The presentation of my paper titled "#NumPyCNNAndroid: A Library for Straightforward Implementation of #ConvolutionalNeuralNetworks for #Android Devices" at the second International Conference of Innovative Trends in #ComputerEngineering (ITCE 2019). The paper proposes a library for implementing convolutional neural networks (CNNs) in order to run on Android devices. The process of running the CNN on the mobile devices is straightforward and does not require an in-between step for model conversion as it uses #Kivy cross-platform library. The CNN layers are implemented in #NumPy. You can find their implementation in my #GitHub project at this link: https://github.com/ahmedfgad/NumPyCNN The library is also open source available here: https://github.com/ahmedfgad/NumPyCNNAndroid There are 2 modes of operation for this work. The first one is training the CNN on the mobile device but it is very time-consuming at least in the current version. The second and preferred way is to train the CNN in a desktop computer and then use it on the mobile device.

1. NumPyCNNAndroid: A Library for Straightforward
Implementation of Convolutional Neural
Networks for Android Devices
Menoufia University
Faculty of Computers and Information
Information Technology Department
By
Ahmed Fawzy Gad
International Conference on Innovative Trends in Computer Engineering (ITCE’2019)

2. Index
2
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

3. Index
3
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

4. Introduction
4
Machine
Learning
Algorithm
Data
Trained
Model
Feature Engineering
Data Scientist Data Analysis Features

5. Introduction
▰ Working with huge amounts of data makes it challenging to
decide what are the suitable features for use.
▰ A dataset such as ImageNet contains millions of images
distributed across thousands of classes.
5
Convolutional Neural Network (CNN)
▰ The problem with the deep learning architectures is being time
consuming and require high processing power.

6. Introduction
▰ There are some already existing libraries for building deep learning
models such as TensorFlow, Keras, Theano, Caffe, and others.
▰ These libraries are normally available for desktop computers due to
the availability of both memory and processing power.
▰ Working with mobile devices, there are 2 important issues.
6
Availability Efficiency
▰ This paper solves the availability issue by proposing a library called
NumPyCNNAndroid (NPCA) for building CNNs for Android devices.

7. Index
7
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

8. Related Work
▰ A network architecture called MobileNets proposes the use of
depthwise separable convolution.
▰ In regular convolution, the 2 steps (filtering and combining) are done
in a single step. These steps are split in the dephwise separable
convolution. The first step is applying a pointwise convolution with
filter size equal to 1x1. Then another pointwise convolution for
combining the results.
8Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications."
arXiv preprint arXiv:1704.04861 (2017).
▰ MobileNets requires the creation of a new network architecture for
every application in order to be supported by the mobile devices.

9. Related Work
▰ A GPU-accelerated
library called
CNNdroid.
9Latifi Oskouei, Seyyed Salar, et al. "CNNdroid: GPU-accelerated execution of trained deep convolutional neural
networks on android." Proceedings of the 2016 ACM Conference on Multimedia. ACM, 2016.

10. Related Work
▰ Some of the exisiting libraries have some releases for supporting the
mobile devices. For example, there is a library called TensorFlow Lite
from TensorFlow. Also there is Caffe mobile from Caffe.
▰ Similar to CNNdroid, there must be an in-between step for converting
the original model into a format supported by the mobile devices.
10

11. Related Work
▰ Here is the summary of the model conversion in TensorFlow.
11
Prepare the TF Model.
Convert the TF Model to TF Lite Model
Create an Android Project
Import the TF Lite Model within the Project
Call the Model within Java Code
▰ In TF Lite, the developer uses multiple tools (Python, TOCO,
Android Studio, and Java)

12. Index
12
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

13. Proposed Work
Advantages
▰ NPCA uses Python for its development which is one of the best tools
in data science.
▰ The same code, unchanged, is executed in all platforms due to the use
of a cross-platform library called Kivy.
▰ Faster deployment for the models of the mobile devices as there is no
in-between step to customize the model.
13

14. Index
14
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

15. Proposed Work
Development Process
▰ The CNN can be trained on the mobile device but it is time-consuming
at least in the current version.
▰ The preferred way is to train the CNN in a desktop computer and then
use it on the mobile device.
15

16. Index
16
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

17. Proposed Work
Supported Layers
▰ Convolution (conv)
▰ Rectified Linear Unit (relu)
▰ Max Pooling (maxpool)
▰ Average Pooling (avgpool)
▰ Fully Connected (fc)
17
GitHub – NumPyCNN
https://github.com/ahmedfgad/NumPyCNN
GitHub – NumPyCNNAndroid
https://github.com/ahmedfgad/NumPyCNNAndroid

