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

Defining and Training the Network Architecture

The process of designing and training the network is similar to the process used in the previous NLP case studies.

Designing the Network

In this case, we want to use a network with five layers:

  • A Keras input layer to define the input shape
  • A Keras LSTM layer for the sequence analysis
  • A Keras dropout layer for regularization
  • A Keras dense layers with linear activation
  • A Keras softmax layer to transform the output into a probability distribution

The number of unique characters in the training set – that is, the character set size – is 95. Since we allow sequences of variable length, the shape of the input layer is ?, 95. The ? stands for a variable sequence length.

Next, we have the Keras LSTM Layer node. This time, it is important to activate the Return sequences and Return state checkboxes, as we need the intermediate output states during the training process and the cell state in...