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constraints.ts
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constraints.ts
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/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/* Original source: keras/contraints.py */
import * as tfc from '@tensorflow/tfjs-core';
import {serialization, Tensor, tidy} from '@tensorflow/tfjs-core';
import {epsilon} from './backend/common';
import {deserializeKerasObject, serializeKerasObject} from './utils/generic_utils';
/**
* Helper function used by many of the Constraints to find the L2Norms.
*/
function calcL2Norms(w: Tensor, axis: number): Tensor {
return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));
}
/**
* Base class for functions that impose constraints on weight values
*
* @doc {
* heading: 'Constraints',
* subheading: 'Classes',
* namespace: 'constraints'
* }
*/
export abstract class Constraint extends serialization.Serializable {
/* Porting note: was __call__, apply chosen to match other similar choices */
abstract apply(w: Tensor): Tensor;
getConfig(): serialization.ConfigDict {
return {};
}
}
export interface MaxNormArgs {
/**
* Maximum norm for incoming weights
*/
maxValue?: number;
/**
* Axis along which to calculate norms.
*
* For instance, in a `Dense` layer the weight matrix
* has shape `[inputDim, outputDim]`,
* set `axis` to `0` to constrain each weight vector
* of length `[inputDim,]`.
* In a `Conv2D` layer with `dataFormat="channels_last"`,
* the weight tensor has shape
* `[rows, cols, inputDepth, outputDepth]`,
* set `axis` to `[0, 1, 2]`
* to constrain the weights of each filter tensor of size
* `[rows, cols, inputDepth]`.
*/
axis?: number;
}
export class MaxNorm extends Constraint {
/** @nocollapse */
static readonly className = 'MaxNorm';
private maxValue: number;
private axis: number;
private readonly defaultMaxValue = 2;
private readonly defaultAxis = 0;
constructor(args: MaxNormArgs) {
super();
this.maxValue =
args.maxValue != null ? args.maxValue : this.defaultMaxValue;
this.axis = args.axis != null ? args.axis : this.defaultAxis;
}
apply(w: Tensor): Tensor {
return tidy(() => {
const norms = calcL2Norms(w, this.axis);
const desired = tfc.clipByValue(norms, 0, this.maxValue);
return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));
});
}
override getConfig(): serialization.ConfigDict {
return {maxValue: this.maxValue, axis: this.axis};
}
}
serialization.registerClass(MaxNorm);
export interface UnitNormArgs {
/**
* Axis along which to calculate norms.
*
* For instance, in a `Dense` layer the weight matrix
* has shape `[inputDim, outputDim]`,
* set `axis` to `0` to constrain each weight vector
* of length `[inputDim,]`.
* In a `Conv2D` layer with `dataFormat="channels_last"`,
* the weight tensor has shape
* `[rows, cols, inputDepth, outputDepth]`,
* set `axis` to `[0, 1, 2]`
* to constrain the weights of each filter tensor of size
* `[rows, cols, inputDepth]`.
*/
axis?: number;
}
export class UnitNorm extends Constraint {
/** @nocollapse */
static readonly className = 'UnitNorm';
private axis: number;
private readonly defaultAxis = 0;
constructor(args: UnitNormArgs) {
super();
this.axis = args.axis != null ? args.axis : this.defaultAxis;
}
apply(w: Tensor): Tensor {
return tidy(
() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));
}
override getConfig(): serialization.ConfigDict {
return {axis: this.axis};
}
}
serialization.registerClass(UnitNorm);
export class NonNeg extends Constraint {
/** @nocollapse */
static readonly className = 'NonNeg';
apply(w: Tensor): Tensor {
return tfc.relu(w);
}
}
serialization.registerClass(NonNeg);
export interface MinMaxNormArgs {
/**
* Minimum norm for incoming weights
*/
minValue?: number;
/**
* Maximum norm for incoming weights
*/
maxValue?: number;
/**
* Axis along which to calculate norms.
* For instance, in a `Dense` layer the weight matrix
* has shape `[inputDim, outputDim]`,
* set `axis` to `0` to constrain each weight vector
* of length `[inputDim,]`.
* In a `Conv2D` layer with `dataFormat="channels_last"`,
* the weight tensor has shape
* `[rows, cols, inputDepth, outputDepth]`,
* set `axis` to `[0, 1, 2]`
* to constrain the weights of each filter tensor of size
* `[rows, cols, inputDepth]`.
*/
axis?: number;
/**
* Rate for enforcing the constraint: weights will be rescaled to yield:
* `(1 - rate) * norm + rate * norm.clip(minValue, maxValue)`.
* Effectively, this means that rate=1.0 stands for strict
* enforcement of the constraint, while rate<1.0 means that
* weights will be rescaled at each step to slowly move
* towards a value inside the desired interval.
*/
rate?: number;
}
export class MinMaxNorm extends Constraint {
/** @nocollapse */
static readonly className = 'MinMaxNorm';
private minValue: number;
private maxValue: number;
private rate: number;
private axis: number;
private readonly defaultMinValue = 0.0;
private readonly defaultMaxValue = 1.0;
private readonly defaultRate = 1.0;
private readonly defaultAxis = 0;
constructor(args: MinMaxNormArgs) {
super();
this.minValue =
args.minValue != null ? args.minValue : this.defaultMinValue;
this.maxValue =
args.maxValue != null ? args.maxValue : this.defaultMaxValue;
this.rate = args.rate != null ? args.rate : this.defaultRate;
this.axis = args.axis != null ? args.axis : this.defaultAxis;
}
apply(w: Tensor): Tensor {
return tidy(() => {
const norms = calcL2Norms(w, this.axis);
const desired = tfc.add(
tfc.mul(
this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)),
tfc.mul(1.0 - this.rate, norms));
return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));
});
}
override getConfig(): serialization.ConfigDict {
return {
minValue: this.minValue,
maxValue: this.maxValue,
rate: this.rate,
axis: this.axis
};
}
}
serialization.registerClass(MinMaxNorm);
/** @docinline */
export type ConstraintIdentifier =
'maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string;
// Maps the JavaScript-like identifier keys to the corresponding registry
// symbols.
export const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP:
{[identifier in ConstraintIdentifier]: string} = {
'maxNorm': 'MaxNorm',
'minMaxNorm': 'MinMaxNorm',
'nonNeg': 'NonNeg',
'unitNorm': 'UnitNorm'
};
export function serializeConstraint(constraint: Constraint):
serialization.ConfigDictValue {
return serializeKerasObject(constraint);
}
export function deserializeConstraint(
config: serialization.ConfigDict,
customObjects: serialization.ConfigDict = {}): Constraint {
return deserializeKerasObject(
config, serialization.SerializationMap.getMap().classNameMap,
customObjects, 'constraint');
}
export function getConstraint(identifier: ConstraintIdentifier|
serialization.ConfigDict|Constraint): Constraint {
if (identifier == null) {
return null;
}
if (typeof identifier === 'string') {
const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?
CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :
identifier;
const config = {className, config: {}};
return deserializeConstraint(config);
} else if (identifier instanceof Constraint) {
return identifier;
} else {
return deserializeConstraint(identifier);
}
}