"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("../engine");
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var gradients_1 = require("../gradients");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var axis_util = require("./axis_util");
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var operation_1 = require("./operation");
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var tensor_ops_1 = require("./tensor_ops");
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/**
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* Computes the log(sum(exp(elements across the reduction dimensions)).
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*
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* Reduces the input along the dimensions given in `axis`. Unless `keepDims`
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* is true, the rank of the array is reduced by 1 for each entry in `axis`.
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* If `keepDims` is true, the reduced dimensions are retained with length 1.
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* If `axis` has no entries, all dimensions are reduced, and an array with a
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* single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.logSumExp().print(); // or tf.logSumExp(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.logSumExp(axis).print(); // or tf.logSumExp(a, axis)
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* ```
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* @param x The input tensor.
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* @param axis The dimension(s) to reduce. If null (the default),
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* reduces all dimensions.
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* @param keepDims If true, retains reduced dimensions with length
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* of 1. Defaults to false.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function logSumExp_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'logSumExp');
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var axes = util.parseAxisParam(axis, $x.shape);
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var xMax = $x.max(axes, true /* keepDims */);
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var a = $x.sub(xMax);
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var b = a.exp();
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var c = b.sum(axes);
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var d = c.log();
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var res = xMax.reshape(d.shape).add(d);
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(res.shape, axes);
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return res.reshape(newShape);
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}
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return res;
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}
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/**
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* Computes the sum of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If axes has no entries, all dimensions are reduced, and a
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.sum().print(); // or tf.sum(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.sum(axis).print(); // or tf.sum(x, axis)
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* ```
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*
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* @param x The input tensor to compute the sum over. If the dtype is `bool`
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* it will be converted to `int32` and the output dtype will be `int32`.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function sum_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sum');
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if ($x.dtype === 'bool') {
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$x = $x.toInt();
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}
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var axes = util.parseAxisParam(axis, $x.shape);
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// Use a custom gradient to bypass 2 gradient backprops since sum is used
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// extremely often.
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var customOp = gradients_1.customGrad(function (x) {
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var permutation = axis_util.getAxesPermutation(axes, x.rank);
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var reductionAxes = axes;
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var permutedX = x;
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if (permutation != null) {
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permutedX = x.transpose(permutation);
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reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, x.rank);
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}
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var gradFunc = function (dy) {
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var expandedDyShape = x.shape.slice();
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axes.forEach(function (axis) {
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expandedDyShape[axis] = 1;
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});
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var expandedDy = dy.reshape(expandedDyShape);
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var derX = expandedDy.mul(tensor_ops_1.ones(x.shape, 'float32'));
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return derX;
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};
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var gradInputs = function (dy) {
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return { x: function () { return gradFunc(dy); } };
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};
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var attrs = { axes: reductionAxes };
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var value = engine_1.ENGINE.runKernelFunc(function (backend) { return backend.sum(permutedX, reductionAxes); }, { x: permutedX }, gradInputs, 'Sum', attrs);
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(value.shape, axes);
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value = value.reshape(newShape);
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}
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return { value: value, gradFunc: gradFunc };
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});
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return customOp($x);
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}
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/**
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* Computes the product of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If `axes` has no entries, all dimensions are reduced, and a
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.prod().print(); // or tf.prod(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.prod(axis).print(); // or tf.prod(x, axis)
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* ```
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*
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* @param x The input tensor to compute the product over. If the dtype is `bool`
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* it will be converted to `int32` and the output dtype will be `int32`.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function prod_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'prod');
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if ($x.dtype === 'bool') {
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$x = $x.toInt();
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}
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var axes = util.parseAxisParam(axis, $x.shape);
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var permutation = axis_util.getAxesPermutation(axes, $x.rank);
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var reductionAxes = axes;
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var permutedX = $x;
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if (permutation != null) {
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permutedX = $x.transpose(permutation);
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reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, $x.rank);
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}
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var value = engine_1.ENGINE.runKernelFunc(function (backend) { return backend.prod(permutedX, reductionAxes); }, { permutedX: permutedX });
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(value.shape, axes);
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value = value.reshape(newShape);
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}
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return value;
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}
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/**
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* Computes the mean of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is
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* true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.
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* If `keepDims` is true, the reduced dimensions are retained with length 1.
