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Tensorflow Qrnn

Tensorflow Qrnn is a Python implementation of the quasi-recurrent neural network (QRNN) architecture for TensorFlow, with scripts for checking forward propagation and comparing QRNN with an LSTM baseline on scikit-learn’s digit dataset.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tensorflow Qrnn — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a code-level QRNN reference or want to reproduce and adapt the repository’s documented forward-propagation and digit-dataset working checks.

Who it is for

  • Developers exploring QRNNs in TensorFlow.
  • Researchers or engineers who want to inspect forward-propagation calculations or use the digit-dataset comparison as an experiment starting point.

Documented uses

  • Use a QRNN implementation in a TensorFlow sequence-modeling experiment.
  • Check QRNN forward propagation with the included test script.
  • Compare QRNN and LSTM working checks on scikit-learn’s digit dataset.
  • View the example calculation results with TensorBoard.
Before you choose

Requirements & limits

The repository is public and licensed under MIT.

  • The README specifies TensorFlow 0.12.0; scikit-learn 0.18.1 is specified for the working check.
  • The documented comparison uses scikit-learn’s digit dataset, so its reported results are specific to that example.
  • The README includes a TensorBoard command for inspecting calculation results.

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

Run the QRNN and LSTM working check

Compare the repository’s QRNN and LSTM baseline working check on scikit-learn’s digit dataset.

Before you start

  • Python environment with TensorFlow 0.12.0 and scikit-learn 0.18.1, the versions listed for the working check.
  • The repository files, including test_tf_qrnn_work.py.

Steps

  1. Run the command below from the repository.

    Documented command
    python test_tf_qrnn_work.py

  2. To inspect calculation results, run tensorboard --logdir=./summary/qrnn as shown in the README.

Expected outcome

The repository describes the script as a QRNN-versus-LSTM working check and provides a TensorBoard command for viewing calculation results.

Guide limitations

  • The README’s reported results concern its digit-dataset example and do not establish performance on other tasks.
  • The TensorBoard command assumes the documented summary directory.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
17 October 2026
Original website title
icoxfog417/tensorflow_qrnn · Official homepage title · retained in its source language
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