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binary_ops.ts
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binary_ops.ts
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/**
* @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 {ENV} from '../environment';
import {KernelBackend} from '../kernels/backend';
import {Tensor} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import {makeTypesMatch} from '../tensor_util';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike, upcastType} from '../types';
import * as util from '../util';
import * as broadcast_util from './broadcast_util';
import {op} from './operation';
import {scalar, zerosLike} from './tensor_ops';
import {neg} from './unary_ops';
/**
* Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
*
* We also expose `tf.addStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([1, 2, 3, 4]);
* const b = tf.tensor1d([10, 20, 30, 40]);
*
* a.add(b).print(); // or tf.add(a, b)
* ```
*
* ```js
* // Broadcast add a with b.
* const a = tf.scalar(5);
* const b = tf.tensor1d([10, 20, 30, 40]);
*
* a.add(b).print(); // or tf.add(a, b)
* ```
* @param a The first `tf.Tensor` to add.
* @param b The second `tf.Tensor` to add. Must have the same type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'add');
let $b = convertToTensor(b, 'b', 'add');
[$a, $b] = makeTypesMatch($a, $b);
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
let res = dy;
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($a.shape);
};
const derB = () => {
let res = dy;
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($b.shape);
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(backend => backend.add($a, $b), {$a, $b}, der) as
T;
}
/**
* Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
*
* ```js
* const a = tf.tensor1d([1, 2]);
* const b = tf.tensor1d([3, 4]);
* const c = tf.tensor1d([5, 6]);
*
* tf.addN([a, b, c]).print();
* ```
* @param tensors A list of tensors with the same shape and dtype.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {
util.assert(
Array.isArray(tensors),
() => 'The argument passed to tf.addN() must be a list of tensors');
util.assert(
tensors.length >= 1,
() => `Must pass at least one tensor to tf.addN(), but got ` +
`${tensors.length}`);
const $tensors =
tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));
const firstTensor = $tensors[0];
$tensors.forEach(t => {
if (t.dtype !== firstTensor.dtype) {
throw new Error(
'All tensors passed to tf.addN() must have the same dtype');
}
});
$tensors.forEach(t => {
if (!util.arraysEqual(t.shape, firstTensor.shape)) {
throw new Error(
'All tensors passed to tf.addN() must have the same shape');
}
});
const der = (dy: T) => {
const ders: {[key: string]: () => Tensor} = {};
$tensors.forEach((t, i) => {
ders[i] = () => dy.clone();
});
return ders;
};
const inputs: NamedTensorMap = $tensors as {} as NamedTensorMap;
return ENV.engine.runKernel(backend => backend.addN($tensors), inputs, der);
}
/**
* Adds two `tf.Tensor`s element-wise, A + B.
*
* Inputs must be the same shape. For broadcasting support, use add() instead.
*
* @param a The first Tensor to add element-wise.
* @param b The second Tensor to add element-wise.
*/
function addStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'addStrict');
const $b = convertToTensor(b, 'b', 'addStrict');
util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');
return $a.add($b);
}
/**
* Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
*
* We also expose `tf.subStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([10, 20, 30, 40]);
* const b = tf.tensor1d([1, 2, 3, 4]);
*
* a.sub(b).print(); // or tf.sub(a, b)
* ```
*
* ```js
* // Broadcast subtract a with b.
* const a = tf.tensor1d([10, 20, 30, 40]);
* const b = tf.scalar(5);
*
* a.sub(b).print(); // or tf.sub(a, b)
* ```
* @param a The first `tf.Tensor` to subtract from.
* @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
* `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'sub');
let $b = convertToTensor(b, 'b', 'sub');
[$a, $b] = makeTypesMatch($a, $b);
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
let res = dy;
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($a.shape);
};
const derB = () => {
let res = dy;
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.neg().reshape($b.shape);
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(
backend => backend.subtract($a, $b), {$a, $b}, der) as T;
}
/**
* Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must
* be the same shape.
*
* For broadcasting support, use `tf.sub` instead.
*
* @param a The first Tensor to subtract element-wise.
* @param b The second Tensor to subtract element-wise.
*/
function subStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'subStrict');
const $b = convertToTensor(b, 'b', 'subStrict');
util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');
return $a.sub($b);
}
/**
* Computes the power of one `tf.Tensor` to another. Supports broadcasting.
*
* Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
* corresponding elements in x and y. The result's dtype will be the upcasted
* type of the `base` and `exp` dtypes.
