General question on UNet filters. Am I correct that the individual numbers & arrangements of each element within each chosen UNet Filter are parameters to be learnt OR are these numbers & arrangements user defined depending on what features the user wants to pick up?

If I am understanding your question correctly, the answer is the same as it always is for neural networks: the architecture of the network (how many layers, how many units in each layer, all the convolution values like filter size, padding and stride, how many pooling layers â€¦) is all chosen (hyperparameters). But the actual *values* at each position in the various filters specified by the architecture (the â€śparametersâ€ť) are learned through back propagation.

Thanks very much Paul. So for the case of U-Net filters on transpose convolution. This is also learnt via back propagation! Separately, it seems itâ€™s the skip connections that enrich back the spatial features previously lost when downsampling.

Yes, any parameters (coefficients) for the transpose convolutions are also learned by back propagation.

That is a good description of the purpose of the skip connections. Note that the architecture of the skip connections is also a choice made by the system designer. The skip connections themselves do not have any parameters, but they are part of the back propagation process: gradients are propagated across all connections in the model.

Tks Paul for the clarification! I will play around with the optional parts of the assignments as the backprops are not a prerequisite for most assignments. Hence, why I might have missed the reinforcement of key concepts.

Well since we have now switched to using TF and Keras, we no longer have to worry about back propagation: the packages just handle all the gradient calculations and the application of the gradients and convergence and all that invisibly for you. You donâ€™t even have to specify a â€ślearning rateâ€ť or worry about any of that.