Add some neuralnetwork optimizer

js/neuralnetwork_builder.js

@@ -1,3 +1,5 @@
+import * as opt from '../../lib/model/nns/optimizer.js'
+
 const layerTypes = {
 	abs: {},
 	clip: { min: 0, max: 1 },
@@ -192,22 +194,12 @@ export default class NeuralNetworkBuilder {
 			this._opt = r.append('select').attr('name', 'optimizer')
 			this._opt
 				.selectAll('option')
-				.data([
-					'sgd',
-					'adam',
-					'momentum',
-					'adagrad',
-					'rmsprop',
-					'adadelta',
-					'rmspropgraves',
-					'smorms3',
-					'adamax',
-					'nadam',
-				])
+				.data(Object.keys(opt))
 				.enter()
 				.append('option')
 				.property('value', d => d)
 				.text(d => d)
+			this._opt.property('value', 'adam')
 		}
 	}
 }

js/view/ladder_network.js

@@ -1,5 +1,6 @@
 import Controller from '../controller.js'
 import { BaseWorker } from '../utils.js'
+import * as opt from '../../lib/model/nns/optimizer.js'
 
 class LadderNetworkWorker extends BaseWorker {
 	constructor() {
@@ -85,18 +86,8 @@ export default function (platform) {
 
 	const optimizer = controller.select({
 		label: ' Optimizer ',
-		values: [
-			'sgd',
-			'adam',
-			'momentum',
-			'adagrad',
-			'rmsprop',
-			'adadelta',
-			'rmspropgraves',
-			'smorms3',
-			'adamax',
-			'nadam',
-		],
+		values: Object.keys(opt),
+		value: 'adam',
 	})
 	const slbConf = controller.stepLoopButtons().init(done => {
 		if (platform.datas.length === 0) {

lib/model/mlp.js

@@ -1,5 +1,5 @@
 import Matrix from '../util/matrix.js'
-import { AdamOptimizer } from './nns/optimizer.js'
+import { adam } from './nns/optimizer.js'
 
 /**
  * @ignore
@@ -31,7 +31,7 @@ class MLP {
 			this._w[i] = Matrix.randn(layer_sizes[i], layer_sizes[i + 1], 0, 0.1)
 			this._b[i] = Matrix.zeros(1, layer_sizes[i + 1])
 		}
-		this._optimizer = new AdamOptimizer()
+		this._optimizer = new adam()
 		this._optimizer_mng = this._optimizer.manager()
 	}
 

lib/model/neuralnetwork.js

@@ -84,31 +84,15 @@ export default class NeuralNetwork {
 
 	/**
 	 * @param {ComputationalGraph} graph Graph of a network
-	 * @param {'sgd' | 'adam' | 'momentum' | 'adagrad' | 'rmsprop' | 'adadelta' | 'rmspropgraves' | 'smorms3' | 'adamax' | 'nadam'} [optimizer] Optimizer of the network
+	 * @param {'sgd' | 'adam' | 'momentum' | 'adagrad' | 'rmsprop' | 'adadelta' | 'rmspropgraves' | 'smorms3' | 'adamax' | 'nadam' | 'santae' | 'santasss' | 'amsgrad' | 'adabound' | 'amsbound' | 'adabelief'} [optimizer] Optimizer of the network
 	 */
 	constructor(graph, optimizer = 'sgd') {
 		this._graph = graph
 		this._optimizer = optimizer
-		if (optimizer === 'adam') {
-			this._opt = new opt.AdamOptimizer()
-		} else if (optimizer === 'momentum') {
-			this._opt = new opt.MomentumOptimizer()
-		} else if (optimizer === 'adagrad') {
-			this._opt = new opt.AdaGradOptimizer()
-		} else if (optimizer === 'rmsprop') {
-			this._opt = new opt.RMSPropOptimizer()
-		} else if (optimizer === 'adadelta') {
-			this._opt = new opt.AdaDeltaOptimizer()
-		} else if (optimizer === 'rmspropgraves') {
-			this._opt = new opt.RMSPropGravesOptimizer()
-		} else if (optimizer === 'smorms3') {
-			this._opt = new opt.SMORMS3Optimizer()
-		} else if (optimizer === 'adamax') {
-			this._opt = new opt.AdaMaxOptimizer()
-		} else if (optimizer === 'nadam') {
-			this._opt = new opt.NadamOptimizer()
+		if (Object.keys(opt).includes(optimizer)) {
+			this._opt = new opt[optimizer]()
 		} else {
-			this._opt = new opt.SGDOptimizer()
+			this._opt = new opt.sgd()
 		}
 		this._opt_managers = []
 		for (let i = 0; i < this._graph.size; i++) {

