/**
 * @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';
import { backend } from '../index';
describeWithFlags('relu', ALL_ENVS, () => {
    it('basic', async () => {
        const a = tf.tensor1d([1, -2, 0, 3, -0.1]);
        const result = tf.relu(a);
        expectArraysClose(await result.data(), [1, 0, 0, 3, 0]);
    });
    it('int32', async () => {
        if (backend() && backend().floatPrecision() === 32) {
            // TODO: Use skip() instead when it is implemented
            const a = tf.tensor1d([12345678, -2, 0, 3, -1], 'int32');
            const result = tf.relu(a);
            expect(result.dtype).toEqual('int32');
            expectArraysClose(await result.data(), [12345678, 0, 0, 3, 0]);
        }
    });
    it('5D', async () => {
        const a = tf.tensor5d([1, -2, 5, -3], [1, 2, 2, 1, 1]);
        const result = tf.relu(a);
        expectArraysClose(await result.data(), [1, 0, 5, 0]);
    });
    it('6D', async () => {
        const a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);
        const result = tf.relu(a);
        expectArraysClose(await result.data(), [1, 0, 5, 0, 0, 4, 7, 8]);
    });
    it('does nothing to positive values', async () => {
        const a = tf.scalar(1);
        const result = tf.relu(a);
        expectArraysClose(await result.data(), [1]);
    });
    it('sets negative values to 0', async () => {
        const a = tf.scalar(-1);
        const result = tf.relu(a);
        expectArraysClose(await result.data(), [0]);
    });
    it('preserves zero values', async () => {
        const a = tf.scalar(0);
        const result = tf.relu(a);
        expectArraysClose(await result.data(), [0]);
    });
    it('propagates NaNs, float32', async () => {
        const a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);
        const result = tf.relu(a);
        expect(result.dtype).toBe('float32');
        expectArraysClose(await result.data(), [1, 0, 0, 3, 0, NaN]);
    });
    it('gradients: positive scalar', async () => {
        const a = tf.scalar(3);
        const dy = tf.scalar(5);
        const grad = tf.grad(a => tf.relu(a));
        const da = grad(a, dy);
        expect(da.shape).toEqual(a.shape);
        expect(da.dtype).toEqual('float32');
        expectArraysClose(await da.data(), [5]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(3);
        const dy = tf.scalar(5);
        const grad = tf.grad(a => tf.relu(a.clone()).clone());
        const da = grad(a, dy);
        expect(da.shape).toEqual(a.shape);
        expect(da.dtype).toEqual('float32');
        expectArraysClose(await da.data(), [5]);
    });
    it('gradients: negative scalar', async () => {
        const a = tf.scalar(-3);
        const dy = tf.scalar(5);
        const grad = tf.grad(a => tf.relu(a));
        const da = grad(a, dy);
        expect(da.shape).toEqual(a.shape);
        expect(da.dtype).toEqual('float32');
        expectArraysClose(await da.data(), [0]);
    });
    it('gradients: array', async () => {
        const a = tf.tensor2d([1, -1, 0, .1], [2, 2]);
        const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
        const grad = tf.grad(a => tf.relu(a));
        const da = grad(a, dy);
        expect(da.shape).toEqual(a.shape);
        expect(da.dtype).toEqual('float32');
        expectArraysClose(await da.data(), [1, 0, 0, 4]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.relu({}))
            .toThrowError(/Argument 'x' passed to 'relu' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.relu([1, -2, 0, 3, -0.1]);
        expectArraysClose(await result.data(), [1, 0, 0, 3, 0]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.relu('q'))
            .toThrowError(/Argument 'x' passed to 'relu' must be numeric/);
    });
});
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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';\nimport {backend} from '../index';\n\ndescribeWithFlags('relu', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.tensor1d([1, -2, 0, 3, -0.1]);\n    const result = tf.relu(a);\n    expectArraysClose(await result.data(), [1, 0, 0, 3, 0]);\n  });\n\n  it('int32', async () => {\n    if (backend() && backend().floatPrecision() === 32) {\n      // TODO: Use skip() instead when it is implemented\n      const a = tf.tensor1d([12345678, -2, 0, 3, -1], 'int32');\n      const result = tf.relu(a);\n      expect(result.dtype).toEqual('int32');\n      expectArraysClose(await result.data(), [12345678, 0, 0, 3, 0]);\n    }\n  });\n\n  it('5D', async () => {\n    const a = tf.tensor5d([1, -2, 5, -3], [1, 2, 2, 1, 1]);\n    const result = tf.relu(a);\n    expectArraysClose(await result.data(), [1, 0, 5, 0]);\n  });\n\n  it('6D', async () => {\n    const a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);\n    const result = tf.relu(a);\n    expectArraysClose(await result.data(), [1, 0, 5, 0, 0, 4, 7, 8]);\n  });\n\n  it('does nothing to positive values', async () => {\n    const a = tf.scalar(1);\n    const result = tf.relu(a);\n    expectArraysClose(await result.data(), [1]);\n  });\n\n  it('sets negative values to 0', async () => {\n    const a = tf.scalar(-1);\n    const result = tf.relu(a);\n    expectArraysClose(await result.data(), [0]);\n  });\n\n  it('preserves zero values', async () => {\n    const a = tf.scalar(0);\n    const result = tf.relu(a);\n    expectArraysClose(await result.data(), [0]);\n  });\n\n  it('propagates NaNs, float32', async () => {\n    const a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);\n    const result = tf.relu(a);\n    expect(result.dtype).toBe('float32');\n    expectArraysClose(await result.data(), [1, 0, 0, 3, 0, NaN]);\n  });\n\n  it('gradients: positive scalar', async () => {\n    const a = tf.scalar(3);\n    const dy = tf.scalar(5);\n\n    const grad = tf.grad(a => tf.relu(a));\n    const da = grad(a, dy);\n\n    expect(da.shape).toEqual(a.shape);\n    expect(da.dtype).toEqual('float32');\n    expectArraysClose(await da.data(), [5]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(3);\n    const dy = tf.scalar(5);\n\n    const grad = tf.grad(a => tf.relu(a.clone()).clone());\n    const da = grad(a, dy);\n\n    expect(da.shape).toEqual(a.shape);\n    expect(da.dtype).toEqual('float32');\n    expectArraysClose(await da.data(), [5]);\n  });\n\n  it('gradients: negative scalar', async () => {\n    const a = tf.scalar(-3);\n    const dy = tf.scalar(5);\n\n    const grad = tf.grad(a => tf.relu(a));\n    const da = grad(a, dy);\n\n    expect(da.shape).toEqual(a.shape);\n    expect(da.dtype).toEqual('float32');\n    expectArraysClose(await da.data(), [0]);\n  });\n\n  it('gradients: array', async () => {\n    const a = tf.tensor2d([1, -1, 0, .1], [2, 2]);\n    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n\n    const grad = tf.grad(a => tf.relu(a));\n    const da = grad(a, dy);\n\n    expect(da.shape).toEqual(a.shape);\n    expect(da.dtype).toEqual('float32');\n    expectArraysClose(await da.data(), [1, 0, 0, 4]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.relu({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'relu' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const result = tf.relu([1, -2, 0, 3, -0.1]);\n    expectArraysClose(await result.data(), [1, 0, 0, 3, 0]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.relu('q'))\n        .toThrowError(/Argument 'x' passed to 'relu' must be numeric/);\n  });\n});\n"]}