| 1 | /// <amd-module name="@tensorflow/tfjs-layers/dist/exports_constraints" />
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| 2 | /**
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| 3 | * @license
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| 4 | * Copyright 2018 Google LLC
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| 5 | *
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| 6 | * Use of this source code is governed by an MIT-style
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| 7 | * license that can be found in the LICENSE file or at
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| 8 | * https://opensource.org/licenses/MIT.
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| 9 | * =============================================================================
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| 10 | */
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| 11 | import { Constraint, MaxNormArgs, MinMaxNormArgs, UnitNormArgs } from './constraints';
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| 12 | /**
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| 13 | * MaxNorm weight constraint.
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| 14 | *
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| 15 | * Constrains the weights incident to each hidden unit
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| 16 | * to have a norm less than or equal to a desired value.
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| 17 | *
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| 18 | * References
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| 19 | * - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting
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| 20 | * Srivastava, Hinton, et al.
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| 21 | * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
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| 22 | *
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| 23 | * @doc {heading: 'Constraints',namespace: 'constraints'}
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| 24 | */
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| 25 | export declare function maxNorm(args: MaxNormArgs): Constraint;
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| 26 | /**
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| 27 | * Constrains the weights incident to each hidden unit to have unit norm.
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| 28 | *
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| 29 | * @doc {heading: 'Constraints', namespace: 'constraints'}
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| 30 | */
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| 31 | export declare function unitNorm(args: UnitNormArgs): Constraint;
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| 32 | /**
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| 33 | * Constains the weight to be non-negative.
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| 34 | *
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| 35 | * @doc {heading: 'Constraints', namespace: 'constraints'}
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| 36 | */
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| 37 | export declare function nonNeg(): Constraint;
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| 38 | /** @doc {heading: 'Constraints', namespace: 'constraints'} */
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| 39 | export declare function minMaxNorm(config: MinMaxNormArgs): Constraint;
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