Below is an example of a README.md written in English for the MiniFlow repository. You can adjust details (such as installation instructions, usage examples, and license type) as needed:

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MiniFlow

MiniFlow is a lightweight C++ library for creating neural networks. It is inspired by the simplicity and design of the Python MiniFlow library and adapts its core concepts to C++. The goal of MiniFlow is to provide an intuitive, modular framework that makes it easy to experiment with neural network architectures and training routines in C++.

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Table of Contents

• Overview
• Folder Structure
• Getting Started
• Usage
• Building the Project
• Contributing
• License
• Acknowledgments

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Overview

MiniFlow offers a simple API for constructing, training, and evaluating neural networks in C++. Whether you are looking to learn about neural network fundamentals or integrate custom network components into a larger C++ project, MiniFlow provides a minimal framework to get you started.

Key features include:

• Simplicity: Minimalistic design for easy understanding and extension.
• Modularity: Separate components for data, network layers, and training routines.
• Extensibility: Easily integrate custom layers and training algorithms.

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Folder Structure

The repository is organized as follows:

• BostonData/
  Contains sample datasets (for example, the Boston Housing dataset) to experiment with network training and evaluation.

• MiniFlow/
  May include example scripts and demo applications that illustrate how to use the library.

• MiniFlowLib/
  Houses the core library source code. This is where the building blocks of the neural network (such as layer definitions, forward/backward propagation routines, etc.) are implemented.

• NeuralNetwork/
  Contains higher-level implementations and sample neural network architectures that utilize the MiniFlowLib components.

• MiniFlow.sln
  A Visual Studio solution file to help build and run the project on Windows.

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Getting Started

Prerequisites

• A C++ compiler (Visual Studio 2019 or later is recommended for Windows users).
• CMake (if you plan to build on non-Windows platforms; see below for notes).
• Standard C++ libraries (the project uses C++11/14 features).

Installation

1. Clone the Repository

   git clone https://github.com/SkivHisink/MiniFlow.git

2. Navigate to the Repository Directory

   cd MiniFlow

3. Open the Project

   • Windows: Open MiniFlow.sln in Visual Studio and build the solution.
   • Other Platforms: (If applicable) Create a CMake project or makefile based on the provided source code.

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Usage

Below is a simple example of how you might use MiniFlow in your own C++ project:

#include "NeuralNetwork/NeuralNetwork.h"

int main() {
    // Create an instance of your neural network
    NeuralNetwork nn;

    // Configure your network (e.g., add layers, set learning rate)
    nn.addLayer(new DenseLayer(10, 5));  // Example: from 10 inputs to 5 neurons
    nn.addLayer(new ActivationLayer("relu"));

    // Load sample data from the BostonData folder (implementation-specific)
    DataSet trainingData = DataLoader::load("BostonData/training.csv");

    // Train the network
    nn.train(trainingData, /* epochs */ 100, /* learning rate */ 0.01);

    // Evaluate the network
    double accuracy = nn.evaluate(trainingData);
    std::cout << "Training Accuracy: " << accuracy << std::endl;

    return 0;
}

Note: The above code is illustrative. Adjust class names and function calls based on the actual implementation provided in MiniFlowLib and NeuralNetwork folders.

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Building the Project

• Windows:
  Open MiniFlow.sln with Visual Studio and build the solution.

• Linux/macOS:
  If you prefer to use CMake, create a CMakeLists.txt file (if not already provided) and configure your build environment. For example:

  cmake_minimum_required(VERSION 3.10)
  project(MiniFlow)
  
  set(CMAKE_CXX_STANDARD 14)
  
  # Add directories containing the library source files
  add_subdirectory(MiniFlowLib)
  add_subdirectory(NeuralNetwork)
  
  # Add your executable target and link against the library targets
  add_executable(MiniFlowDemo MiniFlow/main.cpp)
  target_link_libraries(MiniFlowDemo PRIVATE MiniFlowLib NeuralNetwork)

Then, run:

mkdir build && cd build
cmake ..
make

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Contributing

Contributions are welcome! If you have suggestions, bug fixes, or new features to add, please feel free to open an issue or submit a pull request.

Before contributing, please review our Contributing Guidelines (if available) to ensure a smooth process.

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License

This project is distributed under the MIT License. See the LICENSE file for details.

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Acknowledgments

• Inspiration from the original MiniFlow Python library and various educational resources on neural network design.
• Thanks to the contributors and maintainers of open-source neural network projects for their valuable insights.

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Happy coding!

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This README serves as a starting point for using and extending MiniFlow. Adjust and expand the sections as your project evolves.