Example Code

LabVIEW Deep Learning with Python Keras API.

Products and Environment

This section reflects the products and operating system used to create the example.

To download NI software, including the products shown below, visit ni.com/downloads.


  • LabVIEW

    Operating System

  • Windows

    Programming Language

  • Python

Code and Documents





This example shows how to use the deep learning API to perform numeric classification using the Python Keras library. The model is of sequential type and is compiled using the optimizer provided by Keras. Trained using the mnist dataset, this model recognizes and classifies numbers you draw on the front panel.




This example compiles and trains the model defined in main.py. The model uses a mnist dataset of 28x28 size images provided by Keras and outputs the result by predicting the number drawn on the front panel.


Hardware and Software Requirements


  • LabVIEW 2018 64-bit and later
  • Python 3.6 64-bit
  • TensorFlow Python library.
  • Keras Python library.


Steps to Implement or Execute Code


  1. Make sure you have TensorFlow and Keras installed in your Python environment.
  2. Open mnist Keras.vi.
  3. Run the VI. After the model has been compiled and trained, the Status indicator changes to "Model_Predicting".
  4. Drag the mouse into the draw pane to draw a number and the model will output the prediction.


Additional Information or References


Front Panel: 




Jinho Choi

Staff Technical Support Engineer

Example code from the Example Code Exchange in the NI Community is licensed with the MIT license.