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Keras2Cpp

Keras2Cpp is a C++ developer tool that exports supported Keras neural-network architecture and weights into a plain-text representation and provides code to run model inference.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Keras2Cpp — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a model fits the documented simple convolutional-network support and you want to run inference from C++ rather than Keras.

Who it is for

  • Developers moving inference for a supported Keras model into a C++ application.
  • Practitioners working with simple convolutional networks that use the documented activations and Theano backend.

Documented uses

  • Export a Keras model’s architecture and weights to a plain-text file.
  • Compile and run model inference using the repository’s C++ implementation.
  • Compare Keras and C++ predictions on a generated sample using the documented test script.
Before you choose

Requirements & limits

The public GitHub repository specifies an MIT license.

  • The README documents simple convolutional networks, not general coverage of Keras architectures.
  • Only ReLU and Softmax activations are described as implemented.
  • The documented backend is Theano.
  • The C++ example is compiled with a C++11 compiler.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Export and compile the documented MNIST example

Convert the example CNN architecture and weights into the plain-text format and compile its C++ example.

Before you start

  • The repository’s MNIST example script and Keras model architecture and weights.
  • The exporter script, C++ model files, and example main file from the repository.
  • A C++11 compiler.

Steps

  1. Run example/mnist_cnn_one_iteration.py to produce the example architecture JSON and HDF5 weights.

  2. Run the command below.

    Documented command
    python dump_to_simple_cpp.py -a example/my_nn_arch.json -w example/my_nn_weights.h5 -o example/dumped.nnet

  3. Compile with g++ -std=c++11 keras_model.cc example_main.cc.

Expected outcome

The documented example produces a compiled C++ program using the exported model.

Guide limitations

  • The README’s example includes running the resulting binary, but this guide covers only the documented export and compile stages.
  • The implementation describes support for simple convolutional networks and only ReLU and Softmax activations; it specifies the Theano backend.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Keras2CppExport a Keras model’s architecture and weights to a plain-text file.LinuxThe public GitHub repository specifies an MIT license.
Chainer ChemistryTrain a model on a dataset.Linux, macOS, WindowsThe repository identifies the project as open source under the MIT license.
Omega AIConstruct neural networks and train or test models in Java.ServerThe project is identified as open source and uses the Apache-2.0 license.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
7 November 2026
Original website title
pplonski/keras2cpp · Official homepage title · retained in its source language
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