This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.

1. February 23, 2017
rajiv shah
RajivShah.com
rshah@pobox.com
github.com/rajshah4/image_keras
rajcs4
Image Classification using
TensorFlow/Keras

2. Build a simple convolutional neural
network
Augment data
Use a pretrained convolutional neural
network
Use transfer learning (fine tuning a
pretrained network)
goals

3. what is the
big deal?

4. telling cats & dogs
apart
In 2013 - 82.7%
CNN - 99%

5. Theano
Caffe
Torch
Tensorflow
MXNet
CNTK
methodology

6. build a simple
convolutional
neural network

8. augmenting
data

10. overfitting

11. using a pre-
trained
network

12. 138 GB, 14 million
images

13. pre-trained
networks

14. fine tuning a
pre-trained
network

15. Build a simple convolutional neural
network
Augment data
Use a pretrained convolutional neural
network
Use transfer learning (fine tuning a
pretrained network)
goals

16. February 23, 2017
rajiv shah
RajivShah.com
rshah@pobox.com
github.com/rajshah4/image_keras
rajcs4
Image Classification using
TensorFlow/Keras