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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('neg', ALL_ENVS, () => {\n it('basic', async () => {\n const a = tf.tensor1d([1, -3, 2, 7, -4]);\n const result = tf.neg(a);\n expectArraysClose(await result.data(), [-1, 3, -2, -7, 4]);\n });\n\n it('propagate NaNs', async () => {\n const a = tf.tensor1d([1, -3, 2, 7, NaN]);\n const result = tf.neg(a);\n const expected = [-1, 3, -2, -7, NaN];\n expectArraysClose(await result.data(), expected);\n });\n\n it('gradients: Scalar', async () => {\n const a = tf.scalar(4);\n const dy = tf.scalar(8);\n\n const da = tf.grad(a => tf.neg(a))(a, dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [8 * -1]);\n });\n\n it('gradients: Scalar', async () => {\n const a = tf.scalar(4);\n const dy = tf.scalar(8);\n\n const da = tf.grad(a => tf.neg(a.clone()).clone())(a, dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [8 * -1]);\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.neg(a))(a, dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [1 * -1, 2 * -1, 3 * -1, 4 * -1]);\n });\n\n it('gradients: Tensor2D', async () => {\n const a = tf.tensor2d([3, -1, -2, 3], [2, 2]);\n const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n\n const da = tf.grad(a => tf.neg(a))(a, dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [1 * -1, 2 * -1, 3 * -1, 4 * -1]);\n });\n\n it('throws when passed a non-tensor', () => {\n expect(() => tf.neg({} as tf.Tensor))\n .toThrowError(/Argument 'x' passed to 'neg' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const result = tf.neg([1, -3, 2, 7, -4]);\n expectArraysClose(await result.data(), [-1, 3, -2, -7, 4]);\n });\n\n it('throws for string tensor', () => {\n expect(() => tf.neg('q'))\n .toThrowError(/Argument 'x' passed to 'neg' must be numeric/);\n });\n});\n"]} |