| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2017 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 } from '../test_util';
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| 20 | describeWithFlags('exp', ALL_ENVS, () => {
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| 21 | it('exp', async () => {
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| 22 | const a = tf.tensor1d([1, 2, 0]);
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| 23 | const r = tf.exp(a);
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| 24 | expectArraysClose(await r.data(), [Math.exp(1), Math.exp(2), 1]);
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| 25 | });
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| 26 | it('exp propagates NaNs', async () => {
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| 27 | const a = tf.tensor1d([1, NaN, 0]);
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| 28 | const r = tf.exp(a);
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| 29 | expectArraysClose(await r.data(), [Math.exp(1), NaN, 1]);
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| 30 | });
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| 31 | it('gradients: Scalar', async () => {
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| 32 | const a = tf.scalar(0.5);
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| 33 | const dy = tf.scalar(3);
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| 34 | const gradients = tf.grad(a => tf.exp(a))(a, dy);
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| 35 | expect(gradients.shape).toEqual(a.shape);
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| 36 | expect(gradients.dtype).toEqual('float32');
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| 37 | expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);
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| 38 | });
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| 39 | it('gradient with clones', async () => {
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| 40 | const a = tf.scalar(0.5);
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| 41 | const dy = tf.scalar(3);
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| 42 | const gradients = tf.grad(a => tf.exp(a.clone()).clone())(a, dy);
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| 43 | expect(gradients.shape).toEqual(a.shape);
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| 44 | expect(gradients.dtype).toEqual('float32');
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| 45 | expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);
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| 46 | });
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| 47 | it('gradients: Tensor1D', async () => {
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| 48 | const a = tf.tensor1d([-1, 2, 3, -5]);
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| 49 | const dy = tf.tensor1d([1, 2, 3, 4]);
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| 50 | const gradients = tf.grad(a => tf.exp(a))(a, dy);
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| 51 | expect(gradients.shape).toEqual(a.shape);
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| 52 | expect(gradients.dtype).toEqual('float32');
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| 53 | expectArraysClose(await gradients.data(), [1 * Math.exp(-1), 2 * Math.exp(2), 3 * Math.exp(3), 4 * Math.exp(-5)], 1e-1);
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| 54 | });
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| 55 | it('gradients: Tensor2D', async () => {
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| 56 | const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
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| 57 | const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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| 58 | const gradients = tf.grad(a => tf.exp(a))(a, dy);
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| 59 | expect(gradients.shape).toEqual(a.shape);
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| 60 | expect(gradients.dtype).toEqual('float32');
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| 61 | expectArraysClose(await gradients.data(), [1 * Math.exp(-3), 2 * Math.exp(1), 3 * Math.exp(2), 4 * Math.exp(3)], 1e-1);
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| 62 | });
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| 63 | it('throws when passed a non-tensor', () => {
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| 64 | expect(() => tf.exp({}))
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| 65 | .toThrowError(/Argument 'x' passed to 'exp' must be a Tensor/);
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| 66 | });
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| 67 | it('accepts a tensor-like object', async () => {
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| 68 | const r = tf.exp([1, 2, 0]);
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| 69 | expectArraysClose(await r.data(), [Math.exp(1), Math.exp(2), 1]);
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| 70 | });
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| 71 | it('throws for string tensor', () => {
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| 72 | expect(() => tf.exp('q'))
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| 73 | .toThrowError(/Argument 'x' passed to 'exp' must be numeric/);
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| 74 | });
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| 75 | });
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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 {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('exp', ALL_ENVS, () => {\n  it('exp', async () => {\n    const a = tf.tensor1d([1, 2, 0]);\n    const r = tf.exp(a);\n\n    expectArraysClose(await r.data(), [Math.exp(1), Math.exp(2), 1]);\n  });\n\n  it('exp propagates NaNs', async () => {\n    const a = tf.tensor1d([1, NaN, 0]);\n    const r = tf.exp(a);\n    expectArraysClose(await r.data(), [Math.exp(1), NaN, 1]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(0.5);\n    const dy = tf.scalar(3);\n\n    const gradients = tf.grad(a => tf.exp(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(0.5);\n    const dy = tf.scalar(3);\n\n    const gradients = tf.grad(a => tf.exp(a.clone()).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [3 * Math.exp(0.5)]);\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.exp(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [1 * Math.exp(-1), 2 * Math.exp(2), 3 * Math.exp(3), 4 * Math.exp(-5)],\n        1e-1);\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.exp(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [1 * Math.exp(-3), 2 * Math.exp(1), 3 * Math.exp(2), 4 * Math.exp(3)],\n        1e-1);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.exp({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'exp' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const r = tf.exp([1, 2, 0]);\n    expectArraysClose(await r.data(), [Math.exp(1), Math.exp(2), 1]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.exp('q'))\n        .toThrowError(/Argument 'x' passed to 'exp' must be numeric/);\n  });\n});\n"]} |
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