The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
This Practical covers fundamental CNN development, training and testing. Three exercises of increasing difficulty will be administered, each of them covering a different aspect of CNNs. All the proposed solutions will be implemented in Python, using the PyTorch package. The proposed exercises consist in:
Exercise 1: the simple MNIST dataset will be used to train and test three simple CNNs composed respectively of one, two, and three convolutional layers. Pooling and batch normalisation will be also added to compare the different performances.
Exercise 2: a deep network (e.g., LeNet-5) pretrained on ImageNet will be loaded. Next, the performances on MNIST and CIFAR10 will be evaluated after a fine-tuning stage. Different experiments will be made, considering different conditions, such as fine-tuning all the layers or only the last ones.
Exercise 3: the filters of a learned network will be visualized.
Exercise 4: several datasets (such as CIFAR10 and SVHN) will be tested using other different architectures (such as ResNet and VGG16) and the final performances on the test sets will be evaluated.
Time schedule
Duration (min)
Description
Concepts
Activity
Material
40
Exercise 1: developing, training and testing simple CNNs on a simple dataset
40
Exercise 2: loading a pre-trained model, evaluation after and before fine-tuning on common datasets
20
Exercise 3: visualizing a subset of learned filters
80
Exercise 3: comparing classification performances on different architectures and more complex data
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Acknowledgements
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
