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Machine Learning

AdamW for a ResNet56v2 – I – a detailed look at results based on the Adam optimizer

This post requires Javascript to display formulas! The last days I started to work on ResNets again. The first thing I did was to use a ResNet code which Rowel Atienza has published in his very instructive book “Advanced Deep Learning with Tensorflow2 and Keras” [1]. I used the code on the CIFAR10 dataset. Atienza’s approach for this test example is to use image augmentation in addition to L2-regularization with the good old Adam optimizer and a piecewise constant Learning Rate schedule. For a ResNet56v2… Read More »AdamW for a ResNet56v2 – I – a detailed look at results based on the Adam optimizer

Prompt based image generation with Stable Diffusion on a TI 4060 ?!

Midjourney and OpenAI give you access to image generators which create images based on key words in a text prompt. To access Dall-E2 you need to pay money. The tools of Midjourney are no longer free because they have been misused. OpenArt offers you a free service – but the images are public domain. So, some days ago I asked myself whether one can perform prompt based image creation on a Linux PC with a consumer board and a low price graphics card. I should… Read More »Prompt based image generation with Stable Diffusion on a TI 4060 ?!

Installation of CUDA 12.3 and CuDNN 8.9 on Opensuse Leap 15.5 for Machine Learning

Machine Learning on a Linux system is no fun without a GPU and its parallel processing capabilities. On a system with a Nvidia card you need basic Nvidia drivers and additional libraries for optimal support of Deep Neural Networks and Linear Algebra operations on the GPU sub-processors. E.g., Keras and Tensorflow 2 [TF2] use CUDA and cuDNN-libraries on your Nvidia GPU. Basic information can be found here: This means that you must not only perform an installation of (proprietary) Nvidia drivers, but also of CUDA… Read More »Installation of CUDA 12.3 and CuDNN 8.9 on Opensuse Leap 15.5 for Machine Learning

Using PyQt with QtAgg in Jupyterlab – IV – simple PyQt and MPL application with background worker and receiver threads

As you read this post you are probably interested in Machine Learning [ML] and hopefully in Linux systems as a ML-platform as well. This post series wants to guide you over a bridge between the standard tool-set of Python3 notebooks in Jupyterlab for the control of ML-algorithms and graphical Qt-applications on your Linux desktop. The objective is to become more independent of some limitations of the browser based Jupyterlab notebooks. One aspect is the use of graphical Qt-based control elements (as e.g. buttons, etc.) in… Read More »Using PyQt with QtAgg in Jupyterlab – IV – simple PyQt and MPL application with background worker and receiver threads