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DL 050 Tensorflow2 Basic MLP

TensorFlow 2Deep LearningTfTensorFlowMLP
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Jun 18, 2025 3:47 PM
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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.

adapted from: https://kni.me/w/Z1BLynW6P1l14odY

please download the complete DeepLearning (Keras, Tensorflow, H2O.ai) Workflow group:
https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_deeplearning_keras_tensorflow_classification~G8jl-DTMCBqoxyB9/

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In order to run the example, please make sure you have the following KNIME extensions installed:

* KNIME Deep Learning - TensorFlow 2 Integration (Labs)

You also need a local Python installation that includes TensorFlow 2. Please refer to https://docs.knime.com/latest/deep_learning_installation_guide/#dl_python_setup for installation recommendations and further information.

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
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