F# for Scientists

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It is a good idea to have that in mind before going through the code. We begin with calculating the error-deltas which describe the contribution of the final error attributed to each of the unactivated outputs as a vector. We do this calculation for the very last layer using the helper function described earlier.

Once we get this value we now need to calculate the error-delta vectors for all the layers. Strictly speaking a layer can be classified as such if something feeds into it. Thus, the inputs are not classified as a layer and it makes no sense to calculate error deltas for them. This can be easily done using a custom tail-recursive function. To perform all these operations on a per-layer basis we use List.

The result of this mapping will produce updated weights in the correct original order which can then be directly returned by the back-propagation function.

Machine Learning with F#, Redux - Mathias Brandewinder

A few things to note here. The x vectors, s vectors and w matrices are all passed in reverse order to the recursive function. We remove the first row in the weight matrix because it corresponds to the bias node and there is no contribution from that node to any of the weights behind it i. Iterating over xAndSLstRev.

F# for Scientists announced by Jon Harrop – Don Syme's WebLog on F# and Related Topics

The layerListUpdater function requires an extra 1. Give yourself a pat on the back for making it so far! You can now use these functions to create any arbitrary network from a simple top-level description such as: initNetwork [1 ; 2 ; 1] [ID TANH]. Sign in. Get started. Building Neural Networks in F — Part 1.

Haaris Mehmood Follow. Setup The main objective here is to demonstrate a working neural network which can be built to be as deep or wide as required. Helper Functions The key idea in functional programming is to break your main function into smaller, simpler tasks which are easier to implement, debug and comprehend. Conclusion Give yourself a pat on the back for making it so far!

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[(F# for Scientists)] [by: Jon Harrop] by Jon Harrop

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