planning

snn.c

@@ -0,0 +1,28 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <assert.h>
+
+/*
+neural network
+input layer -> hidden layer -> output layer
+each neuron has weights, numweights = numneurons
+neuron value * weight goes to another neuron, and that sum goes through activation fxn to determine that other neuron"'s output
+
+5 input layers 3 hidden layers
+each connection from a neuron carries a weight, 3 connections from a single neuron. new formula, num connections is equal to hidden num neurons * input neurons
+
+each neuron only gets the weighted sum from that number. ie the first hidden neuron only gets the weighted sum from the input values * their respective first weights.
+
+represent that as a matrix with # cols representing hidden neuron amount and # rows representing input neurons
+
+[.1 .2 .3 .4 .5 w1s of each input neuron
+ .3 .5 .7 .9 1.1 w2s
+ .2 .4 .5 .8 1.2 ] w3s
+
+ just multiply input values by w1 row to get hidden neuron 1's value
+ repeat for hn2 and hn3
+
+this works.
+*/
+