Skip to content

ResNet

Short ResNet training on CIFAR10 over 21 epochs

AdamW for a ResNet56v2 – V – weight decay and cosine shaped schedule of the learning rate

In this post series we try to find methods to reduce the number of epochs for the training of ResNets on image datasets. Our test case is CIFAR10. In this post we will test a modified cosine shaped schedule for a systematic and fast reduction of the learning rate LR. This supplements the approaches described in previous posts of this… Read More »AdamW for a ResNet56v2 – V – weight decay and cosine shaped schedule of the learning rate

AdamW for a ResNet56v2 – III – excursion: weight decay vs. L2 regularization in Adam and AdamW

A major topic of this post series is the investigation of methods to reduce the number of required training epochs for ResNets. In particular with respect to image analysis. Our test case is defined by a ResNet56v2 neural network trained on the CIFAR10 dataset. For intermediate results of numerical experiments see the first two posts During the last week I… Read More »AdamW for a ResNet56v2 – III – excursion: weight decay vs. L2 regularization in Adam and AdamW

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… Read More »AdamW for a ResNet56v2 – I – a detailed look at results based on the Adam optimizer

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… Read More »Installation of CUDA 12.3 and CuDNN 8.9 on Opensuse Leap 15.5 for Machine Learning