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Applying Transfer Learning in TensorFlow

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Scott Thompson

Slides from the TensorFlow meetup at eBay NYC 06/07/2016 based on my blog https://medium.com/@st553/using-transfer-learning-to-classify-images-with-tensorflow-b0f3142b9366

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Applying Transfer Learning in TensorFlow Scott Thompson Software Engineer @KontorHQ
Lessons learned building a visual search and classifier for Gilt
A guide for Software Engineers
Classification Men - Short Sleeve Sport Shirts Men - Long Sleeve Sport Shirts Men - Activewear - Activewear Tops Men - Polos
Visual Search
How to distill the ‘essence’ of an image?
Men - Short Sleeve Sport Shirts Men - Long Sleeve Sport Shirts
Image Features Source: http://opencv.org Edge Detection Color Histogram Pixel Segmentation
How to train? Google Inception-v3 Pre-trained on ImageNet http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz Convolutional Neural Nets Source: https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html
1,000,000 images, 1,000 categories Business office Internet site President Image Source: http://image-net.org/explore
Why use a pre-trained model? It’s faster (it’s pre-trained) It’s cheaper (no need for GPU farm) It generalizes (avoid overfitting) Image Source: http://image-net.org/explore
Transfer Learning
Source: https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html ‘Essence’Edge Detector Shape Detector Structure of the Network
t-SNE plot - CIFAR-10 pool_3 features t-SNE Code: https://lvdmaaten.github.io/tsne/
Output Layer Input Layer Source: https://github.com/sthomp/tensorflow_transfer_cifar10
Performance About 1 second per image Gilt has ~40,000 images ~12 hours Embarrassingly Parallel -> Partition across threads / machines Cache pool_3 features (numpy array)
Applying pool_3 features Nearest Neighbor Search Brute Force Cosine Distance Checkout https://github.com/spotify/annoy Train a classifier Nearest Neighbor Classifier
You don’t need to use TensorFlow for everything Reading files / Work Queues Image resizing Serializing weights

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