  Convolutional Neural Networks (CNNs) dominate various computer vision tasks
since Alex Krizhevsky showed that they can be trained effectively and reduced
the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual
recognition challenge. Many aspects of CNNs are examined in various
publications, but literature about the analysis and construction of neural
network architectures is rare. This work is one step to close this gap. A
comprehensive overview over existing techniques for CNN analysis and topology
construction is provided. A novel way to visualize classification errors with
confusion matrices was developed. Based on this method, hierarchical
classifiers are described and evaluated. Additionally, some results are
confirmed and quantified for CIFAR-100. For example, the positive impact of
smaller batch sizes, averaging ensembles, data augmentation and test-time
transformations on the accuracy. Other results, such as the positive impact of
learned color transformation on the test accuracy could not be confirmed. A
model which has only one million learned parameters for an input size of
32x32x3 and 100 classes and which beats the state of the art on the benchmark
dataset Asirra, GTSRB, HASYv2 and STL-10 was developed.
