/// <amd-module name="@tensorflow/tfjs-layers/dist/exports_initializers" />
|
/**
|
* @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.
|
* =============================================================================
|
*/
|
import { ConstantArgs, IdentityArgs, Initializer, OrthogonalArgs, RandomNormalArgs, RandomUniformArgs, SeedOnlyInitializerArgs, TruncatedNormalArgs, VarianceScalingArgs, Zeros } from './initializers';
|
/**
|
* Initializer that generates tensors initialized to 0.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function zeros(): Zeros;
|
/**
|
* Initializer that generates tensors initialized to 1.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function ones(): Initializer;
|
/**
|
* Initializer that generates values initialized to some constant.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function constant(args: ConstantArgs): Initializer;
|
/**
|
* Initializer that generates random values initialized to a uniform
|
* distribution.
|
*
|
* Values will be distributed uniformly between the configured minval and
|
* maxval.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function randomUniform(args: RandomUniformArgs): Initializer;
|
/**
|
* Initializer that generates random values initialized to a normal
|
* distribution.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function randomNormal(args: RandomNormalArgs): Initializer;
|
/**
|
* Initializer that generates random values initialized to a truncated normal
|
* distribution.
|
*
|
* These values are similar to values from a `RandomNormal` except that values
|
* more than two standard deviations from the mean are discarded and re-drawn.
|
* This is the recommended initializer for neural network weights and filters.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function truncatedNormal(args: TruncatedNormalArgs): Initializer;
|
/**
|
* Initializer that generates the identity matrix.
|
* Only use for square 2D matrices.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function identity(args: IdentityArgs): Initializer;
|
/**
|
* Initializer capable of adapting its scale to the shape of weights.
|
* With distribution=NORMAL, samples are drawn from a truncated normal
|
* distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:
|
* - number of input units in the weight tensor, if mode = FAN_IN.
|
* - number of output units, if mode = FAN_OUT.
|
* - average of the numbers of input and output units, if mode = FAN_AVG.
|
* With distribution=UNIFORM,
|
* samples are drawn from a uniform distribution
|
* within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
|
*
|
* @doc {heading: 'Initializers',namespace: 'initializers'}
|
*/
|
export declare function varianceScaling(config: VarianceScalingArgs): Initializer;
|
/**
|
* Glorot uniform initializer, also called Xavier uniform initializer.
|
* It draws samples from a uniform distribution within [-limit, limit]
|
* where `limit` is `sqrt(6 / (fan_in + fan_out))`
|
* where `fan_in` is the number of input units in the weight tensor
|
* and `fan_out` is the number of output units in the weight tensor
|
*
|
* Reference:
|
* Glorot & Bengio, AISTATS 2010
|
* http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function glorotUniform(args: SeedOnlyInitializerArgs): Initializer;
|
/**
|
* Glorot normal initializer, also called Xavier normal initializer.
|
* It draws samples from a truncated normal distribution centered on 0
|
* with `stddev = sqrt(2 / (fan_in + fan_out))`
|
* where `fan_in` is the number of input units in the weight tensor
|
* and `fan_out` is the number of output units in the weight tensor.
|
*
|
* Reference:
|
* Glorot & Bengio, AISTATS 2010
|
* http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function glorotNormal(args: SeedOnlyInitializerArgs): Initializer;
|
/**
|
* He normal initializer.
|
*
|
* It draws samples from a truncated normal distribution centered on 0
|
* with `stddev = sqrt(2 / fanIn)`
|
* where `fanIn` is the number of input units in the weight tensor.
|
*
|
* Reference:
|
* He et al., http://arxiv.org/abs/1502.01852
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function heNormal(args: SeedOnlyInitializerArgs): Initializer;
|
/**
|
* He uniform initializer.
|
*
|
* It draws samples from a uniform distribution within [-limit, limit]
|
* where `limit` is `sqrt(6 / fan_in)`
|
* where `fanIn` is the number of input units in the weight tensor.
|
*
|
* Reference:
|
* He et al., http://arxiv.org/abs/1502.01852
|
*
|
* @doc {heading: 'Initializers',namespace: 'initializers'}
|
*/
|
export declare function heUniform(args: SeedOnlyInitializerArgs): Initializer;
|
/**
|
* LeCun normal initializer.
|
*
|
* It draws samples from a truncated normal distribution centered on 0
|
* with `stddev = sqrt(1 / fanIn)`
|
* where `fanIn` is the number of input units in the weight tensor.
|
*
|
* References:
|
* [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
|
* [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function leCunNormal(args: SeedOnlyInitializerArgs): Initializer;
|
/**
|
* LeCun uniform initializer.
|
*
|
* It draws samples from a uniform distribution in the interval
|
* `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,
|
* where `fanIn` is the number of input units in the weight tensor.
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function leCunUniform(args: SeedOnlyInitializerArgs): Initializer;
|
/**
|
* Initializer that generates a random orthogonal matrix.
|
*
|
* Reference:
|
* [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)
|
*
|
* @doc {heading: 'Initializers', namespace: 'initializers'}
|
*/
|
export declare function orthogonal(args: OrthogonalArgs): Initializer;
|