*
* ```js
* const a = tf.tensor([[2, 3], [4, 5]])
* const b = tf.tensor([[1, 2], [3, 0]]).toInt();
*
* a.pow(b).print(); // or tf.pow(a, b)
* ```
*
* ```js
* const a = tf.tensor([[1, 2], [3, 4]])
* const b = tf.tensor(2).toInt();
*
* a.pow(b).print(); // or tf.pow(a, b)
* ```
* We also expose `powStrict` which has the same signature as this op and
* asserts that `base` and `exp` are the same shape (does not broadcast).
*
* @param base The base `tf.Tensor` to pow element-wise.
* @param exp The exponent `tf.Tensor` to pow element-wise.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function pow_<T extends Tensor>(base: T|TensorLike, exp: Tensor|TensorLike): T {
const $base = convertToTensor(base, 'base', 'pow');
const $exp = convertToTensor(exp, 'exp', 'pow');
const outShape =
broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);
base = $base.cast(upcastType($base.dtype, $exp.dtype));
exp = $exp.cast(upcastType($base.dtype, $exp.dtype));
const grad = (dy: Tensor, saved: Tensor[]) => {
const [y] = saved;
const derBase = () => {
const expFloat = $exp.toFloat();
let res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));
const reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($base.shape) as T;
};
const derExp = () => {
const condition = $base.greater(0);
const logBase = $base.log().where(condition, zerosLike($base));
let res = dy.mul(y.mul(logBase));
const reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($exp.shape);
};
return {$base: derBase, $exp: derExp};
};
return ENV.engine.runKernel(
(backend, save) => save(backend.pow($base, $exp)), {$base, $exp},
grad) as T;
}
/**
* Computes the power of one `tf.Tensor` to another. Inputs must
* be the same shape.
*
* For broadcasting support, use `tf.pow` instead.
*
* @param base The base tensor to pow element-wise.
* @param exp The exponent tensor to pow element-wise.
*/
function powStrict_<T extends Tensor>(base: T, exp: Tensor): T {
util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');
return base.pow(exp);
}
/**
* Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
*
* We also expose `tf.mulStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([1, 2, 3, 4]);
* const b = tf.tensor1d([2, 3, 4, 5]);
*
* a.mul(b).print(); // or tf.mul(a, b)
* ```
*
* ```js
* // Broadcast mul a with b.
* const a = tf.tensor1d([1, 2, 3, 4]);
* const b = tf.scalar(5);
*
* a.mul(b).print(); // or tf.mul(a, b)
* ```
* @param a The first tensor to multiply.
* @param b The second tensor to multiply. Must have the same dtype as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'mul');
let $b = convertToTensor(b, 'b', 'mul');
[$a, $b] = makeTypesMatch($a, $b);
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
const res = dy.mul($b.toFloat());
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
return res.sum(reduceAxes).reshape($a.shape);
}
return res;
};
const derB = () => {
const res = dy.mul($a.toFloat());
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
return res.sum(reduceAxes).reshape($b.shape);
}
return res;
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(
backend => backend.multiply($a, $b), {$a, $b}, der) as T;
}
/**
* Multiplies two `tf.Tensor`s element-wise, A * B.
*
* Inputs must be the same shape. For broadcasting support, use `tf.mul`.
*
* @param a The first tensor to multiply.
* @param b The first tensor to multiply. Must have the same
* dtype as `a`.
*/
function mulStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'mul');
const $b = convertToTensor(b, 'b', 'mul');
util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');
return $a.mul($b) as T;
}
/**
* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
*
* We also expose `tf.divStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([1, 4, 9, 16]);
* const b = tf.tensor1d([1, 2, 3, 4]);
*
* a.div(b).print(); // or tf.div(a, b)
* ```
*
* ```js
* // Broadcast div a with b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(2);
*
* a.div(b).print(); // or tf.div(a, b)
* ```
*
* @param a The first tensor as the numerator.
* @param b The second tensor as the denominator. Must have the same dtype as
* `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'div');
let $b = convertToTensor(b, 'b', 'div');
[$a, $b] = makeTypesMatch($a, $b);
let forwardFunc: (backend: KernelBackend) => Tensor;
if ($a.dtype === 'int32' && $b.dtype === 'int32') {
return floorDiv($a, $b);
} else {
forwardFunc = (backend: KernelBackend) => backend.realDivide($a, $b);
}
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
const res = dy.div($b.toFloat());
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
return res.sum(reduceAxes).reshape($a.shape);
}
return res;
};
const derB = () => {
let res = dy.mul($a.toFloat());
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes).reshape($b.shape);
}
const tmp = $b.square() as Tensor;
return res.div(tmp.toFloat()).neg() as Tensor;
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;
}
/**
* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
* The result is rounded with floor function.
