Tensorflow C++ api

TensorFlow General Discussion
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Hello,

I am looking to implement a deep learning model using the TensorFlow C++ API to perform inference on an Android phone. I prefer to use C++ because it allows me to easily modify the model’s operation

While I have found examples of using pretrained TensorFlow models for inference with C++, I am struggling to find any code or documentation on how to implement a model directly in C++. Could you please provide some examples or documentation on how to create a deep learning model using the TensorFlow C++ API?

Your assistance would be greatly appreciated.

Thank you.

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Official TensorFlow 2.16 + Python 3.12 – JARaaS Hybrid RAG - 6/17/2024
Note: Sources at the end of the response.

Hello,

Implementing a deep learning model directly using the TensorFlow C++ API can be a bit challenging due to the scarcity of comprehensive documentation compared to the Python APIs. However, I can guide you on how to proceed and provide some relevant resources to help you get started.

Steps to Implement a Deep Learning Model Using TensorFlow C++ API:

  1. Set Up TensorFlow C++ Environment:

    • You need to have TensorFlow built and installed on your system. TensorFlow provides detailed instructions on how to build TensorFlow from source (source).

  2. Include TensorFlow Headers:

    • Make sure you include the necessary TensorFlow headers in your C++ code:
      #include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"

  3. Create the Model graph:

    • TensorFlow C++ API does not directly support high-level API like Keras in Python. Therefore, you would need to define your computation graph manually. Here is an example of creating a simple linear model:
      tensorflow::Scope root = tensorflow::Scope::NewRootScope();

auto X = tensorflow::ops::Placeholder(root.WithOpName("X"), tensorflow::DT_FLOAT);
auto W = tensorflow::ops::Variable(root.WithOpName("W"), {input_dim, output_dim}, tensorflow::DT_FLOAT);
auto b = tensorflow::ops::Variable(root.WithOpName("b"), {output_dim}, tensorflow::DT_FLOAT);
auto Y = tensorflow::ops::Add(root.WithOpName("Y"), tensorflow::ops::MatMul(root, X, W), b);

  4. Initialize Variable:

    • To initialize the variables:
      tensorflow::ClientSession session(root);
TF_CHECK_OK(session.Run({tensorflow::ops::Assign(root, W, tensorflow::ops::RandomNormal(root, {input_dim, output_dim})),
                         tensorflow::ops::Assign(root, b, tensorflow::ops::Zeros(root, {output_dim}))}, nullptr));

  5. Run the Session:

    • Run the session to perform inference or training:
      std::vector<tensorflow::Tensor> outputs;
TF_CHECK_OK(session.Run({{X, input_tensor}}, {Y}, &outputs));

Resources:

Here are some useful resources and documentation for TensorFlow C++ API:

  • TensorFlow C++ API Reference: This provides a reference for the TensorFlow C++ API.
    TensorFlow C++ API

  • TensorFlow GitHub Repository: The TensorFlow repo often has examples of using TensorFlow with C++. Check the tensorflow/cc directory.
    TensorFlow GitHub

  • TensorFlow Forum and Stack Overflow: For specific queries, TensorFlow’s own forum or Stack Overflow can be very useful.

TensorFlow for Mobile:

For deploying the TensorFlow model on Android, you might also want to look into TensorFlow Lite, which is specifically designed for mobile and embedded devices.

If you need an example or more detailed documentation, I recommend exploring the TensorFlow GitHub repository in the tensorflow/cc directory and checking out the TensorFlow Lite documentation for mobile deployment specifics.

Sources:

Sources:

  • TensorFlow C++ API: bib.md (internal document)
  • TensorFlow Layers API: custom_layers.ipynb (internal document)
  • TensorFlow Linear Models: linear.md (internal document)

I hope this information helps you get started with implementing your deep learning model using the TensorFlow C++ API on Android. If you have any further questions, feel free to ask!

Thank you.

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