| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2020 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | import * as tf from '../index';
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| 18 | import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
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| 19 | import { expectArraysClose, expectNumbersClose } from '../test_util';
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| 20 | import { backend } from '../index';
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| 21 | describeWithFlags('square', ALL_ENVS, () => {
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| 22 | it('1D array', async () => {
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| 23 | const a = tf.tensor1d([2, 4, Math.sqrt(2)]);
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| 24 | const r = tf.square(a);
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| 25 | expectArraysClose(await r.data(), [4, 16, 2]);
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| 26 | });
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| 27 | it('2D array', async () => {
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| 28 | const a = tf.tensor2d([1, 2, Math.sqrt(2), Math.sqrt(3)], [2, 2]);
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| 29 | const r = tf.square(a);
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| 30 | expect(r.shape).toEqual([2, 2]);
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| 31 | expectArraysClose(await r.data(), [1, 4, 2, 3]);
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| 32 | });
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| 33 | it('5D array', async () => {
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| 34 | const a = tf.tensor5d([1, 2, Math.sqrt(2), Math.sqrt(3)], [1, 1, 2, 2, 1]);
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| 35 | const r = tf.square(a);
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| 36 | expect(r.shape).toEqual([1, 1, 2, 2, 1]);
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| 37 | expectArraysClose(await r.data(), [1, 4, 2, 3]);
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| 38 | });
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| 39 | it('6D array', async () => {
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| 40 | const a = tf.tensor6d([1, 2, Math.sqrt(2), Math.sqrt(3), 3, 4, Math.sqrt(7), Math.sqrt(13)], [1, 1, 2, 2, 2, 1]);
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| 41 | const r = tf.square(a);
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| 42 | expect(r.shape).toEqual(a.shape);
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| 43 | expectArraysClose(await r.data(), [1, 4, 2, 3, 9, 16, 7, 13]);
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| 44 | });
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| 45 | it('square propagates NaNs', async () => {
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| 46 | const a = tf.tensor1d([1.5, NaN]);
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| 47 | const r = tf.square(a);
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| 48 | expectArraysClose(await r.data(), [2.25, NaN]);
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| 49 | });
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| 50 | it('int32', async () => {
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| 51 | if (backend() && backend().floatPrecision() === 32) {
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| 52 | // TODO: Use skip() instead when it is implemented
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| 53 | const a = tf.tensor1d([2, 4, 40000], 'int32');
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| 54 | const r = tf.square(a);
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| 55 | expect(r.dtype).toEqual('int32');
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| 56 | const data = await r.data();
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| 57 | expectNumbersClose(data[0], 4);
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| 58 | expectNumbersClose(data[1], 16);
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| 59 | // Epsilon must be larger here for webgl1
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| 60 | // TODO: Use expectArraysClose when it supports epsilons scaled by the
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| 61 | // numbers being compared.
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| 62 | expectNumbersClose(data[2], 1600000000, 1000 /* epsilon */);
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| 63 | }
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| 64 | });
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| 65 | it('gradients: Scalar', async () => {
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| 66 | const a = tf.scalar(5);
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| 67 | const dy = tf.scalar(8);
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| 68 | const gradients = tf.grad(a => tf.square(a))(a, dy);
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| 69 | expect(gradients.shape).toEqual(a.shape);
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| 70 | expect(gradients.dtype).toEqual('float32');
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| 71 | expectArraysClose(await gradients.data(), [2 * 5 * 8]);
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| 72 | });
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| 73 | it('gradients: Scalar', async () => {
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| 74 | const a = tf.scalar(5);
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| 75 | const dy = tf.scalar(8);
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| 76 | const gradients = tf.grad(a => tf.square(a.clone()).clone())(a, dy);
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| 77 | expect(gradients.shape).toEqual(a.shape);
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| 78 | expect(gradients.dtype).toEqual('float32');
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| 79 | expectArraysClose(await gradients.data(), [2 * 5 * 8]);
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| 80 | });
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| 81 | it('gradients: Tensor1D', async () => {
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| 82 | const a = tf.tensor1d([-1, 2, 3, -5]);
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| 83 | const dy = tf.tensor1d([1, 2, 3, 4]);
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| 84 | const gradients = tf.grad(a => tf.square(a))(a, dy);
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| 85 | expect(gradients.shape).toEqual(a.shape);
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| 86 | expect(gradients.dtype).toEqual('float32');
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| 87 | expectArraysClose(await gradients.data(), [-2, 4 * 2, 6 * 3, -10 * 4]);
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| 88 | });
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| 89 | it('gradients: Tensor2D', async () => {
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| 90 | const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
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| 91 | const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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| 92 | const gradients = tf.grad(a => tf.square(a))(a, dy);
