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* @license\n * Copyright 2020 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('asinh', ALL_ENVS, () => {\n it('basic', async () => {\n const values = [1, -3, 2, 7, -4];\n const a = tf.tensor1d(values);\n const result = tf.asinh(a);\n\n const expected = [];\n for (let i = 0; i < a.size; i++) {\n expected[i] = Math.asinh(values[i]);\n }\n expectArraysClose(await result.data(), expected);\n });\n\n it('scalar', async () => {\n const a = tf.scalar(1);\n const result = tf.asinh(a);\n\n const expected = [Math.asinh(1)];\n expectArraysClose(await result.data(), expected);\n });\n\n it('tensor2D', async () => {\n const values = [1, -3, 2, 7];\n const a = tf.tensor2d(values, [2, 2]);\n const result = tf.asinh(a);\n\n const expected = [];\n for (let i = 0; i < a.size; i++) {\n expected[i] = Math.asinh(values[i]);\n }\n expectArraysClose(await result.data(), expected);\n });\n\n it('propagates NaNs', async () => {\n const a = tf.tensor1d([4, NaN, 0]);\n const res = tf.asinh(a);\n expectArraysClose(await res.data(), [Math.asinh(4), NaN, Math.asinh(0)]);\n });\n\n it('gradients: Scalar', async () => {\n const a = tf.scalar(0.5);\n const dy = tf.scalar(8);\n\n const gradients = tf.grad(a => tf.asinh(a))(a, dy);\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n expectArraysClose(await gradients.data(), [8 / Math.sqrt(1.0 + 0.5 * 0.5)]);\n });\n\n it('gradient with clones', async () => {\n const a = tf.scalar(0.5);\n const dy = tf.scalar(8);\n\n const gradients = tf.grad(a => tf.asinh(a.clone()).clone())(a, dy);\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n expectArraysClose(await gradients.data(), [8 / Math.sqrt(1.0 + 0.5 * 0.5)]);\n });\n\n it('gradients: Tensor1D', async () => {\n const aValues = [-1, 2, 3, -5];\n const dyValues = [1, 2, 3, 4];\n const a = tf.tensor1d(aValues);\n const dy = tf.tensor1d(dyValues);\n\n const gradients = tf.grad(a => tf.asinh(a))(a, dy);\n\n const expected = [];\n for (let i = 0; i < a.size; i++) {\n expected[i] = dyValues[i] / Math.sqrt(1 + aValues[i] * aValues[i]);\n }\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n expectArraysClose(await gradients.data(), expected);\n });\n\n it('gradients: Tensor2D', async () => {\n const aValues = [-3, 1, 2, 3];\n const dyValues = [1, 2, 3, 4];\n const a = tf.tensor2d(aValues, [2, 2]);\n const dy = tf.tensor2d(dyValues, [2, 2]);\n\n const gradients = tf.grad(a => tf.asinh(a))(a, dy);\n\n const expected = [];\n for (let i = 0; i < a.size; i++) {\n expected[i] = dyValues[i] / Math.sqrt(1 + aValues[i] * aValues[i]);\n }\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n expectArraysClose(await gradients.data(), expected);\n });\n\n it('throws when passed a non-tensor', () => {\n expect(() => tf.asinh({} as tf.Tensor))\n .toThrowError(/Argument 'x' passed to 'asinh' 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.asinh(values);\n\n const expected = [];\n for (let i = 0; i < values.length; i++) {\n expected[i] = Math.asinh(values[i]);\n }\n expectArraysClose(await result.data(), expected);\n });\n\n it('throws for string tensor', () => {\n expect(() => tf.asinh('q'))\n .toThrowError(/Argument 'x' passed to 'asinh' must be numeric/);\n });\n});\n"]} |