Python)

import neuralflow as nf

# Define a simple neural network
model = nf.Sequential([
    nf.layers.Dense(128, activation='relu', input_shape=(784,)),
    nf.layers.Dropout(0.2),
    nf.layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)

# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print(f"Test Accuracy: {accuracy:.4f}")

Contributing

We welcome contributions from the community! Please read our Contributing Guidelines for more information.

License

NeuralFlow-Core is released under the MIT License.