You can edit this page using wiki syntax for markup.

Article contents:== Genann [[toc:]] === Introduction Genann is an chicken egg that provides bindings to the [[https://github.com/codeplea/genann|genann]] ANSI C neural network library. The C library is written only in standard C, the egg is written only in 'standard' Chicken 5 (what comes included with C5, no eggs). === Genann API <procedure>(genann-init inputs hidden-layers hidden-neurons outputs)</procedure> Creates and returns a genann record. Each argument is an integer specifying how many of that the genann should have. {{hidden-neurons}} is the number of hidden neurons ''per hidden layer.'' <procedure>(genann-copy genann)</procedure> Return a new copy of {{genann}}. <procedure>(genann-free! genann)</procedure> Free the memory used by {{genann}}. <procedure>(genann-train genann inputs desired-outputs learning-rate)</procedure> Does a single backpropagation update on {{genann}}, where {{inputs}} and {{desired-outputs}} are f64vectors and {{learning-rate}} is a flonum. <procedure>(genann-run genann inputs)</procedure> Runs the feedforward algorithm to calculate the ann's output from f64vector {{inputs}}. Returns outputs as an f64vector. <procedure>(genann-randomize genann)</procedure> Sets the weights in {{genann}} randomly. This is called by {{genann-init}}. <procedure>(genann-read #!optional port)</procedure> Read a genann record from file port {{port}}. <procedure>(genann-write #!optional port)</procedure> Write a genann to file port {{port}}. === Added procedures <procedure>(genann-init* inputs hidden-layers hidden-neurons outputs)</procedure> <procedure>(make-genann ...)</procedure> Like {{genann-init}}, but sets {{genann-free!}} as a finalizer to the returned genann so it can be properly garbage collected. {{make-genann}} is the same procedure as {{genann-init*}}. <procedure>(genann-copy* genann)</procedure> Like {{genann-copy}}, but sets {{genann-free!}} as a finalizer to the returned genann so it can be properly garbage collected. <procedure>(genann-inputs genann)</procedure> <procedure>(genann-hidden-layers genann)</procedure> <procedure>(genann-hidden-neurons genann)</procedure> <procedure>(genann-outputs genann)</procedure> <procedure>(genann-total-weights genann)</procedure> <procedure>(genann-weights genann)</procedure> Returns number of inputs, hidden layers, hidden neurons per layer, and outputs, respectively, in {{genann}}. These are more or less equivalent to the following in C: <enscript highlight="c"> // genann is created with genann_init genann->inputs genann->hidden_layers genann->hidden genann->outputs genann->total_weights gennan->weight </enscript> <procedure>(genann-weight-set! genann i x)</procedure> <procedure>(genann-weight-ref genann i)</procedure> Set or get the {{i}}th weight of {{genann}}, with the former being a setter for the latter, so you can use {{set!}} with {{genann-weight-ref}}. Useful for training with random search, as in [[https://git.sr.ht/~dieggsy/chicken-genann/tree/master/item/example2.scm|example2.scm]]. === Examples * [[https://git.sr.ht/~dieggsy/chicken-genann/tree/master/item/example1.scm|Training using backpropagation]] * [[https://git.sr.ht/~dieggsy/chicken-genann/tree/master/item/example2.scm|Training using random search]] * [[https://git.sr.ht/~dieggsy/chicken-genann/tree/master/item/example3.scm|Loading a saved ANN]] * [[https://git.sr.ht/~dieggsy/chicken-genann/tree/master/item/example4.scm|Training on the IRIS dataset using backpropagation]] Description of your changes:

I would like to authenticate

Spam control

What do you get when you add 1 to 22?