@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
/**
* @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.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
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}
};
Object.defineProperty(exports, "__esModule", { value: true });
var where_impl_1 = require("../backends/where_impl");
var engine_1 = require("../engine");
var tensor_util_env_1 = require("../tensor_util_env");
var util_1 = require("../util");
var broadcast_util_1 = require("./broadcast_util");
var operation_1 = require("./operation");
var tensor_ops_1 = require("./tensor_ops");
/**
* Returns the truth value of `NOT x` element-wise.
*
* ```js
* const a = tf.tensor1d([false, true], 'bool');
*
* a.logicalNot().print();
* ```
*
* @param x The input tensor. Must be of dtype 'bool'.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
function logicalNot_(x) {
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'logicalNot', 'bool');
return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.logicalNot($x); }, { $x: $x });
}
/**
* Returns the truth value of `a AND b` element-wise. Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([false, false, true, true], 'bool');
* const b = tf.tensor1d([false, true, false, true], 'bool');
*
* a.logicalAnd(b).print();
* ```
*
* @param a The first input tensor. Must be of dtype bool.
* @param b The second input tensor. Must be of dtype bool.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
function logicalAnd_(a, b) {
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'logicalAnd', 'bool');
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'logicalAnd', 'bool');
broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.logicalAnd($a, $b); }, { a: $a, b: $b }, null /* grad */, 'LogicalAnd');
}
/**
* Returns the truth value of `a OR b` element-wise. Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([false, false, true, true], 'bool');
* const b = tf.tensor1d([false, true, false, true], 'bool');
*
* a.logicalOr(b).print();
* ```
* @param a The first input tensor. Must be of dtype bool.
* @param b The second input tensor. Must be of dtype bool.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
function logicalOr_(a, b) {
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'logicalOr', 'bool');
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'logicalOr', 'bool');
broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.logicalOr($a, $b); }, { $a: $a, $b: $b });
}
/**
* Returns the truth value of `a XOR b` element-wise. Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([false, false, true, true], 'bool');
* const b = tf.tensor1d([false, true, false, true], 'bool');
*
* a.logicalXor(b).print();
* ```
*
* @param a The first input tensor. Must be of dtype bool.
* @param b The second input tensor. Must be of dtype bool.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
function logicalXor_(a, b) {
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'logicalXor', 'bool');
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'logicalXor', 'bool');
broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
// x ^ y = (x | y) & ~(x & y)
return exports.logicalOr(a, b).logicalAnd(exports.logicalAnd(a, b).logicalNot());
}
/**
* Returns the elements, either `a` or `b` depending on the `condition`.
*
* If the condition is true, select from `a`, otherwise select from `b`.
*
* ```js
* const cond = tf.tensor1d([false, false, true], 'bool');
* const a = tf.tensor1d([1 , 2, 3]);
* const b = tf.tensor1d([-1, -2, -3]);
*
* a.where(cond, b).print();
* ```
*
* @param condition The input condition. Must be of dtype bool.
* @param a If `condition` is rank 1, `a` may have a higher rank but
* its first dimension must match the size of `condition`.
* @param b A tensor with the same shape and type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
function where_(condition, a, b) {
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'where');
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'where');
var $condition = tensor_util_env_1.convertToTensor(condition, 'condition', 'where', 'bool');
util_1.assertShapesMatch($a.shape, $b.shape, 'Error in where: ');
if ($condition.rank === 1) {
// If condition rank is 1, then the first dimension must match the size of
// condition.
util_1.assert($condition.shape[0] === $a.shape[0], function () { return 'The first dimension of `a` must match the size of `condition`.'; });
}
else {
// A must have the same shape as condition.
util_1.assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');
}
// TODO(julianoks): Return null for condition gradient
// when backprop supports it.
var grad = function (dy, saved) {
var $condition = saved[0];
return {
$condition: function () { return tensor_ops_1.zerosLike($condition).toFloat(); },
$a: function () { return dy.mul($condition.cast(dy.dtype)); },
$b: function () { return dy.mul($condition.logicalNot().cast(dy.dtype)); }
};
};
return engine_1.ENGINE.runKernelFunc(function (backend, save) {
var res = backend.select($condition, $a, $b);
save([$condition]);
return res;
}, { $condition: $condition, $a: $a, $b: $b }, grad);
}
/**
* Returns the coordinates of true elements of condition.
*
* The coordinates are returned in a 2-D tensor where the first dimension (rows)
* represents the number of true elements, and the second dimension (columns)
* represents the coordinates of the true elements. Keep in mind, the shape of
* the output tensor can vary depending on how many true values there are in
* input. Indices are output in row-major order. The resulting tensor has the
* shape `[numTrueElems, condition.rank]`.
*
* This is analogous to calling the python `tf.where(cond)` without an x or y.
*
* ```js
* const cond = tf.tensor1d([false, false, true], 'bool');
* const result = await tf.whereAsync(cond);
* result.print();
* ```
*/
/** @doc {heading: 'Operations', subheading: 'Logical'} */
function whereAsync_(condition) {
return __awaiter(this, void 0, void 0, function () {
var $condition, vals, res;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
$condition = tensor_util_env_1.convertToTensor(condition, 'condition', 'whereAsync', 'bool');
return [4 /*yield*/, $condition.data()];
case 1:
vals = _a.sent();
res = where_impl_1.whereImpl($condition.shape, vals);
if (condition !== $condition) {
$condition.dispose();
}
return [2 /*return*/, res];
}
});
});
}
exports.logicalAnd = operation_1.op({ logicalAnd_: logicalAnd_ });
exports.logicalNot = operation_1.op({ logicalNot_: logicalNot_ });
exports.logicalOr = operation_1.op({ logicalOr_: logicalOr_ });
exports.logicalXor = operation_1.op({ logicalXor_: logicalXor_ });
exports.where = operation_1.op({ where_: where_ });
exports.whereAsync = whereAsync_;
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