Classification with Delira - A very short introduction¶
Author: Justus Schock
Date: 04.12.2018
This Example shows how to set up a basic classification PyTorch experiment and Visdom Logging Environment.
Let’s first setup the essential hyperparameters. We will use
delira’s Parameters-class for this:
Since we did not specify any metric, only the CrossEntropyLoss will
be calculated for each batch. Since we have a classification task, this
should be sufficient. We will train our network with a batchsize of 64
by using Adam as optimizer of choice.
Logging and Visualization¶
To get a visualization of our results, we should monitor them somehow.
For logging we will use Visdom. To start a visdom server you need to
execute the following command inside an environment which has visdom
installed:
visdom -port=9999
This will start a visdom server on port 9999 of your machine and now we can start to configure our logging environment. To view your results you can open http://localhost:9999 in your browser.
Since a single visdom server can run multiple environments, we need to specify a (unique) name for our environment and need to tell the logger, on which port it can find the visdom server.
Data Preparation¶
Loading¶
Next we will create a small train and validation set (based on
torchvision MNIST):
Augmentation¶
For Data-Augmentation we will apply a few transformations:
With these transformations we can now wrap our datasets into datamanagers:
Training¶
After we have done that, we can finally specify our experiment and run
it. We will therfore use the already implemented
ClassificationNetworkBasePyTorch which is basically a ResNet18:
Congratulations, you have now trained your first Classification Model
using delira, we will now predict a few samples from the testset to
show, that the networks predictions are valid:
See Also¶
For a more detailed explanation have a look at * the introduction tutorial * the 2d segmentation example * the 3d segmentation example * the generative adversarial example