@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. 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 { ENGINE } from '../engine';
import { BatchMatMul } from '../kernel_names';
import { makeTypesMatch } from '../tensor_util';
import { convertToTensor } from '../tensor_util_env';
import { op } from './operation';
/**
* Computes the dot product of two matrices, A * B. These must be matrices.
*
* ```js
* const a = tf.tensor2d([1, 2], [1, 2]);
* const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
*
* a.matMul(b).print(); // or tf.matMul(a, b)
* ```
* @param a First matrix in dot product operation.
* @param b Second matrix in dot product operation.
* @param transposeA If true, `a` is transposed before multiplication.
* @param transposeB If true, `b` is transposed before multiplication.
*
* @doc {heading: 'Operations', subheading: 'Matrices'}
*/
function matMul_(a, b, transposeA = false, transposeB = false) {
let $a = convertToTensor(a, 'a', 'matMul');
let $b = convertToTensor(b, 'b', 'matMul');
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
const attrs = { transposeA, transposeB };
return ENGINE.runKernel(BatchMatMul, inputs, attrs);
}
export const matMul = op({ matMul_ });
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