Marton Trencseni - Fri 09 April 2021 • Tagged with python, pytorch, cnn, torchvision, mnist, autoencoder
I measure how the classification accuracy of quantized Autoencoder neural network varies with encoding bits on MNIST digits.
Marton Trencseni - Sun 04 April 2021 • Tagged with python, pytorch, cnn, torchvision, mnist, autoencoder
I investigate how much information an Autoencoder neural network encodes for MNIST digits.
Marton Trencseni - Thu 18 March 2021 • Tagged with pytorch, autoencoder, mnist
I build an Autoencoder network to categorize MNIST digits in Pytorch.
Marton Trencseni - Wed 03 March 2021 • Tagged with python, pytorch, torchvision, mnist, gan
I train a Pytorch Wasserstein MNIST GAN on Google Colab to beautiful MNIST digits.
Marton Trencseni - Tue 02 March 2021 • Tagged with python, pytorch, torchvision, mnist, gan
I train a Pytorch Classic MNIST GAN on Google Colab to generate MNIST digits.
Marton Trencseni - Sat 20 February 2021 • Tagged with python, pytorch, gan, mnist, google-colab
I explore MNIST digits generated by a Generative Adversarial Network trained on Google Colab using Pytorch Lightning.
Marton Trencseni - Sat 01 June 2019 • 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?
Marton Trencseni - Thu 02 May 2019 • 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.
