@tensorflow/tfjs-core

/** * @license * Copyright 2018 Google Inc. 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 { Tensor3D, Tensor4D, Tensor5D } from '../tensor'; import { TensorLike } from '../types'; /** * Computes the 2D max pooling of an image. * * @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 filterSize The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `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 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. */ /** @doc {heading: 'Operations', subheading: 'Convolution'} */ declare function maxPool_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T; /** * Computes the 2D average pooling of an image. * * @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 filterSize The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `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 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. */ /** @doc {heading: 'Operations', subheading: 'Convolution'} */ declare function avgPool_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T; /** * Performs an N-D pooling operation * * @param input The input tensor, of rank 4 or rank 3 of shape * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. * @param windowShape The filter size: `[filterHeight, filterWidth]`. If * `filterSize` is a single number, then `filterHeight == filterWidth`. * @param poolingType The type of pooling, either 'max' or 'avg'. * @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 dilations The dilation rates: `[dilationHeight, dilationWidth]` * in which we sample input values across the height and width dimensions * in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single * number, then `dilationHeight == dilationWidth`. If it is greater than * 1, then all values of `strides` must be 1. * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If * `strides` is a single number, then `strideHeight == strideWidth`. */ /** @doc {heading: 'Operations', subheading: 'Convolution'} */ declare function pool_<T extends Tensor3D | Tensor4D>(input: T | TensorLike, windowShape: [number, number] | number, poolingType: 'avg' | 'max', pad: 'valid' | 'same' | number, dilations?: [number, number] | number, strides?: [number, number] | number): T; /** * Computes the 3D average pooling. * * ```js * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); * const result = tf.avgPool3d(x, 2, 1, 'valid'); * result.print(); * ``` * * @param x The input tensor, of rank 5 or rank 4 of shape * `[batch, depth, height, width, inChannels]`. * @param filterSize The filter size: * `[filterDepth, filterHeight, filterWidth]`. * If `filterSize` is a single number, * then `filterDepth == filterHeight == filterWidth`. * @param strides The strides of the pooling: * `[strideDepth, strideHeight, strideWidth]`. * If `strides` is a single number, * then `strideDepth == 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 1*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 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 dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to * "NDHWC". Specify the data format of the input and output data. With the * default format "NDHWC", the data is stored in the order of: [batch, * depth, height, width, channels]. Only "NDHWC" is currently supported. * @param dilations The dilation rates: * `[dilationDepth, dilationHeight, dilationWidth]` * in which we sample input values across the depth, height and width * dimensions in dilated pooling. * Defaults to `[1, 1, 1]`. If `dilations` is a single number, * then `dilationDepth == dilationHeight == dilationWidth`. * If it is greater than 1, then all values of `strides` must be 1. */ /** @doc {heading: 'Operations', subheading: 'Convolution'} */ declare function avgPool3d_<T extends Tensor4D | Tensor5D>(x: T | TensorLike, filterSize: [number, number, number] | number, strides: [number, number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'NDHWC' | 'NCDHW', dilations?: [number, number, number] | number): T; /** * Computes the 3D max pooling. * * ```js * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); * const result = tf.maxPool3d(x, 2, 1, 'valid'); * result.print(); * ``` * * @param x The input tensor, of rank 5 or rank 4 of shape * `[batch, depth, height, width, inChannels]`. * @param filterSize The filter size: * `[filterDepth, filterHeight, filterWidth]`. * If `filterSize` is a single number, * then `filterDepth == filterHeight == filterWidth`. * @param strides The strides of the pooling: * `[strideDepth, strideHeight, strideWidth]`. * If `strides` is a single number, * then `strideDepth == 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 1*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 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 dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to * "NDHWC". Specify the data format of the input and output data. With the * default format "NDHWC", the data is stored in the order of: [batch, * depth, height, width, channels]. Only "NDHWC" is currently supported. * @param dilations The dilation rates: * `[dilationDepth, dilationHeight, dilationWidth]` * in which we sample input values across the depth, height and width * dimensions in dilated pooling. * Defaults to `[1, 1, 1]`. If `dilations` is a single number, * then `dilationDepth == dilationHeight == dilationWidth`. * If it is greater than 1, then all values of `strides` must be 1. */ /** @doc {heading: 'Operations', subheading: 'Convolution'} */ declare function maxPool3d_<T extends Tensor4D | Tensor5D>(x: T | TensorLike, filterSize: [number, number, number] | number, strides: [number, number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'NDHWC' | 'NCDHW', dilations?: [number, number, number] | number): T; export declare const maxPool: typeof maxPool_; export declare const avgPool: typeof avgPool_; export declare const pool: typeof pool_; export declare const maxPool3d: typeof maxPool3d_; export declare const avgPool3d: typeof avgPool3d_; export {};