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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';
describeWithFlags('logSoftmax', ALL_ENVS, () => {
    it('regular test', async () => {
        const y = tf.logSoftmax(tf.tensor1d([2, 1, 3]));
        expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);
    });
    it('Huge difference', async () => {
        const y = tf.logSoftmax(tf.tensor1d([-1000, +1000]));
        expectArraysClose(await y.data(), [-2000, 0]);
    });
    it('Propagates NaNs', async () => {
        const a = tf.tensor1d([2, 1, NaN]);
        const y = tf.logSoftmax(a);
        expectArraysClose(await y.data(), [NaN, NaN, NaN]);
    });
    it('2D, axis=1', async () => {
        const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
        const expected = [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
        expect(y.rank).toBe(2);
        expectArraysClose(await y.data(), expected);
    });
    it('2D, implicit axis=1', async () => {
        const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
        const expected = [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
        expect(y.rank).toBe(2);
        expectArraysClose(await y.data(), expected);
    });
    it('1D gradient', async () => {
        const x = tf.tensor1d([1, 2, 10]);
        const dy = tf.tensor1d([1, 2, 3]);
        const dx = tf.grad((x) => x.logSoftmax())(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [0.9992599, 1.9979881, -2.9972477]);
    });
    it('2D, axis=0 throws error', () => {
        const f = () => {
            tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
        };
        expect(f).toThrowError();
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.logSoftmax({}))
            .toThrowError(/Argument 'logits' passed to 'logSoftmax' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const y = tf.logSoftmax([2, 1, 3]);
        expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);
    });
});
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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('logSoftmax', ALL_ENVS, () => {\n  it('regular test', async () => {\n    const y = tf.logSoftmax(tf.tensor1d([2, 1, 3]));\n\n    expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);\n  });\n\n  it('Huge difference', async () => {\n    const y = tf.logSoftmax(tf.tensor1d([-1000, +1000]));\n\n    expectArraysClose(await y.data(), [-2000, 0]);\n  });\n\n  it('Propagates NaNs', async () => {\n    const a = tf.tensor1d([2, 1, NaN]);\n    const y = tf.logSoftmax(a);\n    expectArraysClose(await y.data(), [NaN, NaN, NaN]);\n  });\n\n  it('2D, axis=1', async () => {\n    const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);\n    const expected =\n        [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];\n    expect(y.rank).toBe(2);\n    expectArraysClose(await y.data(), expected);\n  });\n\n  it('2D, implicit axis=1', async () => {\n    const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));\n    const expected =\n        [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];\n    expect(y.rank).toBe(2);\n    expectArraysClose(await y.data(), expected);\n  });\n\n  it('1D gradient', async () => {\n    const x = tf.tensor1d([1, 2, 10]);\n    const dy = tf.tensor1d([1, 2, 3]);\n    const dx = tf.grad((x) => x.logSoftmax())(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [0.9992599, 1.9979881, -2.9972477]);\n  });\n\n  it('2D, axis=0 throws error', () => {\n    const f = () => {\n      tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);\n    };\n    expect(f).toThrowError();\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.logSoftmax({} as tf.Tensor))\n        .toThrowError(\n            /Argument 'logits' passed to 'logSoftmax' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const y = tf.logSoftmax([2, 1, 3]);\n\n    expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);\n  });\n});\n"]}