lib/model/nns/optimizer.js

@@ -1,10 +1,16 @@
-export { SGDOptimizer } from './optimizer/sgd.js'
-export { MomentumOptimizer } from './optimizer/momentum.js'
-export { AdaGradOptimizer } from './optimizer/adagrad.js'
-export { RMSPropOptimizer } from './optimizer/rmsprop.js'
-export { AdaDeltaOptimizer } from './optimizer/adadelta.js'
-export { AdamOptimizer } from './optimizer/adam.js'
-export { RMSPropGravesOptimizer } from './optimizer/rmspropgraves.js'
-export { SMORMS3Optimizer } from './optimizer/smorms3.js'
-export { AdaMaxOptimizer } from './optimizer/adamax.js'
-export { NadamOptimizer } from './optimizer/nadam.js'
+export { SGDOptimizer as sgd } from './optimizer/sgd.js'
+export { MomentumOptimizer as momentum } from './optimizer/momentum.js'
+export { AdaGradOptimizer as adagrad } from './optimizer/adagrad.js'
+export { RMSPropOptimizer as rmsprop } from './optimizer/rmsprop.js'
+export { AdaDeltaOptimizer as adadelta } from './optimizer/adadelta.js'
+export { AdamOptimizer as adam } from './optimizer/adam.js'
+export { RMSPropGravesOptimizer as rmspropgraves } from './optimizer/rmspropgraves.js'
+export { SMORMS3Optimizer as smorms3 } from './optimizer/smorms3.js'
+export { AdaMaxOptimizer as adamax } from './optimizer/adamax.js'
+export { NadamOptimizer as nadam } from './optimizer/nadam.js'
+export { SantaEOptimizer as santae } from './optimizer/santae.js'
+export { SantaSSSOptimizer as santasss } from './optimizer/santasss.js'
+export { AMSGradOptimizer as amsgrad } from './optimizer/amsgrad.js'
+export { AdaBoundOptimizer as adabound } from './optimizer/adabound.js'
+export { AMSBoundOptimizer as amsbound } from './optimizer/amsbound.js'
+export { AdaBeliefOptimizer as adabelief } from './optimizer/adabelief.js'

lib/model/nns/optimizer/adabelief.js

@@ -0,0 +1,44 @@
+import Matrix from '../../../util/matrix.js'
+
+export class AdaBeliefOptimizer {
+	constructor(lr = 0.001, beta1 = 0.9, beta2 = 0.999) {
+		this._learningrate = lr
+		this._beta1 = beta1
+		this._beta2 = beta2
+	}
+
+	set learningRate(value) {
+		this._learningrate = value
+	}
+
+	manager() {
+		const this_ = this
+		return {
+			get lr() {
+				return this_._learningrate
+			},
+			params: {},
+			delta(key, value) {
+				const valueIsNumber = typeof value === 'number'
+				if (valueIsNumber) {
+					value = new Matrix(1, 1, value)
+				}
+				if (!this.params[key]) {
+					const z = value.copy()
+					z.fill(0)
+					this.params[key] = { m: z.copy(), v: z, t: 1 }
+				}
+				this.params[key].m.broadcastOperate(value, (a, b) => a * this_._beta1 + b * (1 - this_._beta1))
+				const mo = this.params[key].m.copy()
+				mo.broadcastOperate(value, (a, b) => b - a)
+				this.params[key].v.broadcastOperate(mo, (a, b) => a * this_._beta2 + (1 - this_._beta2) * b * b)
+				const nv = 1 - this_._beta1 ** this.params[key].t
+				const ns = 1 - this_._beta2 ** this.params[key].t
+				const ret = this.params[key].m.copy()
+				ret.broadcastOperate(this.params[key].v, (a, b) => (a / nv) * (this.lr / Math.sqrt(b / ns + 1.0e-12)))
+				this.params[key].t++
+				return valueIsNumber ? ret.toScaler() : ret
+			},
+		}
+	}
+}

lib/model/nns/optimizer/adabound.js

@@ -0,0 +1,48 @@
+import Matrix from '../../../util/matrix.js'
+
+export class AdaBoundOptimizer {
+	constructor(lr = 0.001, alpha = 0.003, beta1 = 0.9, beta2 = 0.999) {
+		this._learningrate = lr
+		this._alpha = alpha
+		this._beta1 = beta1
+		this._beta2 = beta2
+
+		this._eta_lbound = t => this._learningrate * (1 - 1 / ((1 - beta2) * t + 1))
+		this._eta_ubound = t => this._learningrate * (1 + 1 / ((1 - beta2) * t + 1))
+	}
+
+	set learningRate(value) {
+		this._learningrate = value
+	}
+
+	manager() {
+		const this_ = this
+		return {
+			get lr() {
+				return this_._learningrate
+			},
+			params: {},
+			delta(key, value) {
+				const valueIsNumber = typeof value === 'number'
+				if (valueIsNumber) {
+					value = new Matrix(1, 1, value)
+				}
+				if (!this.params[key]) {
+					const z = value.copy()
+					z.fill(0)
+					this.params[key] = { m: z.copy(), v: z, t: 1 }
+				}
+				this.params[key].m.broadcastOperate(value, (a, b) => a * this_._beta1 + b * (1 - this_._beta1))
+				this.params[key].v.broadcastOperate(value, (a, b) => a * this_._beta2 + (1 - this_._beta2) * b * b)
+				const eta_lb = this_._eta_lbound(this.params[key].t)
+				const eta_ub = this_._eta_ubound(this.params[key].t)
+				const eta = this.params[key].v.copy()
+				eta.map(v => Math.min(eta_ub, Math.max(eta_lb, this_._alpha / Math.sqrt(v))))
+				const ret = this.params[key].m.copy()
+				ret.broadcastOperate(eta, (a, b) => (a * b) / Math.sqrt(this.params[key].t))
+				this.params[key].t++
+				return valueIsNumber ? ret.toScaler() : ret
+			},
+		}
+	}
+}

