@tensorflow/tfjs-core/dist/ops/log_sigmoid_test.js

/**
 * @license
 * Copyright 2020 Google Inc. 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('logSigmoid', ALL_ENVS, () => {
    it('basic', async () => {
        const values = [1, -3, 2, 7, -4];
        const a = tf.tensor1d(values);
        const result = tf.logSigmoid(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));
        }
        expectArraysClose(await result.data(), expected);
    });
    it('scalar', async () => {
        const a = tf.scalar(-2);
        const result = tf.logSigmoid(a);
        const expected = [Math.log(1 / (1 + Math.exp(2)))];
        expectArraysClose(await result.data(), expected);
    });
    it('tensor2D', async () => {
        const values = [1, 2, -3, 5];
        const a = tf.tensor2d(values, [2, 2]);
        const result = tf.logSigmoid(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));
        }
        expectArraysClose(await result.data(), expected);
    });
    it('larger magnitude negative inputs', async () => {
        const values = [-100, -200, -3000];
        const a = tf.tensor1d(values);
        const result = tf.logSigmoid(a);
        const expected = [-100, -200, -3000];
        expectArraysClose(await result.data(), expected);
    });
    it('larger magnitude positive inputs', async () => {
        const values = [100, 200, 3000, 50000];
        const a = tf.tensor1d(values);
        const result = tf.logSigmoid(a);
        const expected = [0, 0, 0, 0];
        expectArraysClose(await result.data(), expected);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor1d([3, NaN]);
        const res = tf.logSigmoid(a);
        expectArraysClose(await res.data(), [Math.log(1 / (1 + Math.exp(-3))), NaN]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(3);
        const dy = tf.scalar(4);
        const dyVal = await dy.array();
        const da = tf.grad(a => tf.logSigmoid(a))(a, dy);
        const aVal = await a.array();
        const y = 1 / (1 + Math.exp(aVal));
        expectArraysClose(await da.data(), [dyVal * y]);
    });
    it('gradients: Tensor1D', async () => {
        const a = tf.tensor1d([1, 2, -3, 5]);
        const aVals = await a.array();
        const dy = tf.tensor1d([1, 2, 3, 4]);
        const dyVals = await dy.array();
        const da = tf.grad(a => tf.logSigmoid(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            const y = 1 / (1 + Math.exp(aVals[i]));
            expected[i] = dyVals[i] * y;
        }
        expectArraysClose(await da.data(), expected);
    });
    it('gradient with clones', async () => {
        const a = tf.tensor1d([1, 2, -3, 5]);
        const aVals = await a.array();
        const dy = tf.tensor1d([1, 2, 3, 4]);
        const dyVals = await dy.array();
        const da = tf.grad(a => tf.logSigmoid(a.clone()).clone())(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            const y = 1 / (1 + Math.exp(aVals[i]));
            expected[i] = dyVals[i] * y;
        }
        expectArraysClose(await da.data(), expected);
    });
    it('gradients: Tensor2D', async () => {
        const a = tf.tensor2d([1, 2, -3, 5], [2, 2]);
        const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
        const da = tf.grad(a => tf.logSigmoid(a))(a, dy);
        const expected = [];
        const aVals = await a.data();
        const dyVals = await dy.data();
        for (let i = 0; i < a.size; i++) {
            const y = 1 / (1 + Math.exp(aVals[i]));
            expected[i] = dyVals[i] * y;
        }
        expectArraysClose(await da.data(), expected);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.logSigmoid({}))
            .toThrowError(/Argument 'x' passed to 'logSigmoid' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.logSigmoid(-2);
        const expected = [Math.log(1 / (1 + Math.exp(2)))];
        expectArraysClose(await result.data(), expected);
    });
    it('throws for string tensor', () => {
        expect(() => tf.logSigmoid('q'))
            .toThrowError(/Argument 'x' passed to 'logSigmoid' must be numeric/);
    });
});
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* @license\n * Copyright 2020 Google Inc. 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('logSigmoid', 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.logSigmoid(a);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('scalar', async () => {\n    const a = tf.scalar(-2);\n\n    const result = tf.logSigmoid(a);\n\n    const expected = [Math.log(1 / (1 + Math.exp(2)))];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('tensor2D', async () => {\n    const values = [1, 2, -3, 5];\n    const a = tf.tensor2d(values, [2, 2]);\n\n    const result = tf.logSigmoid(a);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('larger magnitude negative inputs', async () => {\n    const values = [-100, -200, -3000];\n    const a = tf.tensor1d(values);\n\n    const result = tf.logSigmoid(a);\n\n    const expected = [-100, -200, -3000];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('larger magnitude positive inputs', async () => {\n    const values = [100, 200, 3000, 50000];\n    const a = tf.tensor1d(values);\n\n    const result = tf.logSigmoid(a);\n\n    const expected = [0, 0, 0, 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.logSigmoid(a);\n    expectArraysClose(\n        await res.data(), [Math.log(1 / (1 + Math.exp(-3))), NaN]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(3);\n    const dy = tf.scalar(4);\n    const dyVal = await dy.array();\n\n    const da = tf.grad(a => tf.logSigmoid(a))(a, dy);\n    const aVal = await a.array();\n    const y = 1 / (1 + Math.exp(aVal));\n    expectArraysClose(await da.data(), [dyVal * y]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const a = tf.tensor1d([1, 2, -3, 5]);\n    const aVals = await a.array();\n    const dy = tf.tensor1d([1, 2, 3, 4]);\n    const dyVals = await dy.array();\n    const da = tf.grad(a => tf.logSigmoid(a))(a, dy);\n\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;\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 aVals = await a.array();\n    const dy = tf.tensor1d([1, 2, 3, 4]);\n    const dyVals = await dy.array();\n    const da = tf.grad(a => tf.logSigmoid(a.clone()).clone())(a, dy);\n\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;\n    }\n\n    expectArraysClose(await da.data(), expected);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const a = tf.tensor2d([1, 2, -3, 5], [2, 2]);\n    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n\n    const da = tf.grad(a => tf.logSigmoid(a))(a, dy);\n\n    const expected = [];\n    const aVals = await a.data();\n    const dyVals = await dy.data();\n    for (let i = 0; i < a.size; i++) {\n      const y = 1 / (1 + Math.exp(aVals[i]));\n      expected[i] = dyVals[i] * y;\n    }\n\n    expectArraysClose(await da.data(), expected);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.logSigmoid({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'logSigmoid' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const result = tf.logSigmoid(-2);\n    const expected = [Math.log(1 / (1 + Math.exp(2)))];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.logSigmoid('q'))\n        .toThrowError(/Argument 'x' passed to 'logSigmoid' must be numeric/);\n  });\n});\n"]}