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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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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 * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
import { backend } from '../index';
describeWithFlags('leakyrelu', ALL_ENVS, () => {
    it('basic', async () => {
        const a = tf.tensor1d([0, 1, -2]);
        const result = tf.leakyRelu(a);
        expect(result.shape).toEqual(a.shape);
        expectArraysClose(await result.data(), [0, 1, -0.4]);
    });
    it('int32', async () => {
        if (backend() && backend().floatPrecision() === 32) {
            // TODO: Use skip() instead when it is implemented
            const a = tf.tensor1d([0, 1, -2], 'int32');
            const result = tf.leakyRelu(a);
            expect(result.shape).toEqual(a.shape);
            expect(result.dtype).toEqual('float32');
            expectArraysClose(await result.data(), [0, 1, -0.4]);
        }
    });
    it('propagates NaN', async () => {
        const a = tf.tensor1d([0, 1, NaN]);
        const result = tf.leakyRelu(a);
        expect(result.shape).toEqual(a.shape);
        expectArraysClose(await result.data(), [0, 1, NaN]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(-4);
        const dy = tf.scalar(8);
        const alpha = 0.1;
        const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * alpha]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(-4);
        const dy = tf.scalar(8);
        const alpha = 0.1;
        const gradients = tf.grad((a) => tf.leakyRelu(a.clone(), alpha).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * alpha]);
    });
    it('gradients: Tensor1D', async () => {
        const aValues = [1, -1, 0.1];
        const dyValues = [1, 2, 3];
        const alpha = 0.1;
        const a = tf.tensor1d(aValues);
        const dy = tf.tensor1d(dyValues);
        const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [1, 2 * alpha, 3]);
    });
    it('gradients: Tensor2D', async () => {
        const aValues = [1, -1, 0.1, 0.5];
        const dyValues = [1, 2, 3, 4];
        const alpha = 0.1;
        const a = tf.tensor2d(aValues, [2, 2]);
        const dy = tf.tensor2d(dyValues, [2, 2]);
        const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [1, 2 * alpha, 3, 4]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.leakyRelu({}))
            .toThrowError(/Argument 'x' passed to 'leakyRelu' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.leakyRelu([0, 1, -2]);
        expect(result.shape).toEqual([3]);
        expectArraysClose(await result.data(), [0, 1, -0.4]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.leakyRelu('q'))
            .toThrowError(/Argument 'x' passed to 'leakyRelu' must be numeric/);
    });
});
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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} from '../test_util';\nimport {backend} from '../index';\n\ndescribeWithFlags('leakyrelu', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.tensor1d([0, 1, -2]);\n    const result = tf.leakyRelu(a);\n\n    expect(result.shape).toEqual(a.shape);\n    expectArraysClose(await result.data(), [0, 1, -0.4]);\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([0, 1, -2], 'int32');\n      const result = tf.leakyRelu(a);\n\n      expect(result.shape).toEqual(a.shape);\n      expect(result.dtype).toEqual('float32');\n      expectArraysClose(await result.data(), [0, 1, -0.4]);\n    }\n  });\n\n  it('propagates NaN', async () => {\n    const a = tf.tensor1d([0, 1, NaN]);\n    const result = tf.leakyRelu(a);\n\n    expect(result.shape).toEqual(a.shape);\n    expectArraysClose(await result.data(), [0, 1, NaN]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(-4);\n    const dy = tf.scalar(8);\n    const alpha = 0.1;\n\n    const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [8 * alpha]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(-4);\n    const dy = tf.scalar(8);\n    const alpha = 0.1;\n\n    const gradients =\n        tf.grad((a) => tf.leakyRelu(a.clone(), alpha).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [8 * alpha]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const aValues = [1, -1, 0.1];\n    const dyValues = [1, 2, 3];\n    const alpha = 0.1;\n\n    const a = tf.tensor1d(aValues);\n    const dy = tf.tensor1d(dyValues);\n\n    const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n\n    expectArraysClose(await gradients.data(), [1, 2 * alpha, 3]);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const aValues = [1, -1, 0.1, 0.5];\n    const dyValues = [1, 2, 3, 4];\n    const alpha = 0.1;\n\n    const a = tf.tensor2d(aValues, [2, 2]);\n    const dy = tf.tensor2d(dyValues, [2, 2]);\n\n    const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n\n    expectArraysClose(await gradients.data(), [1, 2 * alpha, 3, 4]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.leakyRelu({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'leakyRelu' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const result = tf.leakyRelu([0, 1, -2]);\n\n    expect(result.shape).toEqual([3]);\n    expectArraysClose(await result.data(), [0, 1, -0.4]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.leakyRelu('q'))\n        .toThrowError(/Argument 'x' passed to 'leakyRelu' must be numeric/);\n  });\n});\n"]}