/**
 * @license
 * Copyright 2017 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('softmax', ALL_ENVS, () => {
    it('regular test', async () => {
        const y = tf.softmax(tf.tensor1d([2, 1, 3]));
        expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);
        expectArraysClose(await y.sum().data(), 1);
    });
    it('overflow', async () => {
        const y = tf.softmax(tf.tensor1d([100, 100]));
        expectArraysClose(await y.data(), [0.5, 0.5]);
    });
    it('underflow', async () => {
        const y = tf.softmax(tf.tensor1d([-100, -100]));
        expectArraysClose(await y.data(), [0.5, 0.5]);
    });
    it('odd number of inputs', async () => {
        const y = tf.softmax(tf.tensor1d([-400, -400, 0, -400, -400, -400, -400]));
        expectArraysClose(await y.data(), [0, 0, 1, 0, 0, 0, 0]);
    });
    it('Huge difference between probabilities', async () => {
        const y = tf.softmax(tf.tensor1d([-1000, +1000]));
        expectArraysClose(await y.data(), [0, 1]);
    });
    it('Propagates NaNs', async () => {
        const a = tf.tensor1d([2, 1, NaN]);
        const y = tf.softmax(a);
        expectArraysClose(await y.data(), [NaN, NaN, NaN]);
    });
    it('2D, dim=1', async () => {
        const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
        const expected = [
            0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
        ];
        expect(y.rank).toBe(2);
        expectArraysClose(await y.data(), expected);
    });
    it('2D, implicit dim=1', async () => {
        const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
        const expected = [
            0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
        ];
        expect(y.rank).toBe(2);
        expectArraysClose(await y.data(), expected);
    });
    it('2D, dim=0 throws error', () => {
        const f = () => {
            tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
        };
        expect(f).toThrowError();
    });
    it('1D gradient', async () => {
        const x = tf.tensor1d([10, 0, -1]);
        const y = tf.softmax(x);
        const dy = tf.tensor1d([1, 2, 3]);
        const dx = tf.grad((x) => x.softmax())(x, dy);
        const totalSum = tf.sum(tf.mul(dy, y));
        const dyVals = await dy.array();
        const sumVals = await totalSum.array();
        const yVals = await y.array();
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [
            (dyVals[0] - sumVals) * yVals[0],
            (dyVals[1] - sumVals) * yVals[1],
            (dyVals[2] - sumVals) * yVals[2],
        ]);
    });
    it('gradient with clones', () => {
        const x = tf.tensor1d([10, 0, -1]);
        const dx = tf.grad((x) => x.clone().softmax().clone())(x);
        expect(dx.shape).toEqual(x.shape);
        expect(dx.dtype).toBe('float32');
    });
    it('2D gradient', async () => {
        const x = tf.tensor2d([10, 0, -1, 5, 4, 3], [2, 3]);
        const y = tf.softmax(x);
        const dy = tf.tensor2d([3, 2, 1, 1, 2, 3], [2, 3]);
        const dx = tf.grad((x) => x.softmax())(x, dy);
        const axis = -1;
        const totalSum = tf.sum(tf.mul(dy, y), axis);
        const dyVals = await dy.array();
        const sumVals = await totalSum.array();
        const yVals = await y.array();
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [
            (dyVals[0][0] - sumVals[0]) * yVals[0][0],
            (dyVals[0][1] - sumVals[0]) * yVals[0][1],
            (dyVals[0][2] - sumVals[0]) * yVals[0][2],
            (dyVals[1][0] - sumVals[1]) * yVals[1][0],
            (dyVals[1][1] - sumVals[1]) * yVals[1][1],
            (dyVals[1][2] - sumVals[1]) * yVals[1][2]
        ]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.softmax({}))
            .toThrowError(/Argument 'logits' passed to 'softmax' must be a Tensor/);
    });
    it('throws when passed an int32 tensor', async () => {
        expect(() => tf.softmax(tf.tensor1d([2, 1, 3], 'int32')))
            .toThrowError(/Argument 'logits' passed to 'softmax' must be float32/);
    });
    it('accepts a tensor-like object', async () => {
        const y = tf.softmax([2, 1, 3]);
        expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);
