  Convolutional Neural Networks (CNNs) usually use the same activation
function, such as RELU, for all convolutional layers. There are performance
limitations of just using RELU. In order to achieve better classification
performance, reduce training and testing times, and reduce power consumption
and memory usage, a new "Compressed Multi-function CNN" is developed. Google's
Inception-V4, for example, is a very deep CNN that consists of 4 Inception-A
blocks, 7 Inception-B blocks, and 3 Inception-C blocks. RELU is used for all
convolutional layers. A new "Compressed Multi-function Inception-V4" (CMI) that
can use different activation functions is created with k Inception-A blocks, m
Inception-B blocks, and n Inception-C blocks where k in {1, 2, 3, 4}, m in {1,
2, 3, 4, 5, 6, 7}, n in {1, 2, 3}, and (k+m+n)<14. For performance analysis, a
dataset for classifying brain MRI images into one of the four stages of
Alzheimer's disease is used to compare three CMI architectures with
Inception-V4 in terms of F1-score, training and testing times (related to power
consumption), and memory usage (model size). Overall, simulations show that the
new CMI models can outperform both the commonly used Inception-V4 and
Inception-V4 using different activation functions. In the future, other
"Compressed Multi-function CNNs", such as "Compressed Multi-function ResNets
and DenseNets" that have a reduced number of convolutional blocks using
different activation functions, will be developed to further increase
classification accuracy, reduce training and testing times, reduce
computational power, and reduce memory usage (model size) for building more
effective healthcare systems, such as implementing accurate and convenient
disease diagnosis systems on mobile devices that have limited battery power and
memory.

Computer Vision and Pattern Recognition