Screen 1 of 3 - Read before you experiment
A CNN transforms spatial evidence one operation at a time
Kernel size defines the local receptive field, while stride and padding determine the output geometry. Convolution creates feature maps, activation keeps useful nonlinear responses, and pooling compresses spatial evidence.
This laboratory connects those geometric rules to real held-out samples and trained CNN tensors. Follow one sample through the pipeline before comparing channels or datasets.
Evidence levelReal trained CNNs + held-out MNIST, Fashion-MNIST, and CIFAR-10 samples
Screen 2 of 3 - Experiment
Run, inspect, and compare
Follow the three guided moves above. Change one variable at a time so every visual change has a clear cause.
Screen 3 of 3 - Consolidate
By the end of this lesson, you will be able to:
- Trace an image through convolution, activation, pooling, and classification layers.
- Predict output dimensions from kernel size, stride, and padding settings.
- Connect feature-map activations and receptive fields to the final class prediction.
Describes convolution layers, stride, padding, and pooling. Shows how CNNs form feature maps and receptive fields. Explains hierarchical representation of features.