/**
 * @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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-layers/dist/layers/merge" />
import { serialization, Tensor } from '@tensorflow/tfjs-core';
import { Layer, LayerArgs, SymbolicTensor } from '../engine/topology';
import { Shape } from '../keras_format/common';
import { Kwargs } from '../types';
/**
 * Generic Merge layer for element-wise merge functions.
 *
 * Used to implement `Sum`, `Average`, `Concatenate`, etc.
 */
export declare abstract class Merge extends Layer {
    protected reshapeRequired: boolean;
    constructor(args?: LayerArgs);
    /**
     * Logic for merging multiple tensors, to be overridden by subclasses.
     * @param inputs
     */
    protected mergeFunction(inputs: Tensor[]): Tensor;
    /**
     * Computes the shape of the result of an elementwise operation.
     *
     * @param shape1: Shape of the first tensor.
     * @param shape2: Shape of the second tensor.
     * @returns Expected output shape when an elementwise operation is carried
     *   out on 2 tensors with shapes `shape1` and `shape2`.
     * @throws ValueError: If `shape1` and `shape2` are not compatible for
     *   element-wise operations.
     */
    private computeElementwiseOpOutputShape;
    build(inputShape: Shape | Shape[]): void;
    call(inputs: Tensor | Tensor[], kwargs: Kwargs): Tensor | Tensor[];
    computeOutputShape(inputShape: Shape | Shape[]): Shape | Shape[];
    computeMask(inputs: Tensor | Tensor[], mask?: Tensor | Tensor[]): Tensor;
}
export declare class Add extends Merge {
    /** @nocollapse */
    static className: string;
    constructor(args?: LayerArgs);
    protected mergeFunction(inputs: Tensor[]): Tensor;
}
/**
 * Calculate the element-wise sum of inputs, which all have the same shape.
 *
 * This function can be invoked in three ways.
 *
 * 1. Construct an instance of `Add` layer, by using no input argument
 *    or a single configuration argument. The resultant `Add` layer can then
 *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
 *
 * ```js
 * const addLayer = tf.layers.add();
 *
 * // The layer can be applied to inputs.
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = addLayer.apply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.SymbolicTensor`. For example:
 *
 * ```js
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = tf.layers.add([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.Tensor` as the result of the computation. For
 * example:
 *
 * ```js
 * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);
 * tf.layers.add([input1, input2]).print();
 * // Gives [[11, 22], [33, 44]].
 *
 */
export declare function add(config?: SymbolicTensor[] | Tensor[] | LayerArgs): Layer | SymbolicTensor | Tensor;
export declare class Multiply extends Merge {
    /** @nocollapse */
    static className: string;
    constructor(args?: LayerArgs);
    protected mergeFunction(inputs: Tensor[]): Tensor;
}
/**
 * Calculate the element-wise product of inputs, which all have the same shape.
 *
 * This function can be invoked in three ways.
 *
 * 1. Construct an instance of `Multiply` layer, by using no input argument
 *    or a single configuration argument. The resultant `Multiply` layer can
 *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
 *
 * ```js
 * const multiplyLayer = tf.layers.multiply();
 *
 * // The layer can be applied to inputs.
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = multiplyLayer.apply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.SymbolicTensor`. For example:
 *
 * ```js
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = tf.layers.multiply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.Tensor` as the result of the computation. For
 * example:
 *
 * ```js
 * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);
 * tf.layers.multiply([input1, input2]).print();
 * // Gives [[10, 40], [90, 160]].
 *
 */
export declare function multiply(config?: SymbolicTensor[] | Tensor[] | LayerArgs): Layer | SymbolicTensor | Tensor;
export declare class Average extends Merge {
    /** @nocollapse */
    static className: string;
    constructor(args?: LayerArgs);
    protected mergeFunction(inputs: Tensor[]): Tensor;
}
/**
 * Calculate the element-wise arithmetic mean of inputs, which all have the same
 * shape.
 *
 * This function can be invoked in three ways.
 *
 * 1. Construct an instance of `Average` layer, by using no input argument
 *    or a single configuration argument. The resultant `Average` layer can then
 *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
 *
 * ```js
 * const averageLayer = tf.layers.average();
 *
 * // The layer can be applied to inputs.
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = averageLayer.apply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.SymbolicTensor`. For example:
 *
 * ```js
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = tf.layers.average([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.Tensor` as the result of the computation. For
 * example:
 *
 * ```js
 * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);
 * tf.layers.average([input1, input2]).print();
 * // Gives [[5.5, 11], [16.5, 22]].
 *
 */
export declare function average(config?: SymbolicTensor[] | Tensor[] | LayerArgs): Layer | SymbolicTensor | Tensor;
export declare class Maximum extends Merge {
    /** @nocollapse */
    static className: string;
    constructor(args?: LayerArgs);
    protected mergeFunction(inputs: Tensor[]): Tensor;
}
/**
 * Calculate the element-wise maximum of inputs, which all have the same shape.
 *
 * This function can be invoked in three ways.
 *
 * 1. Construct an instance of `Maximum` layer, by using no input argument
 *    or a single configuration argument. The resultant `Maximum` layer can then
 *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
 *
 * ```js
 * const maximumLayer = tf.layers.maximum();
 *
 * // The layer can be applied to inputs.
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = maximumLayer.apply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.SymbolicTensor`. For example:
 *
 * ```js
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = tf.layers.maximum([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.Tensor` as the result of the computation. For
 * example:
 *
 * ```js
 * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);
 * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);
 * tf.layers.maximum([input1, input2]).print();
 * // Gives [[10, 20], [30, 40]].
 *
 */
export declare function maximum(config?: SymbolicTensor[] | Tensor[] | LayerArgs): Layer | SymbolicTensor | Tensor;
export declare class Minimum extends Merge {
    /** @nocollapse */
    static className: string;
    constructor(args?: LayerArgs);
    protected mergeFunction(inputs: Tensor[]): Tensor;
}
/**
 * Calculate the element-wise minimum of inputs, which all have the same shape.
 *
 * This function can be invoked in three ways.
 *
 * 1. Construct an instance of `Minimum` layer, by using no input argument
 *    or a single configuration argument. The resultant `Minimum` layer can then
 *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
 *
 * ```js
 * const minimumLayer = tf.layers.minimum();
 *
 * // The layer can be applied to inputs.
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = minimumLayer.apply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.SymbolicTensor`. For example:
 *
 * ```js
 * const input1 = tf.input({shape: [2, 2]});
 * const input2 = tf.input({shape: [2, 2]});
 * const output = tf.layers.minimum([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension.
 * ```
 *
 * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.Tensor` as the result of the computation. For
 * example:
 *
 * ```js
 * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);
 * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);
 * tf.layers.minimum([input1, input2]).print();
 * // Gives [[1, 2], [3, 4]].
 *
 */
export declare function minimum(config?: SymbolicTensor[] | Tensor[] | LayerArgs): Layer | SymbolicTensor | Tensor;
export declare interface ConcatenateLayerArgs extends LayerArgs {
    /**
     * Axis along which to concatenate.
     */
    axis?: number;
}
export declare class Concatenate extends Merge {
    /** @nocollapse */
    static className: string;
    readonly DEFAULT_AXIS = -1;
    private readonly axis;
    constructor(args?: ConcatenateLayerArgs);
    build(inputShape: Shape | Shape[]): void;
    protected mergeFunction(inputs: Tensor[]): Tensor;
    computeOutputShape(inputShape: Shape | Shape[]): Shape | Shape[];
    computeMask(inputs: Tensor | Tensor[], mask?: Tensor | Tensor[]): Tensor;
    getConfig(): serialization.ConfigDict;
}
/**
 * Concatenate an `Array` of inputs.
 *
 * This function can be invoked in three ways.
 *
 * 1. Construct an instance of `Concatenate` layer, by using no input argument
 *    or a single configuration argument. The resultant `Concatenate` layer can
 *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:
 *
 * ```js
 * const concatLayer = tf.layers.concatenate();
 *
 * // The layer can be applied to inputs.
 * const input1 = tf.input({shape: [2, 3]});
 * const input2 = tf.input({shape: [2, 4]});
 * const output = concatLayer.apply([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 7], with the first dimension as the undetermined batch
 * // dimension and the last dimension as the result of concatenating the
 * // last dimensions of the two inputs.
 * ```
 *
 * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.SymbolicTensor`. For example:
 *
 * ```js
 * const input1 = tf.input({shape: [2, 3]});
 * const input2 = tf.input({shape: [2, 4]});
 * const output = tf.layers.concatenate([input1, input2]);
 * console.log(output.shape);
 * // You get [null, 2, 2], with the first dimension as the undetermined batch
 * // dimension and the last dimension as the result of concatenating the
 * // last dimensions of the two inputs.
 * ```
 *
 * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
 *    an `Layer` object internally and calls its `apply` method on the inputs,
 *    generating a new `tf.Tensor` as the result of the computation. For
 * example:
 *
 * ```js
 * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
 * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);
 * tf.layers.concatenate([input1, input2]).print();
 * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].
 *
 */
export declare function concatenate(config?: SymbolicTensor[] | Tensor[] | ConcatenateLayerArgs): Layer | SymbolicTensor | Tensor;
export declare interface DotLayerArgs extends LayerArgs {
    /**
     * Axis or axes along which the dot product will be taken.
     *
     * Integer or an Array of integers.
     */
    axes: number | [number, number];
    /**
     * Whether to L2-normalize samples along the dot product axis
     * before taking the dot product.
     *
     * If set to `true`, the output of the dot product is the cosine
     * proximity between the two samples.
     */
    normalize?: boolean;
}
export declare class Dot extends Merge {
    /** @nocollapse */
    static className: string;
    private axes;
    private normalize;
    constructor(args: DotLayerArgs);
    build(inputShape: Shape | Shape[]): void;
    protected mergeFunction(inputs: Tensor[]): Tensor;
    private interpretAxes;
    computeOutputShape(inputShape: Shape | Shape[]): Shape | Shape[];
    computeMask(inputs: Tensor | Tensor[], mask?: Tensor | Tensor[]): Tensor;
    getConfig(): serialization.ConfigDict;
}