1. Logistic Regression & Transfer Learning
Mohammad Masum
Ph.D. Student
Institute of Analytics and Data Science
Kennesaw State University

2. Logistic Regression Mnist Digit Data
Binary classification; Label  7 & 8
Shape  8,000 x 784

3. Logistic Regression Mnist Digit Data Binary classification
Shape  8,000 x 784

4. Transfer Learning
Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task
Three Possible Benefits:
Higher start
Higher slope
Higher asymptote
The benefits of using transfer learning is not obvious

5. Transfer Learning: Pre-trained Model
Available Models in Keras Framework
Models for image classification with weights trained on ImageNet
Xception
VGG16
VGG19
ResNet50
InceptionV3
InceptionResnetV2
MobileNet
DenceNet
NasNet
MobileNetV2
Built by: Oxford Visual Geometry Group
Total 16 layers
Given image  find object name in the image
It can detect any one of 1000 images
It take input image size 224 x 224 x 3 (RGB image)

6. Pre-Trained Model
Number of Features
Feature Extraction

7. VGG16 Architecture

8. VGG16 Architecture

9. Data Representation Mnist Image Data Feature Extraction
Last Conv Layer Flatten Data

10. Logistic regression with Extracted Features Data

11. Logistic regression with Extracted Features Data
Possible reasons that VGG16 does not perform well:
VGG16 is trained for 3-channel RGB images while mnist digit data is 1-channel gray scale
Background of images
VGG16 trained for 1,000 objects label