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const math = require('mathjs');
const activationFunctionList = require('./activationFunctions');
class Layer {
constructor(inputCount, nodeCount, activationFunctionName) {
this.gx = activationFunctionList[activationFunctionName].gx;
this.dg_dx = activationFunctionList[activationFunctionName].dg_dx;
this.weights = math.matrix(math.random([inputCount, nodeCount]));
this.weights = math.map(this.weights, (element) => (element - 0.5) * 2);
this.biases = math.matrix(math.zeros([nodeCount]));
this.z = math.matrix(math.zeros([nodeCount]));
this.dc_dw_vectors = math.matrix(math.zeros(this.weights._size));
this.dc_db_vectors = math.matrix(math.zeros(this.biases._size));
//console.log('dc_db_vectors', this.dc_db_vectors);
}
forwardPropogation(activationInput) {
this.ai = activationInput;
let z = math.multiply(activationInput, this.weights);
z = math.add(z, this.biases);
this.z = z;
let ao = this.gx(z);
return ao;
}
backPropogation(dc_dao) {
let dao_dz = this.dg_dx(this.z);
let dc_db = math.dotMultiply(dao_dz, dc_dao);
let dz_dw = math.ones(this.weights._size);
dz_dw = math.multiply(this.ai, dz_dw);
let dc_dw = math.dotMultiply(dc_db, dz_dw);
let dz_dai = this.weights;
let dc_dai = math.multiply(dz_dai, dc_db);
//store cost vectors for gradient descent
//console.log('dc_db_vectors', this.dc_db_vectors);
//console.log('dc_db', dc_db);
this.dc_db_vectors = math.concat(this.dc_db_vectors, dc_db, 0);
this.dc_dw_vectors = math.concat(this.dc_dw_vectors, dc_dw, 0);
//console.log('dc_db_vectors', this.dc_db_vectors);
return dc_dai;
}
gradientDescent(learningRate) {
//console.log(this.dc_dw_vectors);
//console.log(this.dc_db_vectors);
let dc_dw_avg = math.mean(this.dc_dw_vectors, 0)
let dc_db_avg = math.mean(this.dc_db_vectors, 0)
this.weights = this.weights - (learningRate * dc_dw_avg);
this.biases = this.biases - (learningRate * dc_dw_avg);
this.dc_dw_vectors = math.matrix([]);
this.dc_db_vectors = math.matrix([]);
}
};
module.exports = Layer;
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