18. Index
18
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

19. Proposed Work
Example
19
Read Image - PIL

20. Index
20
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

21. Validation
21

22. Index
22
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

23. Find NumPyCNNAndroid at Google Play
▰ To facilitate testing the library,
a free application named
NumPyCNNAndroid is
available at Google Play for
download.
▰ https://play.google.com/store/
apps/details?id=org.numpycnn.
numpycnn
23

24. Find NumPyCNNAndroid at Google Play
24

25. Index
25
• Introduction
• Related Work
• Proposed Library (NumPyCNNAndroid)
• Advantages
• Development Process
• Supported Layers
• Example
• Validation
• Find NumPyCNNAndroid at Google Play
• References

26. References
▰ Nanni, Loris, Stefano Ghidoni, and Sheryl Brahnam. "Handcrafted vs. non-handcrafted features for computer vision classification." Pattern
Recognition 71 (2017): 158-172.
▰ Russakovsky, Olga, et al. "Imagenet large scale visual recognition challenge." International Journal of Computer Vision 115.3 (2015): 211-252.
▰ Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and
Classification. Springer, 2017.
▰ Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015): 85-117.
▰ Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural
information processing systems. 2012.
▰ Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556
(2014).
▰ Szegedy, Christian, et al. "Going deeper with convolutions." Cvpr, 2015.
▰ Cai, Ermao, et al. "Neuralpower: Predict and deploy energy-efficient convolutional neural networks." arXiv preprint arXiv:1710.05420 (2017).
▰ Kim, Yong-Deok, et al. "Compression of deep convolutional neural networks for fast and low power mobile applications." arXiv preprint
arXiv:1511.06530 (2015).
▰ Latifi Oskouei, Seyyed Salar, et al. "CNNdroid: GPU-accelerated execution of trained deep convolutional neural networks on android." Proceedings of
the 2016 ACM Conference on Multimedia. ACM, 2016. https://github.com/ENCP/CNNdroid
26

27. References
▰ Abadi, Martín, et al. "TensorFlow: A System for Large-Scale Machine Learning." OSDI. Vol. 16. 2016.
▰ Jia, Yangqing, et al. "Caffe: Convolutional architecture for fast feature embedding." Proceedings of the 22nd ACM international conference on
Multimedia. ACM, 2014.
▰ Al-Rfou, Rami, et al. "Theano: A Python framework for fast computation of mathematical expressions." arXiv preprint arXiv:1605.02688 472 (2016):
473.
▰ Nicholson, A. Chris, and Adam Gibson. "Deeplearning4j: Open-source, Distributed Deep Learning for the JVM." https://Deeplearning4j.org (2017).
▰ Chen, Tianqi, et al. "MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems." arXiv preprint arXiv:1512.01274
(2015).
▰ Chollet, François. "Keras: Deep learning library for Theano and TensorFlow." URL: https://keras.io/ (2015): 8.
▰ Torch: http://torch.ch/
▰ Wu, Jiaxiang, et al. "Quantized convolutional neural networks for mobile devices." Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition. 2016.
▰ Han, Song, Huizi Mao, and William J. Dally. "Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman
coding." arXiv preprint arXiv:1510.00149 (2015).
▰ Chen, Wenlin, et al. "Compressing neural networks with the hashing trick." International Conference on Machine Learning. 2015.
▰ Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861
(2017).
▰ Zhang, Xiangyu, et al. "Shufflenet: An extremely efficient convolutional neural network for mobile devices." arXiv preprint arXiv:1707.01083 (2017).
27

28. References
28
▰ TensorFlow Lite: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite.
▰ Caffe Android: https://github.com/sh1r0/caffe-android-lib.
▰ Torch Android: https://github.com/soumith/torch-android.
▰ python-for-android: https://github.com/kivy/python-for-android
▰ Virbel, Mathieu, Thomas E. Hansen, and Oleksandr Lobunets. "Kivy-A Framework for Rapid Creation of Innovative User Interfaces." Mensch &
Computer Workshopband. 2011.
▰ Walt, Stéfan van der, S. Chris Colbert, and Gael Varoquaux. "The NumPy array: a structure for efficient numerical computation." Computing in
Science & Engineering 13.2 (2011): 22-30.
▰ Python Imaging Library (PI) http://www.pythonware.com/products/pil
▰ Buildozer: https://github.com/kivy/buildozer
▰ NumPyCNN (NPC): https://github.com/ahmedfgad/NumPyCNN
▰ NumPyCNNAndroid (NPCA): https://github.com/ahmedfgad/NumPyCNNAndroid
▰ NumPyCNNAndroid (NPCA): https://play.google.com/store/apps/details?id=org.numpycnn.numpycnn

29. 29
THANKS