The document discusses transfer learning, particularly using pretrained models like ResNet50 and VGG16 for image classification tasks. It outlines the benefits of transfer learning, including faster model training and effective feature extraction from existing neural networks. It also provides practical resources, exercises, and code links to help practitioners implement transfer learning using Keras.

1. 國立臺北護理健康大學 NTUNHS
Transfer Learning
Orozco Hsu
2023-05-16
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2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Work Experience
• Telecom big data Innovation
• AI projects
• Retail marketing technology
• User Group
• TW Spark User Group
• TW Hadoop User Group
• Taiwan Data Engineer Association Director
• Research
• Big Data/ ML/ AIOT/ AI Columnist
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3. Tutorial
Content
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Transfer Learning introduction
Homework
Keras pre-trained model
Transfer Learning with the ResNet50 Model

4. Code
• Download code
• https://github.com/orozcohsu/ntunhs_2023_01
• Folder/file
• 20230516_01/run.ipynb
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5. Code
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Click button
Open it with Colab
Copy it to your
google drive
Check your google
drive

6. Pretrained Deep Neural Networks
• You can take a pretrained image classification neural network that has
already learned to extract powerful and informative features from
natural images.
• The majority of the pretrained neural networks are trained on a
subset of the ImageNet database, which is used in the ImageNet
Large-Scale Visual Recognition Challenge (ILSVRC).
• Using a pretrained neural network with transfer learning is typically
much faster and easier than training a neural network from scratch.
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7. Pretrained Deep Neural Networks following
tasks
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Purpose Description
Classification
Apply pretrained neural networks directly to classification
problems.
Transfer Learning
Take layers from a neural network trained on a large data set and
fine-tune on a NEW data set.
Feature
Extraction
Use a pretrained neural network as a feature extractor by using
the layer activations as features. You can use these activations as
features to train another machine learning model, such as a
support vector machine (SVM) or other classifiers.

8. Keras Application Pretrained model
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Ref: https://keras.io/api/applications/

9. Keras Application Pretrained model
• Keras’s Applications are deep learning models that are made available
alongside pre-trained weights.
• These models can be used for prediction, feature extraction, and fine-
tuning.
• Weights are downloaded automatically when instantiating a model.
They are stored at ~/.keras/models/
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10. Exercise
• Try it up!
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pre-trained

11. Transfer Learning introduction
• Transfer Learning is a process where a model built for a problem is
again reused for another problem based on some factors
• Using Transfer Learning you can achieve good results if the data is
kind of SIMILAR to the data on which model is already trained
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12. Transfer Learning introduction
• A CNN can be divided into two main parts
• Feature learning and Classifier
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Ref: https://towardsdatascience.com/transfer-learning-with-vgg16-and-keras-50ea161580b4

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Transfer Learning introduction
The VGG16 Model has 16 Convolutional and Max Pooling layers, 3 Dense layers
for the Fully-Connected layer, and an output layer of 1,000 classes
Ref: https://www.learndatasci.com/tutorials/hands-on-transfer-learning-keras/

14. Transfer Learning introduction
• Feature Extraction Approach (excluding top)
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We will bootstrap a Fully-Connected layer to
generate predictions
top

15. Exercise
• Try it up!
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transfer_learning_mnist
training data
training data
Model Output
Output
fit
fit

16. Transfer Learning introduction
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• Fine-Tuning Approach
the final convolutional and pooling layers are
unfrozen to allow training. A Fully-Connected
layer is defined for training and prediction.

17. Exercise
• Try it up!
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fine_tuning
Layer1
Layer2
Layer3
…
Layer148
Layer149
Layer150
Layer151
Layer152
Layer153
Layer154
Set output as an input to the New layer
New layer

18. Transfer Learning with the ResNet50 Model
• It consists of 60,000 (size: 32x32) RGB color images in 10 classes
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Ref: https://www.cs.toronto.edu/~kriz/cifar.html

19. Transfer Learning with the ResNet50 Model
• ResNet model of Computer Vision that is trained on the ImageNet
dataset having 14 million images that can be used for Image
classification tasks
• imagenet 1,000 class
• https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
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20. STAGE 1
STAGE 2
STAGE 3
STAGE 4
STAGE 0
INPUT
OUTPUT
ResNet50
CONV: 7× 7, 64, /2
BN, RELU
MAXPOOL: 3× 3, /2
STAGE 0
(3, 224, 224)
(64, 56, 56)
STAGE 1
BTNK1: 64, 56, 64, 1
(256, 56, 56)
BTNK2: 256, 56
(256, 56, 56)
BTNK2: 256, 56
(256, 56, 56)
STAGE 2
BTNK1: 256, 56, 128, 2
(512, 28, 28)
BTNK2: 512, 28
(512, 28, 28)
BTNK2: 512, 28
(512, 28, 28)
BTNK2: 512, 28
(512, 28, 28)
STAGE 3
BTNK1: 512, 28, 256, 2
(1024, 14, 14)
BTNK2: 1024, 14
(1024, 14, 14)
BTNK2: 1024, 14
(1024, 14, 14)
BTNK2: 1024, 14
(1024, 14, 14)
BTNK2: 1024, 14
(1024, 14, 14)
BTNK2: 1024, 14
(1024, 14, 14)
STAGE 4
BTNK1: 1024, 14, 512, 2
(2048, 7, 7)
BTNK2: 2048, 7
(2048, 7, 7)
BTNK2: 2048, 7
(2048, 7, 7)
BTNK1: C, W, C1, S
CONV: 1× 1, C1, /S
BN, RELU
CONV: 3× 3, C1, /1
BN, RELU
CONV: 1× 1, C1*4, /1
BN
CONV: 1× 1, C1*4, /S
BN
+, RELU
(C, W, W)
(C1*4, W/S, W/S)
BTNK2: C, W
CONV: 1× 1, C/4, /1
BN, RELU
CONV: 3× 3, C/4, /1
BN, RELU
CONV: 1× 1, C, /1
BN
+, RELU
(C, W, W)
(C, W, W)

21. Exercise
• Try it up!
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transfer_learning_resnet50

22. Homework
• Try to use some images to make a prediction with Top5 in pre-
trained.ipynb.
• Print the Top-5 prediction results
• Try to inference some images to make a prediction for BOTH
fine_tuning.ipynb and transfer_learning_resnet50.ipynb.
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