*
*
* ```js
* const a = tf.tensor1d([1, 4, 9, 16]);
* const b = tf.tensor1d([1, 2, 3, 4]);
*
* a.floorDiv(b).print(); // or tf.div(a, b)
* ```
*
* ```js
* // Broadcast div a with b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(2);
*
* a.floorDiv(b).print(); // or tf.floorDiv(a, b)
* ```
*
* @param a The first tensor as the numerator.
* @param b The second tensor as the denominator. Must have the same dtype as
* `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function floorDiv_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'floorDiv');
let $b = convertToTensor(b, 'b', 'floorDiv');
[$a, $b] = makeTypesMatch($a, $b);
const forwardFunc = (backend: KernelBackend) => backend.floorDiv($a, $b);
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
const res = dy.div($b.toFloat());
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
return res.sum(reduceAxes).reshape($a.shape);
}
return res;
};
const derB = () => {
let res = dy.mul($a.toFloat());
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes).reshape($b.shape);
}
const tmp = $b.square() as Tensor;
return res.div(tmp.toFloat()).neg() as Tensor;
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;
}
/**
* Divides two `tf.Tensor`s element-wise, A / B. Inputs must
* be the same shape.
*
* @param a The first tensor as the numerator for element-wise division.
* @param b The second tensor as the denominator for element-wise division.
*/
function divStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'div');
const $b = convertToTensor(b, 'b', 'div');
util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');
return $a.div($b) as T;
}
/**
* Returns the mod of a and b element-wise.
* `floor(x / y) * y + mod(x, y) = x`
* Supports broadcasting.
*
* We also expose `tf.modStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([1, 4, 3, 16]);
* const b = tf.tensor1d([1, 2, 9, 4]);
*
* a.mod(b).print(); // or tf.mod(a, b)
* ```
*
* ```js
* // Broadcast a mod b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(5);
*
* a.mod(b).print(); // or tf.mod(a, b)
* ```
*
* @param a The first tensor.
* @param b The second tensor. Must have the same type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'mod');
let $b = convertToTensor(b, 'b', 'mod');
[$a, $b] = makeTypesMatch($a, $b);
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
return dy.sum(reduceAxes).reshape($a.shape);
}
return dy;
};
const derB = () => {
const res = dy.mul($a.div($b).floor().neg());
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
return res.sum(reduceAxes).reshape($b.shape);
}
return res;
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(backend => backend.mod($a, $b), {$a, $b}, der) as
T;
}
/**
* Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must
* be the same shape. For broadcasting support, use mod().
*
* @param a The first tensor.
* @param b The second tensor. Must have the same dtype as `a`.
*/
function modStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'modStrict');
const $b = convertToTensor(b, 'b', 'modStrict');
util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');
return $a.mod($b);
}
/**
* Returns the min of a and b (`a < b ? a : b`) element-wise.
* Supports broadcasting.
*
* We also expose `minimumStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([1, 4, 3, 16]);
* const b = tf.tensor1d([1, 2, 9, 4]);
*
* a.minimum(b).print(); // or tf.minimum(a, b)
* ```
*
* ```js
* // Broadcast minimum a with b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(5);
*
* a.minimum(b).print(); // or tf.minimum(a, b)
* ```
*
* @param a The first tensor.
* @param b The second tensor. Must have the same type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function minimum_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'minimum');
let $b = convertToTensor(b, 'b', 'minimum');
[$a, $b] = makeTypesMatch($a, $b);
if ($a.dtype === 'bool') {
$a = $a.toInt();
$b = $b.toInt();
}
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => dy.mul($a.lessEqual($b).toFloat());
const derB = () => dy.mul($a.greater($b).toFloat());
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(
backend => backend.minimum($a, $b), {$a, $b}, der) as T;
}
/**
* Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must
* be the same shape. For broadcasting support, use minimum().
*
* @param a The first tensor.
* @param b The second tensor. Must have the same dtype as `a`.
*/
function minimumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'minimumStrict');
const $b = convertToTensor(b, 'b', 'minimumStrict');
util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');
return $a.minimum($b);
}
/**
* Returns the max of a and b (`a > b ? a : b`) element-wise.
* Supports broadcasting.
*
* We also expose `tf.maximumStrict` which has the same signature as this op and
* asserts that `a` and `b` are the same shape (does not broadcast).
