/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/// <amd-module name="@tensorflow/tfjs-layers/dist/activations" />
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import { serialization, Tensor } from '@tensorflow/tfjs-core';
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import { ActivationIdentifier } from './keras_format/activation_config';
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/**
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* Base class for Activations.
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*
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* Special note: due to cross-language compatibility reasons, the
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* static readonly className field in this family of classes must be set to
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* the initialLowerCamelCase name of the activation.
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*/
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export declare abstract class Activation extends serialization.Serializable {
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abstract apply(tensor: Tensor, axis?: number): Tensor;
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getConfig(): serialization.ConfigDict;
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}
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/**
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* Exponential linear unit (ELU).
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* Reference: https://arxiv.org/abs/1511.07289
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*/
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export declare class Elu extends Activation {
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/** @nocollapse */
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static readonly className = "elu";
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/**
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* Calculate the activation function.
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*
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* @param x: Input.
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* @param alpha: Scaling factor the negative section.
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* @return Output of the ELU activation.
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*/
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apply(x: Tensor, alpha?: number): Tensor;
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}
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/**
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* Scaled Exponential Linear Unit. (Klambauer et al., 2017).
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* Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515
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* Notes:
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* - To be used together with the initialization "lecunNormal".
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* - To be used together with the dropout variant "AlphaDropout".
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*/
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export declare class Selu extends Activation {
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/** @nocollapse */
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static readonly className = "selu";
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apply(x: Tensor): Tensor;
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}
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/**
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* Rectified linear unit
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*/
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export declare class Relu extends Activation {
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/** @nocollapse */
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static readonly className = "relu";
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apply(x: Tensor): Tensor;
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}
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/**
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* Rectified linear unit activation maxing out at 6.0.
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*/
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export declare class Relu6 extends Activation {
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/** @nocollapse */
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static readonly className = "relu6";
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apply(x: Tensor): Tensor;
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}
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export declare class Linear extends Activation {
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/** @nocollapse */
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static readonly className = "linear";
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apply(x: Tensor): Tensor;
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}
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/**
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* Sigmoid activation function.
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*/
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export declare class Sigmoid extends Activation {
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/** @nocollapse */
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static readonly className = "sigmoid";
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apply(x: Tensor): Tensor;
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}
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/**
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* Segment-wise linear approximation of sigmoid.
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*/
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export declare class HardSigmoid extends Activation {
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/** @nocollapse */
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static readonly className = "hardSigmoid";
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apply(x: Tensor): Tensor;
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}
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/**
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* Softplus activation function.
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*/
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export declare class Softplus extends Activation {
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/** @nocollapse */
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static readonly className = "softplus";
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apply(x: Tensor): Tensor;
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}
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/**
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* Softsign activation function.
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*/
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export declare class Softsign extends Activation {
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/** @nocollapse */
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static readonly className = "softsign";
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apply(x: Tensor): Tensor;
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}
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/**
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* Hyperbolic tangent function.
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*/
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export declare class Tanh extends Activation {
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/** @nocollapse */
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static readonly className = "tanh";
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apply(x: Tensor): Tensor;
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}
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/**
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* Softmax activation function
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*/
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export declare class Softmax extends Activation {
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/** @nocollapse */
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static readonly className = "softmax";
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/**
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* Calculate the activation function.
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*
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* @param x Tensor.
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* @param axis Integer, axis along which the softmax normalization is applied.
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* Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be
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* an error.
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*
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* @returns a Tensor of the same shape as x
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*
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* @throws ValueError: In case `dim(x) < 2`.
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*/
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apply(x: Tensor, axis?: number): Tensor;
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}
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/**
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* Log softmax activation function
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*/
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export declare class LogSoftmax extends Activation {
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/** @nocollapse */
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static readonly className = "logSoftmax";
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/**
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* Calculate the activation function of log softmax:
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* log( exp(x_i) / sum(exp(x)) )
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*
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* @param x Tensor.
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* @param axis Integer, axis along which the softmax normalization is applied.
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* Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be
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* an error.
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*
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* @returns a Tensor of the same shape as x
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*
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* @throws ValueError: In case `dim(x) < 2`.
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*/
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apply(x: Tensor, axis?: number): Tensor;
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}
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/**
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* Swish activation function
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*/
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export declare class Swish extends Activation {
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/** @nocollapse */
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static readonly className = "swish";
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/**
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* Calculate the activation function.
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*
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* @param x Tensor.
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* @param alpha Scaling factor for the sigmoid function.
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* @returns a Tensor of the same shape as x
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*/
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apply(x: Tensor, alpha?: number): Tensor;
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}
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/**
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* Mish activation function
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*/
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export declare class Mish extends Activation {
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/** @nocollapse */
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static readonly className = "mish";
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/**
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* Calculate the activation function.
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*
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* @param x Tensor.
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* @returns a Tensor of the same shape as x
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*/
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apply(x: Tensor): Tensor;
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}
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export declare function serializeActivation(activation: Activation): string;
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export declare function deserializeActivation(config: serialization.ConfigDict, customObjects?: serialization.ConfigDict): Activation;
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export declare function getActivation(identifier: ActivationIdentifier | serialization.ConfigDict | Activation): Activation;
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