Workflow
Train a simple Multilayer Perceptron using TensorFlow 2
Train a simple Multilayer Perceptron using TensorFlow 2 for a binary classification
This workflow shows how to train a simple multilayer perceptron for classification. It is demonstrated how the "DL Python Network Creator" can be used to create a simple neural network using the tf.keras API and how the "DL Python Network Learner" can be used to train the created network on data.
Please note this example should demonstrate how to set up the deep learning environment with Tensor Flow 2 and provide a working simple example.
External resources
- (knime forum hint) Tensorflow and python-flatbuffers<2.0
- KNIME Hub - Train a simple Multilayer Perceptron using TensorFlow 2
- compact KNIME forum - "KNIME - Python and Deep Learning"
- Medium: KNIME and Python — Setting up and managing Conda environments
- (official) KNIME Deep Learning Integration Installation Guide
- Meta Collection about KNIME and Python
- (official) KNIME Python Integration Guide
- please download the complete DeepLearning (Keras, Tensorflow, H2O.ai) Workflow group
- Codeless Deep Learning with KNIME
- TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras
- adapted from: Train a simple Multilayer Perceptron using TensorFlow 2
- KNIME Deep Learning Integration Installation Guide
Used extensions & nodes
Created with KNIME Analytics Platform version 4.6.4
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