*
* ```js
* const a = tf.tensor1d([1, 4, 3, 16]);
* const b = tf.tensor1d([1, 2, 9, 4]);
*
* a.maximum(b).print(); // or tf.maximum(a, b)
* ```
*
* ```js
* // Broadcast maximum a with b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(5);
*
* a.maximum(b).print(); // or tf.maximum(a, b)
* ```
*
* @param a The first tensor.
* @param b The second tensor. Must have the same type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function maximum_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'maximum');
let $b = convertToTensor(b, 'b', 'maximum');
[$a, $b] = makeTypesMatch($a, $b);
if ($a.dtype === 'bool') {
$a = $a.toInt();
$b = $b.toInt();
}
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => dy.mul($a.greaterEqual($b).toFloat());
const derB = () => dy.mul($a.less($b).toFloat());
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(
backend => backend.maximum($a, $b), {$a, $b}, der) as T;
}
/**
* Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must
* be the same shape. For broadcasting support, use maximum().
*
* @param a The first tensor.
* @param b The second tensor. Must have the same dtype as `a`.
*/
function maximumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'maximumStrict');
const $b = convertToTensor(b, 'b', 'maximumStrict');
util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');
return $a.maximum($b);
}
/**
* Returns (a - b) * (a - b) element-wise.
* Supports broadcasting.
*
* We also expose `tf.squaredDifferenceStrict` which has the same signature as
* this op and asserts that `a` and `b` are the same shape (does not
* broadcast).
*
* ```js
* const a = tf.tensor1d([1, 4, 3, 16]);
* const b = tf.tensor1d([1, 2, 9, 4]);
*
* a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)
* ```
*
* ```js
* // Broadcast squared difference a with b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(5);
*
* a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)
* ```
*
* @param a The first tensor.
* @param b The second tensor. Must have the same type as `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function squaredDifference_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'squaredDifference');
let $b = convertToTensor(b, 'b', 'squaredDifference');
[$a, $b] = makeTypesMatch($a, $b);
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const two = scalar(2);
const derA = () => dy.mul($a.sub($b).mul(two));
const derB = () => dy.mul($b.sub($a).mul(two));
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(
backend => backend.squaredDifference($a, $b), {$a, $b}, der) as T;
}
/**
* Returns (a - b) * (a - b) element-wise.
*
* Inputs must be the same shape. For broadcasting support, use
* `tf.squaredDifference` instead.
*
* @param a The first tensor.
* @param b The second tensor. Must have the same type as `a`.
*/
function squaredDifferenceStrict_<T extends Tensor>(
a: T|TensorLike, b: T|TensorLike): T {
const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');
const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');
util.assertShapesMatch(
$a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');
return $a.squaredDifference($b);
}
/**
* Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
* Supports broadcasting.
*
* ```js
* const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
* const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
*
* tf.atan2(a, b).print()
* ```
*
* @param a The first tensor.
* @param b The second tensor. Must have the same dtype as `a`.
*
*/
/** @doc {heading: 'Operations', subheading: 'Basic math'} */
function atan2_<T extends Tensor>(
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
let $a = convertToTensor(a, 'a', 'atan2');
let $b = convertToTensor(b, 'b', 'atan2');
[$a, $b] = makeTypesMatch($a, $b);
const outShape =
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
const der = (dy: Tensor) => {
const derA = () => {
const d = add($a.square(), $b.square());
let res = dy.mul($b.div(d));
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($a.shape);
};
const derB = () => {
const d = add($a.square(), $b.square()) as T;
let res = neg(dy.mul($a.div(d)));
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
if (reduceAxes.length > 0) {
res = res.sum(reduceAxes);
}
return res.reshape($b.shape);
};
return {$a: derA, $b: derB};
};
return ENV.engine.runKernel(
backend => backend.atan2($a, $b), {$a, $b}, der) as T;
}
export const add = op({add_});
export const addN = op({addN_});
export const addStrict = op({addStrict_});
export const atan2 = op({atan2_});
export const div = op({div_});
export const divStrict = op({divStrict_});
export const floorDiv = op({floorDiv_});
export const maximum = op({maximum_});
export const maximumStrict = op({maximumStrict_});
export const minimum = op({minimum_});
export const minimumStrict = op({minimumStrict_});
export const mod = op({mod_});
export const modStrict = op({modStrict_});
export const mul = op({mul_});
export const mulStrict = op({mulStrict_});
export const pow = op({pow_});
export const powStrict = op({powStrict_});
export const squaredDifference = op({squaredDifference_});
export const squaredDifferenceStrict = op({squaredDifferenceStrict_});
export const sub = op({sub_});
export const subStrict = op({subStrict_});