From 6fd23da97fa9700f59c61a966b4bf7d25fa46b34 Mon Sep 17 00:00:00 2001 From: Paul Oliver Date: Thu, 29 Feb 2024 03:15:03 +0100 Subject: initial commit --- README.md | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 README.md (limited to 'README.md') diff --git a/README.md b/README.md new file mode 100644 index 0000000..05a3b72 --- /dev/null +++ b/README.md @@ -0,0 +1,46 @@ +Neural Network library used for the Guppies project. Parameters that can be set +by it follow: + +## NETWORK STRUCTURE +Defines the main topology of the Neural Network (i.e. the way that Nodes +connect with each other). The four possible choices (in order of +complexity) are: + +### Single MultiLayer Perceptron +A FeedForward network with 1 hidden layer. Information flows +unidirectionally from input nodes to output nodes (and not the +other way around). + +### Dual MultiLayer Perceptron +Same as above, but with two hidden layers instead of one. + +### Simple Recurrent Network (Elman Network) +This type of recurrent network has a set of "context" units that +store the output of the (single) hidden layer and feeds it back +to the input layer on the next time-step, giving it a kind of +short term memory. + +### Fully Recurrent Network +All Nodes in this network are connected to each other. It's the +most complex (and processor intensive) network of the four. + +## NODE STRUCTURE +Defines the structure of each network node. The two choices are: + +### Neuron +Each neuron computes its outputs from a given set of inputs. Output +equals the weighted sum of all inputs. + +### Memory Cell +This kind of node is based in the Long-Short Term Memory recurrent +network model. It contains 4 neurons, 3 of them act as "gates" that +allow it to block input, store it and output it, thus being able to +hold in information or "memories" for a long time span. It's the +most complex (and processor intensive) type of node. + +## NODES PER HIDDEN LAYER +Number of nodes that reside on each hidden layer. SingleMLPs, SimpleRNs and +FullyRNs have one hidden layer. DualMLPs have two (thus, their number of +hidden nodes is "this value" x 2). The more nodes, the more complex the +Neural Networks of the Guppies are (and the more time it'll take to evolve +them). \ No newline at end of file -- cgit v1.2.1