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| 93 | expect(gradients.shape).toEqual(a.shape);
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| 94 | expect(gradients.dtype).toEqual('float32');
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| 95 | expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);
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| 96 | });
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| 97 | it('gradients: Tensor5D', async () => {
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| 98 | const a = tf.tensor5d([-3, 1, 2, 3], [1, 1, 1, 2, 2]);
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| 99 | const dy = tf.tensor5d([1, 2, 3, 4], [1, 1, 1, 2, 2]);
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| 100 | const gradients = tf.grad(a => tf.square(a))(a, dy);
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| 101 | expect(gradients.shape).toEqual(a.shape);
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| 102 | expect(gradients.dtype).toEqual('float32');
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| 103 | expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);
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| 104 | });
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| 105 | it('gradients: Tensor6D', async () => {
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| 106 | const a = tf.tensor6d([-3, 1, 2, 3, -4, 5, 12, 3], [1, 1, 1, 2, 2, 2]);
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| 107 | const dy = tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 1, 2, 2, 2]);
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| 108 | const gradients = tf.grad(a => tf.square(a))(a, dy);
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| 109 | expect(gradients.shape).toEqual(a.shape);
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| 110 | expect(gradients.dtype).toEqual('float32');
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| 111 | expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4, -8 * 5, 10 * 6, 24 * 7, 6 * 8]);
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| 112 | });
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| 113 | it('throws when passed a non-tensor', () => {
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| 114 | expect(() => tf.square({}))
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| 115 | .toThrowError(/Argument 'x' passed to 'square' must be a Tensor/);
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| 116 | });
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| 117 | it('accepts a tensor-like object', async () => {
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| 118 | const r = tf.square([2, 4, Math.sqrt(2)]);
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| 119 | expectArraysClose(await r.data(), [4, 16, 2]);
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| 120 | });
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| 121 | it('throws for string tensor', () => {
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| 122 | expect(() => tf.square('q'))
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| 123 | .toThrowError(/Argument 'x' passed to 'square' must be numeric/);
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| 124 | });
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| 125 | });
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| 126 | //# 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* @license\n * Copyright 2020 Google LLC. 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 {expectArraysClose, expectNumbersClose} from '../test_util';\nimport { backend } from '../index';\n\ndescribeWithFlags('square', ALL_ENVS, () => {\n  it('1D array', async () => {\n    const a = tf.tensor1d([2, 4, Math.sqrt(2)]);\n    const r = tf.square(a);\n    expectArraysClose(await r.data(), [4, 16, 2]);\n  });\n\n  it('2D array', async () => {\n    const a = tf.tensor2d([1, 2, Math.sqrt(2), Math.sqrt(3)], [2, 2]);\n    const r = tf.square(a);\n    expect(r.shape).toEqual([2, 2]);\n    expectArraysClose(await r.data(), [1, 4, 2, 3]);\n  });\n\n  it('5D array', async () => {\n    const a = tf.tensor5d([1, 2, Math.sqrt(2), Math.sqrt(3)], [1, 1, 2, 2, 1]);\n    const r = tf.square(a);\n    expect(r.shape).toEqual([1, 1, 2, 2, 1]);\n    expectArraysClose(await r.data(), [1, 4, 2, 3]);\n  });\n\n  it('6D array', async () => {\n    const a = tf.tensor6d(\n        [1, 2, Math.sqrt(2), Math.sqrt(3), 3, 4, Math.sqrt(7), Math.sqrt(13)],\n        [1, 1, 2, 2, 2, 1]);\n    const r = tf.square(a);\n    expect(r.shape).toEqual(a.shape);\n    expectArraysClose(await r.data(), [1, 4, 2, 3, 9, 16, 7, 13]);\n  });\n\n  it('square propagates NaNs', async () => {\n    const a = tf.tensor1d([1.5, NaN]);\n    const r = tf.square(a);\n    expectArraysClose(await r.data(), [2.25, NaN]);\n  });\n\n  it('int32', async () => {\n    if (backend() && backend().floatPrecision() === 32) {\n      // TODO: Use skip() instead when it is implemented\n      const a = tf.tensor1d([2, 4, 40000], 'int32');\n      const r = tf.square(a);\n      expect(r.dtype).toEqual('int32');\n      const data = await r.data();\n      expectNumbersClose(data[0], 4);\n      expectNumbersClose(data[1], 16);\n      // Epsilon must be larger here for webgl1\n      // TODO: Use expectArraysClose when it supports epsilons scaled by the\n      // numbers being compared.\n      expectNumbersClose(data[2], 1_600_000_000, 1_000 /* epsilon */);\n    }\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(5);\n    const dy = tf.scalar(8);\n\n    const gradients = tf.grad(a => tf.square(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [2 * 5 * 8]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(5);\n    const dy = tf.scalar(8);\n\n    const gradients = tf.grad(a => tf.square(a.clone()).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [2 * 5 * 8]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const a = tf.tensor1d([-1, 2, 3, -5]);\n    const dy = tf.tensor1d([1, 2, 3, 4]);\n\n    const gradients = tf.grad(a => tf.square(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [-2, 4 * 2, 6 * 3, -10 * 4]);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);\n    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n\n    const gradients = tf.grad(a => tf.square(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);\n  });\n\n  it('gradients: Tensor5D', async () => {\n    const a = tf.tensor5d([-3, 1, 2, 3], [1, 1, 1, 2, 2]);\n    const dy = tf.tensor5d([1, 2, 3, 4], [1, 1, 1, 2, 2]);\n\n    const gradients = tf.grad(a => tf.square(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);\n  });\n\n  it('gradients: Tensor6D', async () => {\n    const a = tf.tensor6d([-3, 1, 2, 3, -4, 5, 12, 3], [1, 1, 1, 2, 2, 2]);\n    const dy = tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 1, 2, 2, 2]);\n\n    const gradients = tf.grad(a => tf.square(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [-6 * 1, 2 * 2, 4 * 3, 6 * 4, -8 * 5, 10 * 6, 24 * 7, 6 * 8]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.square({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'square' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const r = tf.square([2, 4, Math.sqrt(2)]);\n    expectArraysClose(await r.data(), [4, 16, 2]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.square('q'))\n        .toThrowError(/Argument 'x' passed to 'square' must be numeric/);\n  });\n});\n"]} |
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