Book Image

Codeless Deep Learning with KNIME

By : Kathrin Melcher, KNIME AG, Rosaria Silipo
Book Image

Codeless Deep Learning with KNIME

By: Kathrin Melcher, KNIME AG, Rosaria Silipo

Overview of this book

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.
Table of Contents (16 chapters)
1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

Training the Network

We have the data ready and we have the network. The goal of this section is to show you how to train the network with the data in the training set. This requires the selection of the loss function, the setting of the training parameters, the specification of the training set and the validation set, and the tracking of the training progress.

The key node for network training and for all these training settings is the Keras Network Learner node. This is a really powerful, really flexible node, with many possible settings, distributed over four tabs: Input Data, Target Data, Options, and Advanced Options.

The Keras Network Learner node has three input ports:

  • Top port: The neural network you want to train
  • Middle port: The training set
  • Lowest port: The optional validation set

It has one output port, exporting the trained network.

In addition, the node has the Learning Monitor view, which you can use to monitor the network training progress...