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Codeless Deep Learning with KNIME

You're reading from  Codeless Deep Learning with KNIME

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781800566613
Pages 384 pages
Edition 1st Edition
Languages
Authors (3):
Kathrin Melcher Kathrin Melcher
Profile icon Kathrin Melcher
KNIME AG KNIME AG
Rosaria Silipo Rosaria Silipo
Profile icon Rosaria Silipo
View More author details

Table of Contents (16) Chapters

Preface Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform Chapter 3: Getting Started with Neural Networks Chapter 4: Building and Training a Feedforward Neural Network Section 2: Deep Learning Networks
Chapter 5: Autoencoder for Fraud Detection Chapter 6: Recurrent Neural Networks for Demand Prediction Chapter 7: Implementing NLP Applications Chapter 8: Neural Machine Translation Chapter 9: Convolutional Neural Networks for Image Classification Section 3: Deployment and Productionizing
Chapter 10: Deploying a Deep Learning Network Chapter 11: Best Practices and Other Deployment Options Other Books You May Enjoy

Testing and Applying the Network

Now that the neural network has been trained, the last step is to apply the network to the test set and evaluate its performance.

Executing the Network

To execute a trained network, you can use the Keras Network Executor node, as in Figure 4.22. The node has two input ports: a Keras network port for the trained network and a data input port for the test set or new data.

In the first tab of the configuration window, named Options, you can select, in the upper part, the backend engine, the batch size for the input data, and whether to also keep the original input columns in the output data table.

Under that, you can specify the input columns and the required conversion. Like in the Keras Network Learner node, the input specifications from the neural network are printed at the top. Remember that, since you are using the same network and the same format for the data, the settings for the input features must be the same as the ones in the Keras...

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