@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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TypeScript
/**
* @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 { Tensor } from '../tensor';
import { TensorLike } from '../types';
/**
* Computes the log(sum(exp(elements across the reduction dimensions)).
*
* Reduces the input along the dimensions given in `axis`. Unless `keepDims`
* is true, the rank of the array is reduced by 1 for each entry in `axis`.
* If `keepDims` is true, the reduced dimensions are retained with length 1.
* If `axis` has no entries, all dimensions are reduced, and an array with a
* single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.logSumExp().print(); // or tf.logSumExp(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* const axis = 1;
* x.logSumExp(axis).print(); // or tf.logSumExp(a, axis)
* ```
* @param x The input tensor.
* @param axis The dimension(s) to reduce. If null (the default),
* reduces all dimensions.
* @param keepDims If true, retains reduced dimensions with length
* of 1. Defaults to false.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function logSumExp_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Computes the sum of elements across dimensions of a `tf.Tensor`.
*
* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
* `axes`. If `keepDims` is true, the reduced dimensions are retained with
* length 1. If axes has no entries, all dimensions are reduced, and a
* `tf.Tensor` with a single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.sum().print(); // or tf.sum(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* const axis = 1;
* x.sum(axis).print(); // or tf.sum(x, axis)
* ```
*
* @param x The input tensor to compute the sum over. If the dtype is `bool`
* it will be converted to `int32` and the output dtype will be `int32`.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function sum_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Computes the product of elements across dimensions of a `tf.Tensor`.
*
* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
* `axes`. If `keepDims` is true, the reduced dimensions are retained with
* length 1. If `axes` has no entries, all dimensions are reduced, and a
* `tf.Tensor` with a single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.prod().print(); // or tf.prod(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* const axis = 1;
* x.prod(axis).print(); // or tf.prod(x, axis)
* ```
*
* @param x The input tensor to compute the product over. If the dtype is `bool`
* it will be converted to `int32` and the output dtype will be `int32`.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function prod_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Computes the mean of elements across dimensions of a `tf.Tensor`.
*
* Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is
* true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.
* If `keepDims` is true, the reduced dimensions are retained with length 1.
* If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with
* a single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.mean().print(); // or tf.mean(a)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* const axis = 1;
* x.mean(axis).print(); // or tf.mean(x, axis)
* ```
*
* @param x The input tensor.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function mean_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Computes the minimum value from the input.
*
* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
* is true, the rank of the array is reduced by 1 for each entry in `axes`.
* If `keepDims` is true, the reduced dimensions are retained with length 1.
* If `axes` has no entries, all dimensions are reduced, and an array with a
* single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.min().print(); // or tf.min(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* const axis = 1;
* x.min(axis).print(); // or tf.min(x, axis)
* ```
*
* @param x The input Tensor.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function min_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Computes the maximum of elements across dimensions of a `tf.Tensor`.
*
* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
* `axes`. If `keepDims` is true, the reduced dimensions are retained with
* length 1. If `axes` has no entries, all dimensions are reduced, and an
* `tf.Tensor` with a single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.max().print(); // or tf.max(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* const axis = 1;
* x.max(axis).print(); // or tf.max(x, axis)
* ```
*
* @param x The input tensor.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function max_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Returns the indices of the minimum values along an `axis`.
*
* The result has the same shape as `input` with the dimension along `axis`
* removed.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.argMin().print(); // or tf.argMin(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
*
* const axis = 1;
* x.argMin(axis).print(); // or tf.argMin(x, axis)
* ```
*
* @param x The input tensor.
* @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
*
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function argMin_<T extends Tensor>(x: Tensor | TensorLike, axis?: number): T;
/**
* Returns the indices of the maximum values along an `axis`.
*
* The result has the same shape as `input` with the dimension along `axis`
* removed.
*
* ```js
* const x = tf.tensor1d([1, 2, 3]);
*
* x.argMax().print(); // or tf.argMax(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
*
* const axis = 1;
* x.argMax(axis).print(); // or tf.argMax(x, axis)
* ```
*
* @param x The input tensor.
* @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function argMax_<T extends Tensor>(x: Tensor | TensorLike, axis?: number): T;
/**
* Computes the logical and of elements across dimensions of a `tf.Tensor`.
*
* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
* `axes`. If `keepDims` is true, the reduced dimensions are retained with
* length 1. If `axes` has no entries, all dimensions are reduced, and an
* `tf.Tensor` with a single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 1, 1], 'bool');
*
* x.all().print(); // or tf.all(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
*
* const axis = 1;
* x.all(axis).print(); // or tf.all(x, axis)
* ```
*
* @param x The input tensor. Must be of dtype bool.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function all_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Computes the logical or of elements across dimensions of a `tf.Tensor`.
*
* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
* `axes`. If `keepDims` is true, the reduced dimensions are retained with
* length 1. If `axes` has no entries, all dimensions are reduced, and an
* `tf.Tensor` with a single element is returned.
*
* ```js
* const x = tf.tensor1d([1, 1, 1], 'bool');
*
* x.any().print(); // or tf.any(x)
* ```
*
* ```js
* const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
*
* const axis = 1;
* x.any(axis).print(); // or tf.any(x, axis)
* ```
*
* @param x The input tensor. Must be of dtype bool.
* @param axis The dimension(s) to reduce. By default it reduces
* all dimensions.
* @param keepDims If true, retains reduced dimensions with size 1.
*/
/** @doc {heading: 'Operations', subheading: 'Reduction'} */
declare function any_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
/**
* Calculates the mean and variance of `x`. The mean and variance are
* calculated by aggregating the contents of `x` across `axes`. If `x` is
* 1-D and `axes = [0]` this is just the mean and variance of a vector.
*
* @param x The input tensor.
* @param axis The dimension(s) along with to compute mean and
* variance. By default it reduces all dimensions.
* @param keepDims If true, the moments have the same dimensionality as the
* input.
* @return An object with two keys: `mean` and `variance`.
*/
/** @doc {heading: 'Operations', subheading: 'Normalization'} */
declare function moments_(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): {
mean: Tensor;
variance: Tensor;
};
export declare const all: typeof all_;
export declare const any: typeof any_;
export declare const argMax: typeof argMax_;
export declare const argMin: typeof argMin_;
export declare const logSumExp: typeof logSumExp_;
export declare const max: typeof max_;
export declare const mean: typeof mean_;
export declare const min: typeof min_;
export declare const moments: typeof moments_;
export declare const sum: typeof sum_;
export declare const prod: typeof prod_;
export {};