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Logistic Regression & Transfer Learning

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Presentation on theme: "Logistic Regression & Transfer Learning"— Presentation transcript:

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

12


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