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* If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with
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* a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.mean().print(); // or tf.mean(a)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.mean(axis).print(); // or tf.mean(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function mean_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'mean');
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var axes = util.parseAxisParam(axis, $x.shape);
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var shapes = axis_util.computeOutAndReduceShapes($x.shape, axes);
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var reduceShape = shapes[1];
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var reduceSize = util.sizeFromShape(reduceShape);
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// Use a custom gradient to bypass 2 gradient backprops since mean is used
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// extremely often.
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var customOp = gradients_1.customGrad(function (x) {
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var reduceSizeScalar = tensor_ops_1.scalar(reduceSize);
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// Cast if needed.
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var xReduce = reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);
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var res = xReduce.div(reduceSizeScalar);
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var value = res.sum(axis, keepDims);
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var gradFunc = function (dy) {
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var expandedDyShape = x.shape.slice();
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axes.forEach(function (axis) {
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expandedDyShape[axis] = 1;
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});
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var expandedDy = dy.reshape(expandedDyShape);
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var derX = expandedDy.mul(tensor_ops_1.ones(x.shape, 'float32')).div(reduceSize);
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return derX;
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};
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return { value: value, gradFunc: gradFunc };
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});
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return customOp($x);
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}
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/**
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* Gradient helper function for the min and max operations.
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*/
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function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {
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if (y.rank < xOrig.rank) {
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y = y.reshape(axis_util.expandShapeToKeepDim(y.shape, origAxes));
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}
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if (dy.rank < xOrig.rank) {
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dy = dy.reshape(axis_util.expandShapeToKeepDim(dy.shape, origAxes));
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}
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return {
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x: function () {
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var dx = dy.mul(xOrig.equal(y).cast(dy.dtype));
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return permutedAxes == null ? dx : dx.transpose(permutedAxes);
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}
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};
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}
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/**
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* Computes the minimum value from the input.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the array is reduced by 1 for each entry in `axes`.
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* If `keepDims` is true, the reduced dimensions are retained with length 1.
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* If `axes` has no entries, all dimensions are reduced, and an array with a
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* single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.min().print(); // or tf.min(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.min(axis).print(); // or tf.min(x, axis)
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* ```
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*
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* @param x The input Tensor.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function min_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'min');
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var xOrig = $x;
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var origAxes = util.parseAxisParam(axis, $x.shape);
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var axes = origAxes;
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var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
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if (permutedAxes != null) {
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$x = $x.transpose(permutedAxes);
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axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
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}
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var grad = function (dy, saved) {
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return gradForMinAndMax(dy, saved[1], saved[0], origAxes, permutedAxes);
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};
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var inputsToSave = [$x];
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var outputsToSave = [true];
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var y = backend.min($x, axes);
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save([xOrig, y]);
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return y;
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}, { x: $x }, grad, 'Min', { axes: axes }, inputsToSave, outputsToSave);
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
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res = res.reshape(newShape);
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}
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return res;
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}
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/**
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* Computes the maximum of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If `axes` has no entries, all dimensions are reduced, and an
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.max().print(); // or tf.max(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.max(axis).print(); // or tf.max(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function max_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'max');
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var xOrig = $x;
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var origAxes = util.parseAxisParam(axis, $x.shape);
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var axes = origAxes;
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var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
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if (permutedAxes != null) {
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$x = $x.transpose(permutedAxes);
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axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
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}
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var grad = function (dy, saved) {
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return gradForMinAndMax(dy, saved[1], saved[0], origAxes, permutedAxes);
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};
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var inputsToSave = [$x];
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var outputsToSave = [true];
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var y = backend.max($x, axes);
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save([xOrig, y]);
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return y;
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}, { x: $x }, grad, 'Max', { axes: axes }, inputsToSave, outputsToSave);
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
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res = res.reshape(newShape);
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}
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return res;
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}
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/**
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* Returns the indices of the minimum values along an `axis`.
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*
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* The result has the same shape as `input` with the dimension along `axis`
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* removed.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.argMin().print(); // or tf.argMin(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
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*
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* const axis = 1;
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* x.argMin(axis).print(); // or tf.argMin(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
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*
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function argMin_(x, axis) {
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if (axis === void 0) { axis = 0; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'argMin');
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if (axis == null) {
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axis = 0;
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}
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var axes = util.parseAxisParam(axis, $x.shape);
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var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
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if (permutedAxes != null) {
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$x = $x.transpose(permutedAxes);
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axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
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}
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var grad = function (dy, saved) {
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var $x = saved[0];
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return { $x: function () { return tensor_ops_1.zerosLike($x); } };
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};
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return engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.argMin($x, axes[0]);
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save([$x]);
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return res;
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}, { $x: $x }, grad);
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}
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/**
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* Returns the indices of the maximum values along an `axis`.
