Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • hilal
  • Spaces
  • Public
  • 01_Using_DeepLearning4J_to_classify_MNIST_Digits
WorkflowWorkflow

Classifying handwritten digits using KNIME, DL4J and a LeNet variant

Deep learning GPU Image classification Digit recognition Le Net

Last edited: 

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
The workflow downloads, uncompresses and preprocesses the original MNIST dataset. The two "Normalize Images" components use the KNIME Streaming functionality to convert the input files into KNIME image cells that can be used by the DL4J Learner and Predictor. The "LeNet" metanode (taken from the Node Repository) is a variant of the originally described LeNet convolutional neural network. The images and the DL4J model is then used by the Learner to train a model (saved using the DL4J Model Writer), which is then applied to the test set, which is finally scored.

External resources

  • KNIME Image Processing - Deeplearning4J Integration (64bit only) extension
  • The MNIST DATABASE of handwritten digits
  • Learning Deep Learning. A tutorial on KNIME Deeplearning4J Integration

Used extensions & nodes

Created with KNIME Analytics Platform version 4.1.0 Note: Not all extensions may be displayed.
  • Go to item
    KNIME Core Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

  • Go to item
    KNIME Deeplearning4J Integration (64bit only) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

  • Go to item
    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.1

  • Go to item
    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

  • Go to item
    Vernalis KNIME Nodes Trusted extension

    Vernalis Research Ltd, Cambridge, UK

    Version 1.24.4

  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item

Legal

By using or downloading the workflow, you agree to our terms and conditions.

Discussion
Discussions are currently not available, please try again later.

KNIME
Open for Innovation

KNIME AG
Hardturmstrasse 66
8005 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Server
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits