/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { Tensor, Tensor3D, Tensor4D } from '../tensor';
import { TensorLike } from '../types';
import { Activation } from './fused_util';
/**
 * Computes the dot product of two matrices with optional activation and bias.
 *
 * ```js
 * const a = tf.tensor2d([-1, -2], [1, 2]);
 * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const bias = tf.tensor2d([1, 2], [1, 2]);
 *
 * tf.fused.matMul(a, b, false, false, bias, 'relu').print();
 * ```
 *
 * @param a First matrix in dot product operation.
 * @param b Second matrix in dot product operation.
 * @param transposeA If true, `a` is transposed before multiplication.
 * @param transposeB If true, `b` is transposed before multiplication.
 * @param bias Matrix to be added to the result.
 * @param activation Name of activation kernel (defaults to `linear`).
 */
/** @doc {heading: 'Operations', subheading: 'Matrices', namespace: 'fused'} */
declare function matMul_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike, transposeA?: boolean, transposeB?: boolean, bias?: Tensor | TensorLike, activation?: Activation): T;
/**
 * Computes a 2D convolution over the input x, optionally fused with adding a
 * bias and applying an activation.
 *
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter, rank 4, of shape
 *     `[filterHeight, filterWidth, inDepth, outDepth]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
 *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels]. Only "NHWC" is currently supported.
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dimRoundingMode The rounding mode used when computing output
 *     dimensions if pad is a number. If none is provided, it will not round
 *     and error if the output is of fractional size.
 * @param bias Tensor to be added to the result.
 * @param activation Name of activation kernel (defaults to `linear`).
 */
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
declare function conv2d_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil', bias?: Tensor | TensorLike, activation?: Activation): T;
export declare const matMul: typeof matMul_;
export declare const conv2d: typeof conv2d_;
export { Activation };