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*
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* The result has the same shape as `input` with the dimension along `axis`
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* removed.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.argMax().print(); // or tf.argMax(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
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*
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* const axis = 1;
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* x.argMax(axis).print(); // or tf.argMax(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function argMax_(x, axis) {
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if (axis === void 0) { axis = 0; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'argMax');
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if (axis == null) {
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axis = 0;
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}
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var axes = util.parseAxisParam(axis, $x.shape);
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var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
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if (permutedAxes != null) {
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$x = $x.transpose(permutedAxes);
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axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
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}
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var grad = function (dy, saved) {
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var $x = saved[0];
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return { x: function () { return tensor_ops_1.zerosLike($x); } };
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};
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var attrs = { axis: axes[0] };
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var inputsToSave = [$x];
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return engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.argMax($x, axes[0]);
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save([$x]);
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return res;
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}, { x: $x }, grad, 'ArgMax', attrs, inputsToSave);
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}
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/**
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* 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
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* const x = tf.tensor1d([1, 1, 1], 'bool');
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*
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* x.all().print(); // or tf.all(x)
|
* ```
|
*
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* ```js
|
* const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
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*
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* const axis = 1;
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* x.all(axis).print(); // or tf.all(x, axis)
|
* ```
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*
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* @param x The input tensor. Must be of dtype bool.
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* @param axis The dimension(s) to reduce. By default it reduces
|
* all dimensions.
|
* @param keepDims If true, retains reduced dimensions with size 1.
|
*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
|
function all_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'all', 'bool');
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var origAxes = util.parseAxisParam(axis, $x.shape);
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var axes = origAxes;
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var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
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if (permutedAxes != null) {
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$x = $x.transpose(permutedAxes);
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axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
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}
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var res = engine_1.ENGINE.runKernelFunc(function (backend) { return backend.all($x, axes); }, { $x: $x });
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
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return res.reshape(newShape);
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}
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return res;
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}
|
/**
|
* 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');
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*
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* x.any().print(); // or tf.any(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
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*
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* const axis = 1;
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* x.any(axis).print(); // or tf.any(x, axis)
|
* ```
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*
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* @param x The input tensor. Must be of dtype bool.
|
* @param axis The dimension(s) to reduce. By default it reduces
|
* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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function any_(x, axis, keepDims) {
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if (axis === void 0) { axis = null; }
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if (keepDims === void 0) { keepDims = false; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'any', 'bool');
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var origAxes = util.parseAxisParam(axis, $x.shape);
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var axes = origAxes;
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var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
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if (permutedAxes != null) {
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$x = $x.transpose(permutedAxes);
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axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
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}
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var res = engine_1.ENGINE.runKernelFunc(function (backend) { return backend.any($x, axes); }, { $x: $x });
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if (keepDims) {
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var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
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return res.reshape(newShape);
|
}
|
return res;
|
}
|
/**
|
* 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'} */
|
function moments_(x, axis, keepDims) {
|
if (axis === void 0) { axis = null; }
|
if (keepDims === void 0) { keepDims = false; }
|
x = tensor_util_env_1.convertToTensor(x, 'x', 'moments');
|
var axes = util.parseAxisParam(axis, x.shape);
|
var mean = x.mean(axes, keepDims);
|
var keepDimsShape = mean.shape;
|
if (!keepDims) {
|
keepDimsShape = axis_util.expandShapeToKeepDim(mean.shape, axes);
|
}
|
var devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();
|
var variance = devSquared.mean(axes, keepDims);
|
return { mean: mean, variance: variance };
|
}
|
exports.all = operation_1.op({ all_: all_ });
|
// tslint:disable-next-line:variable-name
|
exports.any = operation_1.op({ any_: any_ });
|
exports.argMax = operation_1.op({ argMax_: argMax_ });
|
exports.argMin = operation_1.op({ argMin_: argMin_ });
|
exports.logSumExp = operation_1.op({ logSumExp_: logSumExp_ });
|
exports.max = operation_1.op({ max_: max_ });
|
exports.mean = operation_1.op({ mean_: mean_ });
|
exports.min = operation_1.op({ min_: min_ });
|
exports.moments = operation_1.op({ moments_: moments_ });
|
exports.sum = operation_1.op({ sum_: sum_ });
|
exports.prod = operation_1.op({ prod_: prod_ });
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//# sourceMappingURL=reduction_ops.js.map
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