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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, expectArraysEqual} from '../test_util';\n\nimport * as reduce_util from './reduce_util';\n\ndescribeWithFlags('argmin', ALL_ENVS, () => {\n it('Tensor1D', async () => {\n const a = tf.tensor1d([1, 0, 3, 2]);\n const result = tf.argMin(a);\n expectArraysEqual(await result.data(), 1);\n });\n\n it('one value', async () => {\n const a = tf.tensor1d([10]);\n const result = tf.argMin(a);\n expectArraysEqual(await result.data(), 0);\n });\n\n it('N > than parallelization threshold', async () => {\n const n = reduce_util.PARALLELIZE_THRESHOLD * 2;\n const values = new Float32Array(n);\n for (let i = 0; i < n; i++) {\n values[i] = n - i;\n }\n const a = tf.tensor1d(values);\n const result = tf.argMin(a);\n expect(result.dtype).toBe('int32');\n expectArraysEqual(await result.data(), n - 1);\n });\n\n it('4D, N > than parallelization threshold', async () => {\n const n = reduce_util.PARALLELIZE_THRESHOLD * 2;\n const values = new Float32Array(n);\n for (let i = 0; i < n; i++) {\n values[i] = n - i;\n }\n const a = tf.tensor4d(values, [1, 1, 1, n]);\n const result = tf.argMin(a, -1);\n expect(result.dtype).toBe('int32');\n expectArraysEqual(await result.data(), n - 1);\n });\n\n it('min index corresponds to start of a non-initial window', async () => {\n const n = reduce_util.PARALLELIZE_THRESHOLD * 2;\n const windowSize = reduce_util.computeOptimalWindowSize(n);\n const values = new Float32Array(n);\n const index = windowSize * 2;\n values[index] = -1;\n const a = tf.tensor1d(values);\n const result = tf.argMin(a);\n expect(result.dtype).toBe('int32');\n expectArraysEqual(await result.data(), index);\n });\n\n it('ignores NaNs', async () => {\n const a = tf.tensor1d([5, 0, NaN, -1, 3]);\n const res = tf.argMin(a);\n expectArraysEqual(await res.data(), 3);\n });\n\n it('3D, ignores NaNs', async () => {\n const a = tf.tensor3d([5, 0, NaN, -1, 3], [1, 1, 5]);\n const res = tf.argMin(a, -1);\n expectArraysEqual(await res.data(), 3);\n });\n\n it('2D, no axis specified', async () => {\n const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n expectArraysEqual(await tf.argMin(a).data(), [0, 1, 0]);\n });\n\n it('2D, axis=0', async () => {\n const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n const r = tf.argMin(a, 0);\n\n expect(r.shape).toEqual([3]);\n expect(r.dtype).toBe('int32');\n expectArraysEqual(await r.data(), [0, 1, 0]);\n });\n\n it('2D, axis=1', async () => {\n const a = tf.tensor2d([3, 2, 5, 100, -7, -8], [2, 3]);\n const r = tf.argMin(a, 1);\n expectArraysEqual(await r.data(), [1, 2]);\n });\n\n it('2D, axis = -1', async () => {\n const a = tf.tensor2d([3, 2, 5, 100, -7, -8], [2, 3]);\n const r = tf.argMin(a, -1);\n expectArraysEqual(await r.data(), [1, 2]);\n });\n\n it('throws when passed a non-tensor', () => {\n expect(() => tf.argMin({} as tf.Tensor))\n .toThrowError(/Argument 'x' passed to 'argMin' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const result = tf.argMin([1, 0, 3, 2]);\n expectArraysEqual(await result.data(), 1);\n });\n\n it('accepts tensor with bool values', async () => {\n const t = tf.tensor1d([0, 1], 'bool');\n const result = tf.argMin(t);\n expect(result.dtype).toBe('int32');\n expectArraysEqual(await result.data(), 0);\n });\n\n it('has gradient', async () => {\n const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n const dy = tf.ones([3], 'float32');\n const da = tf.grad((x: tf.Tensor2D) => tf.argMin(x))(a, dy);\n\n expect(da.dtype).toBe('float32');\n expect(da.shape).toEqual([2, 3]);\n expectArraysClose(await da.data(), [0, 0, 0, 0, 0, 0]);\n });\n\n it('gradient with clones', async () => {\n const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n const dy = tf.ones([3], 'float32');\n const da = tf.grad((x: tf.Tensor2D) => tf.argMin(x.clone()).clone())(a, dy);\n\n expect(da.dtype).toBe('float32');\n expect(da.shape).toEqual([2, 3]);\n expectArraysClose(await da.data(), [0, 0, 0, 0, 0, 0]);\n });\n\n it('throws error for string tensor', () => {\n expect(() => tf.argMin(['a']))\n .toThrowError(/Argument 'x' passed to 'argMin' must be numeric tensor/);\n });\n});\n"]} |