@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());
});
it('should throw error when shape is not integer', () => {
const a = tf.scalar(4.2);
expect(() => tf.broadcastTo(a, [2, 2.22, 3.33])).toThrow();
});
});
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {Tensor} from '../tensor';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('broadcastTo', ALL_ENVS, () => {\n  it('[] -> [3,2]', async () => {\n    const a = tf.scalar(4.2);\n    const A = tf.tensor2d([[4.2, 4.2], [4.2, 4.2], [4.2, 4.2]]);\n\n    expectArraysClose(\n        await A.array(), await tf.broadcastTo(a, A.shape).array());\n\n    // test gradients\n    const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]),\n          f = (a: Tensor) =>\n              tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(),\n          h = (a: Tensor) => a.mul(w).mean().asScalar();\n\n    const df = tf.grad(f), dh = tf.grad(h);\n\n    expectArraysClose(await df(a).array(), await dh(a).array());\n  });\n\n  it('[2] -> [3,2]', async () => {\n    const a = tf.tensor1d([1, 2]);\n    const A = tf.tensor2d([[1, 2], [1, 2], [1, 2]]);\n    expectArraysClose(\n        await A.array(), await tf.broadcastTo(a, A.shape).array());\n\n    // test gradients\n    const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]),\n          f = (a: Tensor) =>\n              tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(),\n          h = (a: Tensor) => a.mul(w).mean().asScalar();\n\n    const df = tf.grad(f), dh = tf.grad(h);\n\n    expectArraysClose(await df(a).array(), await dh(a).array());\n  });\n\n  it('[3,1] -> [3,2]', async () => {\n    const a = tf.tensor2d([[1], [2], [3]]);\n    const A = tf.tensor2d([[1, 1], [2, 2], [3, 3]]);\n\n    expectArraysClose(\n        await A.array(), await tf.broadcastTo(a, A.shape).array());\n\n    // test gradients\n    const w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]),\n          f = (a: Tensor) =>\n              tf.broadcastTo(a, A.shape).mul(w).mean().asScalar(),\n          h = (a: Tensor) => a.mul(w).mean().asScalar();\n\n    const df = tf.grad(f), dh = tf.grad(h);\n\n    expectArraysClose(await df(a).array(), await dh(a).array());\n  });\n\n  it('should throw error when shape is not integer', () => {\n    const a = tf.scalar(4.2);\n    expect(() => tf.broadcastTo(a, [2, 2.22, 3.33])).toThrow();\n  });\n});\n"]}