@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2018 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 * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('broadcastTo', ALL_ENVS, () => {
it('[] -> [3,2]', async () => {
const a = tf.scalar(4.2);
const A = tf.tensor2d([[4.2, 4.2], [4.2, 4.2], [4.2, 4.2]]);
expectArraysClose(await A.array(), await tf.broadcastTo(a, A.shape).array());
// test gradients
const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]), f = (a) => tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(), h = (a) => a.mul(w).mean().asScalar();
const df = tf.grad(f), dh = tf.grad(h);
expectArraysClose(await df(a).array(), await dh(a).array());
});
it('[2] -> [3,2]', async () => {
const a = tf.tensor1d([1, 2]);
const A = tf.tensor2d([[1, 2], [1, 2], [1, 2]]);
expectArraysClose(await A.array(), await tf.broadcastTo(a, A.shape).array());
// test gradients
const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]), f = (a) => tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(), h = (a) => a.mul(w).mean().asScalar();
const df = tf.grad(f), dh = tf.grad(h);
expectArraysClose(await df(a).array(), await dh(a).array());
});
it('[3,1] -> [3,2]', async () => {
const a = tf.tensor2d([[1], [2], [3]]);
const A = tf.tensor2d([[1, 1], [2, 2], [3, 3]]);
expectArraysClose(await A.array(), await tf.broadcastTo(a, A.shape).array());
// test gradients
const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]), f = (a) => tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(), h = (a) => a.mul(w).mean().asScalar();
const df = tf.grad(f), dh = tf.grad(h);
expectArraysClose(await df(a).array(), await dh(a).array());
});
});
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