Playing Go with supervised learning in Pytorch

Posted on Sun 25 August 2019 in Machine Learning • Tagged with python, pytorch, cnn, go

Using historic gameplay between strong Go players as training data, a CNN model is built to predict good Go moves on a standard 19x19 Go board.

Go prediction sample

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MNIST pixel attacks with Pytorch

Posted on Sat 01 June 2019 in Machine Learning • Tagged with python, pytorch, cnn, torchvision, mnist, skl

It’s easy to build a CNN that does well on MNIST digit classification. How easy is it to break it, to distort the images and cause the model to misclassify?

MNIST attack accuracy

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Solving CIFAR-10 with Pytorch and SKL

Posted on Tue 14 May 2019 in Machine Learning • Tagged with python, pytorch, cnn, torchvision, cifar, skl

CIFAR-10 is a classic image recognition problem, consisting of 60,000 32x32 pixel RGB images (50,000 for training and 10,000 for testing) in 10 categories: plane, car, bird, cat, deer, dog, frog, horse, ship, truck. Convolutional Neural Networks (CNN) do really well on CIFAR-10, achieving 99%+ accuracy. The Pytorch distribution includes an example CNN for solving CIFAR-10, at 45% accuracy. I will use that and merge it with a Tensorflow example implementation to achieve 75%. We use torchvision to avoid downloading and data wrangling the datasets. Like in the MNIST example, I use Scikit-Learn to calculate goodness metrics and plots.

CIFAR examples

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Solving MNIST with Pytorch and SKL

Posted on Thu 02 May 2019 in Machine Learning • Tagged with python, pytorch, cnn, torchvision, mnist, skl

MNIST is a classic image recognition problem, specifically digit recognition. It contains 70,000 28x28 pixel grayscale images of hand-written, labeled images, 60,000 for training and 10,000 for testing. Convolutional Neural Networks (CNN) do really well on MNIST, achieving 99%+ accuracy. The Pytorch distribution includes a 4-layer CNN for solving MNIST. Here I will unpack and go through this example. We use torchvision to avoid downloading and data wrangling the datasets. Finally, instead of calculating performance metrics of the model by hand, I will extract results in a format so we can use SciKit-Learn's rich library of metrics.

MNIST example digits

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