gx

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 * as selu_util from './selu_util';
describeWithFlags('selu', ALL_ENVS, () => {
    const scaleAlpha = selu_util.SELU_SCALEALPHA;
    const scale = selu_util.SELU_SCALE;
    it('calculate selu', async () => {
        const a = tf.tensor1d([1, -1, 0]);
        const result = tf.selu(a);
        expect(result.shape).toEqual(a.shape);
        expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);
    });
    it('selu propagates NaN', async () => {
        const a = tf.tensor1d([1, NaN]);
        const result = tf.selu(a);
        expect(result.shape).toEqual(a.shape);
        expectArraysClose(await result.data(), [1.0507, NaN]);
    });
    it('gradients: Scalar', async () => {
        let aValue = 1;
        let dyValue = 1;
        let a = tf.scalar(aValue);
        let dy = tf.scalar(dyValue);
        let gradients = tf.grad(a => tf.selu(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [dyValue * scale]);
        aValue = -1;
        dyValue = 2;
        a = tf.scalar(aValue);
        dy = tf.scalar(dyValue);
        gradients = tf.grad(a => tf.selu(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [dyValue * scaleAlpha * Math.exp(aValue)]);
    });
    it('gradient with clones', async () => {
        const aValue = 1;
        const dyValue = 1;
        const a = tf.scalar(aValue);
        const dy = tf.scalar(dyValue);
        const gradients = tf.grad(a => tf.selu(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [dyValue * scale]);
    });
    it('gradients: Tensor1D', async () => {
        const aValues = [1, -1, 0];
        const dyValues = [1, 2, 3];
        const a = tf.tensor1d(aValues);
        const dy = tf.tensor1d(dyValues);
        const gradients = tf.grad(a => tf.selu(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            if (aValues[i] > 0) {
                expected[i] = dyValues[i] * scale;
            }
            else {
                expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
            }
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), expected);
    });
    it('gradients: Tensor2D', async () => {
        const aValues = [1, -1, 0, 0.5];
        const dyValues = [1, 2, 3, 4];
        const a = tf.tensor2d(aValues, [2, 2]);
        const dy = tf.tensor2d(dyValues, [2, 2]);
        const gradients = tf.grad(a => tf.selu(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            if (aValues[i] > 0) {
                expected[i] = dyValues[i] * scale;
            }
            else {
                expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
            }
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), expected);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.selu({}))
            .toThrowError(/Argument 'x' passed to 'selu' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.selu([1, -1, 0]);
        expect(result.shape).toEqual([3]);
        expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.selu('q'))
            .toThrowError(/Argument 'x' passed to 'selu' 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';\n\nimport * as selu_util from './selu_util';\n\ndescribeWithFlags('selu', ALL_ENVS, () => {\n  const scaleAlpha = selu_util.SELU_SCALEALPHA;\n  const scale = selu_util.SELU_SCALE;\n\n  it('calculate selu', async () => {\n    const a = tf.tensor1d([1, -1, 0]);\n    const result = tf.selu(a);\n\n    expect(result.shape).toEqual(a.shape);\n    expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);\n  });\n\n  it('selu propagates NaN', async () => {\n    const a = tf.tensor1d([1, NaN]);\n    const result = tf.selu(a);\n    expect(result.shape).toEqual(a.shape);\n    expectArraysClose(await result.data(), [1.0507, NaN]);\n  });\n\n  it('gradients: Scalar', async () => {\n    let aValue = 1;\n    let dyValue = 1;\n    let a = tf.scalar(aValue);\n    let dy = tf.scalar(dyValue);\n\n    let gradients = tf.grad(a => tf.selu(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [dyValue * scale]);\n\n    aValue = -1;\n    dyValue = 2;\n    a = tf.scalar(aValue);\n    dy = tf.scalar(dyValue);\n\n    gradients = tf.grad(a => tf.selu(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(), [dyValue * scaleAlpha * Math.exp(aValue)]);\n  });\n\n  it('gradient with clones', async () => {\n    const aValue = 1;\n    const dyValue = 1;\n    const a = tf.scalar(aValue);\n    const dy = tf.scalar(dyValue);\n\n    const gradients = tf.grad(a => tf.selu(a.clone()).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [dyValue * scale]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const aValues = [1, -1, 0];\n    const dyValues = [1, 2, 3];\n    const a = tf.tensor1d(aValues);\n    const dy = tf.tensor1d(dyValues);\n\n    const gradients = tf.grad(a => tf.selu(a))(a, dy);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      if (aValues[i] > 0) {\n        expected[i] = dyValues[i] * scale;\n      } else {\n        expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);\n      }\n    }\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), expected);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const aValues = [1, -1, 0, 0.5];\n    const dyValues = [1, 2, 3, 4];\n    const a = tf.tensor2d(aValues, [2, 2]);\n    const dy = tf.tensor2d(dyValues, [2, 2]);\n\n    const gradients = tf.grad(a => tf.selu(a))(a, dy);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      if (aValues[i] > 0) {\n        expected[i] = dyValues[i] * scale;\n      } else {\n        expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);\n      }\n    }\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), expected);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.selu({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'selu' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const result = tf.selu([1, -1, 0]);\n    expect(result.shape).toEqual([3]);\n    expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.selu('q'))\n        .toThrowError(/Argument 'x' passed to 'selu' must be numeric/);\n  });\n});\n"]}