@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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TypeScript
/**
* @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 };