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';
describeWithFlags('sigmoid', ALL_ENVS, () => {
    it('basic', async () => {
        const values = [1, -3, 2, 7, -4];
        const a = tf.tensor1d(values);
        const result = tf.sigmoid(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = 1 / (1 + Math.exp(-values[i]));
        }
        expectArraysClose(await result.data(), expected);
    });
    it('6D', async () => {
        const a = tf.ones([2, 2, 2, 2, 2, 2]);
        const result = tf.sigmoid(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = 1 / (1 + Math.exp(-1.0));
        }
        expectArraysClose(await result.data(), expected);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor1d([3, NaN]);
        const res = tf.sigmoid(a);
        expectArraysClose(await res.data(), [1 / (1 + Math.exp(-3)), NaN]);
    });
    it('gradients: Tensor1D', async () => {
        const a = tf.tensor1d([1, 2, -3, 5]);
        const dy = tf.tensor1d([1, 2, 3, 4]);
        const da = tf.grad(a => tf.sigmoid(a))(a, dy);
        const aVals = await a.array();
        const dyVals = await dy.array();
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            const y = 1 / (1 + Math.exp(-aVals[i]));
            expected[i] = dyVals[i] * y * (1 - y);
        }
        expectArraysClose(await da.data(), expected);
    });
    it('gradient with clones', async () => {
        const a = tf.tensor1d([1, 2, -3, 5]);
        const dy = tf.tensor1d([1, 2, 3, 4]);
        const da = tf.grad(a => tf.sigmoid(a.clone()).clone())(a, dy);
        const aVals = await a.array();
        const dyVals = await dy.array();
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            const y = 1 / (1 + Math.exp(-aVals[i]));
            expected[i] = dyVals[i] * y * (1 - y);
        }
        expectArraysClose(await da.data(), expected);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.sigmoid({}))
            .toThrowError(/Argument 'x' passed to 'sigmoid' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const values = [1, -3, 2, 7, -4];
        const result = tf.sigmoid(values);
        const expected = [];
        for (let i = 0; i < values.length; i++) {
            expected[i] = 1 / (1 + Math.exp(-values[i]));
        }
        expectArraysClose(await result.data(), expected);
    });
    it('throws for string tensor', () => {
        expect(() => tf.sigmoid('q'))
            .toThrowError(/Argument 'x' passed to 'sigmoid' must be float32/);
    });
    it('throws for int32 tensor', () => {
        expect(() => tf.sigmoid(tf.tensor1d([1], 'int32')))
            .toThrowError(/Argument 'x' passed to 'sigmoid' must be float32/);
    });
});
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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('sigmoid', ALL_ENVS, () => {\n  it('basic', async () => {\n    const values = [1, -3, 2, 7, -4];\n    const a = tf.tensor1d(values);\n\n    const result = tf.sigmoid(a);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = 1 / (1 + Math.exp(-values[i]));\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('6D', async () => {\n    const a = tf.ones([2, 2, 2, 2, 2, 2]);\n    const result = tf.sigmoid(a);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = 1 / (1 + Math.exp(-1.0));\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('propagates NaNs', async () => {\n    const a = tf.tensor1d([3, NaN]);\n    const res = tf.sigmoid(a);\n    expectArraysClose(await res.data(), [1 / (1 + Math.exp(-3)), NaN]);\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 da = tf.grad(a => tf.sigmoid(a))(a, dy);\n\n    const aVals = await a.array();\n    const dyVals = await dy.array();\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      const y = 1 / (1 + Math.exp(-aVals[i]));\n      expected[i] = dyVals[i] * y * (1 - y);\n    }\n\n    expectArraysClose(await da.data(), expected);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.tensor1d([1, 2, -3, 5]);\n    const dy = tf.tensor1d([1, 2, 3, 4]);\n\n    const da = tf.grad(a => tf.sigmoid(a.clone()).clone())(a, dy);\n\n    const aVals = await a.array();\n    const dyVals = await dy.array();\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      const y = 1 / (1 + Math.exp(-aVals[i]));\n      expected[i] = dyVals[i] * y * (1 - y);\n    }\n\n    expectArraysClose(await da.data(), expected);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.sigmoid({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'sigmoid' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const values = [1, -3, 2, 7, -4];\n    const result = tf.sigmoid(values);\n\n    const expected = [];\n    for (let i = 0; i < values.length; i++) {\n      expected[i] = 1 / (1 + Math.exp(-values[i]));\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.sigmoid('q'))\n        .toThrowError(/Argument 'x' passed to 'sigmoid' must be float32/);\n  });\n\n  it('throws for int32 tensor', () => {\n    expect(() => tf.sigmoid(tf.tensor1d([1], 'int32')))\n        .toThrowError(/Argument 'x' passed to 'sigmoid' must be float32/);\n  });\n});\n"]}