        expectArraysClose(await y.sum().data(), 1);
    });
});
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* @license\n * Copyright 2017 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\ndescribeWithFlags('softmax', ALL_ENVS, () => {\n  it('regular test', async () => {\n    const y = tf.softmax(tf.tensor1d([2, 1, 3]));\n\n    expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);\n    expectArraysClose(await y.sum().data(), 1);\n  });\n\n  it('overflow', async () => {\n    const y = tf.softmax(tf.tensor1d([100, 100]));\n\n    expectArraysClose(await y.data(), [0.5, 0.5]);\n  });\n\n  it('underflow', async () => {\n    const y = tf.softmax(tf.tensor1d([-100, -100]));\n\n    expectArraysClose(await y.data(), [0.5, 0.5]);\n  });\n\n  it('odd number of inputs', async () => {\n    const y = tf.softmax(tf.tensor1d([-400, -400, 0, -400, -400, -400, -400]));\n\n    expectArraysClose(await y.data(), [0, 0, 1, 0, 0, 0, 0]);\n  });\n\n  it('Huge difference between probabilities', async () => {\n    const y = tf.softmax(tf.tensor1d([-1000, +1000]));\n\n    expectArraysClose(await y.data(), [0, 1]);\n  });\n\n  it('Propagates NaNs', async () => {\n    const a = tf.tensor1d([2, 1, NaN]);\n    const y = tf.softmax(a);\n    expectArraysClose(await y.data(), [NaN, NaN, NaN]);\n  });\n\n  it('2D, dim=1', async () => {\n    const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);\n    const expected = [\n      0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847\n    ];\n    expect(y.rank).toBe(2);\n    expectArraysClose(await y.data(), expected);\n  });\n\n  it('2D, implicit dim=1', async () => {\n    const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));\n    const expected = [\n      0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847\n    ];\n    expect(y.rank).toBe(2);\n    expectArraysClose(await y.data(), expected);\n  });\n\n  it('2D, dim=0 throws error', () => {\n    const f = () => {\n      tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);\n    };\n    expect(f).toThrowError();\n  });\n\n  it('1D gradient', async () => {\n    const x = tf.tensor1d([10, 0, -1]);\n    const y = tf.softmax(x);\n    const dy = tf.tensor1d([1, 2, 3]);\n    const dx = tf.grad((x) => x.softmax())(x, dy);\n\n    const totalSum: tf.Scalar = tf.sum(tf.mul(dy, y));\n\n    const dyVals = await dy.array();\n    const sumVals = await totalSum.array();\n    const yVals = await y.array();\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [\n      (dyVals[0] - sumVals) * yVals[0],\n      (dyVals[1] - sumVals) * yVals[1],\n      (dyVals[2] - sumVals) * yVals[2],\n    ]);\n  });\n\n  it('gradient with clones', () => {\n    const x = tf.tensor1d([10, 0, -1]);\n    const dx = tf.grad((x) => x.clone().softmax().clone())(x);\n    expect(dx.shape).toEqual(x.shape);\n    expect(dx.dtype).toBe('float32');\n  });\n\n  it('2D gradient', async () => {\n    const x = tf.tensor2d([10, 0, -1, 5, 4, 3], [2, 3]);\n    const y = tf.softmax(x);\n    const dy = tf.tensor2d([3, 2, 1, 1, 2, 3], [2, 3]);\n    const dx = tf.grad((x) => x.softmax())(x, dy);\n\n    const axis = -1;\n    const totalSum: tf.Tensor1D = tf.sum(tf.mul(dy, y), axis);\n\n    const dyVals = await dy.array();\n    const sumVals = await totalSum.array();\n    const yVals = await y.array();\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [\n      (dyVals[0][0] - sumVals[0]) * yVals[0][0],\n      (dyVals[0][1] - sumVals[0]) * yVals[0][1],\n      (dyVals[0][2] - sumVals[0]) * yVals[0][2],\n      (dyVals[1][0] - sumVals[1]) * yVals[1][0],\n      (dyVals[1][1] - sumVals[1]) * yVals[1][1],\n      (dyVals[1][2] - sumVals[1]) * yVals[1][2]\n    ]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.softmax({} as tf.Tensor))\n        .toThrowError(/Argument 'logits' passed to 'softmax' must be a Tensor/);\n  });\n\n  it('throws when passed an int32 tensor', async () => {\n    expect(() => tf.softmax(tf.tensor1d([2, 1, 3], 'int32')))\n        .toThrowError(/Argument 'logits' passed to 'softmax' must be float32/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const y = tf.softmax([2, 1, 3]);\n\n    expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);\n    expectArraysClose(await y.sum().data(), 1);\n  });\n});\n"]}