  Machine learning techniques have been increasingly useful in astronomical
applications over the last few years, for example in the morphological
classification of galaxies. Convolutional neural networks have proven to be
highly effective in classifying objects in image data. The current work aims to
establish when multiple components are present, in the astronomical context of
synthesis imaging observations of radio sources. To this effect, we design a
convolutional neural network to differentiate between different morphology
classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project.
In this first step, we focus on exploring the factors that affect the
performance of such neural networks, such as the amount of training data,
number and nature of layers and the hyperparameters. We begin with a simple
experiment in which we only differentiate between two extreme morphologies,
using compact and multiple component extended sources. We found that a three
convolutional layer architecture yielded very good results, achieving a
classification accuracy of 97.4% on a test data set. The same architecture was
then tested on a four-class problem where we let the network classify sources
into compact and three classes of extended sources, achieving a test achieving
a test accuracy of 93.5%. The best-performing convolutional neural network
setup has been verified against RGZ Data Release 1 where a final test accuracy
of 94.8% was obtained, using both original and augmented images. The use of
sigma clipping does not offer a significant benefit overall, except in cases
with a small number of training images.
