Tensorflow_cpp_installation_tutorial

This repository showcases how to use TensorFlow with C++ for machine learning and deep learning applications. It includes examples, installation guides, and tutorials for setting up and running TensorFlow C++ applications on different operating systems.

Contents

• .gitignore - Specifies intentionally untracked files to ignore.
• README.md - This file, providing an overview and documentation of the project.
• build_linear_regression.ipynb - A Jupyter notebook demonstrating the building of a linear regression model using TensorFlow in C++.
• frozen_models - Directory containing frozen models for TensorFlow inference.
• hello_tf.c - A simple C program to test TensorFlow installation and basic functionality.
• inference_frozen_model.cpp - C++ code for running inference using a frozen TensorFlow model.
• install_tensorflow_c_api.md - Guide on installing TensorFlow C API on various systems.
• the_model.ipynb - A Jupyter notebook detailing the process of creating and training a model with TensorFlow in C++.
• try_again.sh - A shell script related to the project setup or execution.
• tutorial_macOS.md - A detailed tutorial on setting up TensorFlow for C++ on macOS.
• tutorial_windows.md - A comprehensive guide for installing and running TensorFlow C++ applications on Windows.

Getting Started

Prerequisites

Ensure you have the following installed:

• TensorFlow C API
• C++ compiler
• Jupyter Notebook (for .ipynb files)

Installation

Follow the instructions in install_tensorflow_c_api.md to set up the TensorFlow C API on your machine.

Running the Examples

• To test your TensorFlow setup, compile and run hello_tf.c.
• For inference examples, see inference_frozen_model.cpp.
• The Jupyter notebooks (build_linear_regression.ipynb, the_model.ipynb) provide interactive examples of model building and training.

Tutorials

• macOS Users: Check out tutorial_macOS.md for a step-by-step guide to setting up your environment.
• Windows Users: tutorial_windows.md offers detailed instructions for Windows setup.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues to improve the repository.

License

This project is open source and available under the MIT License.