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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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/** * @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_ }); //# sourceMappingURL=mat_mul.js.map