This workflow shows the different options of training and executing a network using TF2 on the example of an autoencoder:
Option 1: Define network using Keras Layer nodes, train and execute using TF2
Option 2: Define network using Python code, train and execute using TF2
Option 3. Define and train the network using Keras, convert and save as TF2 model, read TF2 model, and execute network using TF2.
Workflow
Different options to train an autoencoder using TensorFlow 2
Used extensions & nodes
Created with KNIME Analytics Platform version 4.2.0
- Go to item
- Go to item
- Go to item
- Go to item
- Go to item
- Go to item
Loading deployments
Loading ad hoc jobs
Legal
By using or downloading the workflow, you agree to our terms and conditions.