Tutorial (II) : Image Recognition Using TensorFlow

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• Course: Digital Image Processing (ELEC4245)• Tutor: Dong Jinping (CB206, jpdong@eee.hku.hk)• Venue: CB103• Date: 18 Apr, 2017

Outline

• Introduction of TensorFlow

• Image Recognition• Example on MNIST dataset

• Practice on CIFAR-10 dataset

• Summary

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Introduction of TensorFlow

• TensorFlow1

• Open-source software for Machine Intelligence

• AlphaGo

• Installation on Windows2

• Installing with Anaconda (Python 3.5.x)

• Validate installation• valdt_instal.py

• Spyder• Scientific Python Development EnviRonment

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1. https://www.tensorflow.org/2. https://www.tensorflow.org/install/install_windows3. Image from: http://www.cw.com.tw/article/article.action?id=5075965

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Introduction of TensorFlow• Central unit of data: tensor

• A tensor's rank is its number of dimensions.

• Computational Graph (CG)• Build the CG• Run the CG

• cg_1.py• cg_2.py

• TensorBoard1

• Easier to understand, debug, optimize• Event file → Visualization

• tensorboard --logdir=logs• http://localhost:6006• tensorboard_example.py

41. https://www.tensorflow.org/get_started/graph_viz

3 ∗ 𝑎 + 𝑏 CG

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Image Recognition-Example• MNIST dataset of handwritten digits1

• 55,000 data points of training data (mnist.train)

• 10,000 points of test data (mnist.test)

• 5,000 points of validation data (mnist.validation)

• Every MNIST data point• An image of a handwritten digit, its corresponding label

51. http://yann.lecun.com/exdb/mnist/

5 0 4 1

Images

Labels

Image Recognition-Example

• MNIST dataset of handwritten digits1

• Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers

• mnist.train.images is a tensor with a shape of [55000, 784]

• mnist.train.labels [55000, 10]

• mnist_example.py

61. http://yann.lecun.com/exdb/mnist/

Image Recognition-Example

• Softmax Regression1

• Add up the evidence of the input being in certain classes

• Convert that evidence into probabilities

71. https://www.tensorflow.org/get_started/mnist/beginners

Image Recognition-Example

• Image Recognition on MNIST1

• Softmax, Cross-entropy, Gradient descent (for more2),

• Build, Train and Evaluate the model

• Img_rcg_example.py

81. http://yann.lecun.com/exdb/mnist/2. https://www.tensorflow.org/api_guides/python/train#optimizers

Image Recognition-Example

• Results1

• Accuracy varies each time you run the code• ~92%

• Good or not?• Random guess: 10% ☺

• Current best model: 99.7%

• What if more training steps?• 1000 → 2000 → 3000

91. https://www.tensorflow.org/get_started/mnist/beginners

CG of this model

Image Recognition-Practice• CIFAR-10 dataset1

• Classify RGB 32x32 pixel images across 10 categories

• You need to do • Learn the functions used in Img_rcg_example.py

• ? / help() / https://www.tensorflow.org/api_docs/python/• Write Img_rcg_mine.py

• Similar procedure(build, train and evaluate the model)• Load CIFAR-10 using data_helpers.py2

• import data_helpers• data_sets = data_helpers.load_data()

• Use ‘loss function’• Satisfied accuracy? Why?• How to do better? One layer → Two layer?• Other optimizer?

• Answer code• Img_rcg_practice.py2

101. http://www.cs.toronto.edu/~kriz/cifar.html2. https://github.com/wolfib/image-classification-CIFAR10-tf

CIFAR-10

Summary

• Simple examples about Image Recognition using Machine Learning• MNIST

• CIFAR-10

• How TensorFlow works

• Core concepts about Machine Learning

• Need more efforts to learn this topic deeply

• To learn more• https://www.tensorflow.org/tutorials/mandelbrot

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Thank you!

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