lib/model/nns/optimizer/amsbound.js

@@ -0,0 +1,49 @@
+import Matrix from '../../../util/matrix.js'
+
+export class AMSBoundOptimizer {
+	constructor(lr = 0.001, alpha = 0.003, beta1 = 0.9, beta2 = 0.999) {
+		this._learningrate = lr
+		this._alpha = alpha
+		this._beta1 = beta1
+		this._beta2 = beta2
+
+		this._eta_lbound = t => this._learningrate * (1 - 1 / ((1 - beta2) * t + 1))
+		this._eta_ubound = t => this._learningrate * (1 + 1 / ((1 - beta2) * t + 1))
+	}
+
+	set learningRate(value) {
+		this._learningrate = value
+	}
+
+	manager() {
+		const this_ = this
+		return {
+			get lr() {
+				return this_._learningrate
+			},
+			params: {},
+			delta(key, value) {
+				const valueIsNumber = typeof value === 'number'
+				if (valueIsNumber) {
+					value = new Matrix(1, 1, value)
+				}
+				if (!this.params[key]) {
+					const z = value.copy()
+					z.fill(0)
+					this.params[key] = { m: z.copy(), v: z.copy(), vh: z, t: 1 }
+				}
+				this.params[key].m.broadcastOperate(value, (a, b) => a * this_._beta1 + b * (1 - this_._beta1))
+				this.params[key].v.broadcastOperate(value, (a, b) => a * this_._beta2 + (1 - this_._beta2) * b * b)
+				this.params[key].vh.broadcastOperate(this.params[key].v, (a, b) => Math.max(a, b))
+				const eta_lb = this_._eta_lbound(this.params[key].t)
+				const eta_ub = this_._eta_ubound(this.params[key].t)
+				const eta = this.params[key].vh.copy()
+				eta.map(v => Math.min(eta_ub, Math.max(eta_lb, this_._alpha / Math.sqrt(v))))
+				const ret = this.params[key].m.copy()
+				ret.broadcastOperate(eta, (a, b) => (a * b) / Math.sqrt(this.params[key].t))
+				this.params[key].t++
+				return valueIsNumber ? ret.toScaler() : ret
+			},
+		}
+	}
+}

lib/model/nns/optimizer/amsgrad.js

@@ -0,0 +1,43 @@
+import Matrix from '../../../util/matrix.js'
+
+export class AMSGradOptimizer {
+	constructor(lr = 0.001, beta1 = 0.9, beta2 = 0.999) {
+		this._learningrate = lr
+		this._beta1 = beta1
+		this._beta2 = beta2
+		this._a = t => this._learningrate / Math.sqrt(t)
+	}
+
+	set learningRate(value) {
+		this._learningrate = value
+	}
+
+	manager() {
+		const this_ = this
+		return {
+			get lr() {
+				return this_._learningrate
+			},
+			params: {},
+			delta(key, value) {
+				const valueIsNumber = typeof value === 'number'
+				if (valueIsNumber) {
+					value = new Matrix(1, 1, value)
+				}
+				if (!this.params[key]) {
+					const z = value.copy()
+					z.fill(0)
+					this.params[key] = { m: z.copy(), v: z.copy(), vh: z, t: 1 }
+				}
+				this.params[key].m.broadcastOperate(value, (a, b) => a * this_._beta1 + b * (1 - this_._beta1))
+				this.params[key].v.broadcastOperate(value, (a, b) => a * this_._beta2 + b ** 2 * (1 - this_._beta2))
+				this.params[key].vh.broadcastOperate(this.params[key].v, (a, b) => Math.max(a, b))
+				const ret = this.params[key].m.copy()
+				const lr = this_._a(this.params[key].t)
+				ret.broadcastOperate(this.params[key].vh, (a, b) => (lr * a) / Math.sqrt(b + 1.0e-12))
+				this.params[key].t++
+				return valueIsNumber ? ret.toScaler() : ret
+			},
+		}
+	}
+}