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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 */\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('sub', ALL_ENVS, () => {\n it('c - A', async () => {\n const c = tf.scalar(5);\n const a = tf.tensor1d([7, 2, 3]);\n\n const result = tf.sub(c, a);\n\n expectArraysClose(await result.data(), [-2, 3, 2]);\n });\n\n it('A - c', async () => {\n const a = tf.tensor1d([1, 2, -3]);\n const c = tf.scalar(5);\n\n const result = tf.sub(a, c);\n\n expectArraysClose(await result.data(), [-4, -3, -8]);\n });\n\n it('A - c propagates NaNs', async () => {\n const a = tf.tensor1d([1, NaN, 3]);\n const c = tf.scalar(5);\n\n const res = tf.sub(a, c);\n\n expectArraysClose(await res.data(), [-4, NaN, -2]);\n });\n\n it('A - B', async () => {\n const a = tf.tensor1d([2, 5, 1]);\n const b = tf.tensor1d([4, 2, -1]);\n\n const result = tf.sub(a, b);\n\n const expected = [-2, 3, 2];\n expectArraysClose(await result.data(), expected);\n });\n\n it('TensorLike', async () => {\n const a = [2, 5, 1];\n const b = [4, 2, -1];\n\n const result = tf.sub(a, b);\n\n const expected = [-2, 3, 2];\n expectArraysClose(await result.data(), expected);\n });\n\n it('TensorLike chained', async () => {\n const a = tf.tensor1d([2, 5, 1]);\n const b = [4, 2, -1];\n\n const result = a.sub(b);\n\n const expected = [-2, 3, 2];\n expectArraysClose(await result.data(), expected);\n });\n\n it('A - B propagates NaNs', async () => {\n const a = tf.tensor1d([2, 5, 1]);\n const b = tf.tensor1d([4, NaN, -1]);\n\n const res = tf.sub(a, b);\n\n expectArraysClose(await res.data(), [-2, NaN, 2]);\n });\n\n it('A - B throws when passed tensors with different shape', () => {\n const a = tf.tensor1d([2, 5, 1, 5]);\n const b = tf.tensor1d([4, 2, -1]);\n\n expect(() => tf.sub(a, b)).toThrowError();\n expect(() => tf.sub(b, a)).toThrowError();\n });\n\n it('A - B broadcasting same rank Tensors different shape', async () => {\n const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);\n const b = tf.tensor2d([2, 3], [2, 1]);\n\n const result = tf.sub(a, b);\n\n expect(result.shape).toEqual([2, 2]);\n const expected = [-1, 0, -6, -7];\n\n expectArraysClose(await result.data(), expected);\n });\n\n it('A - B broadcast 2D + 1D', async () => {\n const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);\n const b = tf.tensor1d([1, 2]);\n\n const result = tf.sub(a, b);\n\n expect(result.shape).toEqual([2, 2]);\n const expected = [0, 0, -4, -6];\n\n expectArraysClose(await result.data(), expected);\n });\n\n it('2D-scalar broadcast', async () => {\n const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n const b = tf.scalar(2);\n const res = tf.sub(a, b);\n expect(res.shape).toEqual([2, 3]);\n expectArraysClose(await res.data(), [-1, 0, 1, 2, 3, 4]);\n });\n\n it('scalar-1D broadcast', async () => {\n const a = tf.scalar(2);\n const b = tf.tensor1d([1, 2, 3, 4, 5, 6]);\n const res = tf.sub(a, b);\n expect(res.shape).toEqual([6]);\n expectArraysClose(await res.data(), [1, 0, -1, -2, -3, -4]);\n });\n\n it('2D-2D broadcast each with 1 dim', async () => {\n const a = tf.tensor2d([1, 2, 5], [1, 3]);\n const b = tf.tensor2d([7, 3], [2, 1]);\n const res = tf.sub(a, b);\n expect(res.shape).toEqual([2, 3]);\n expectArraysClose(await res.data(), [-6, -5, -2, -2, -1, 2]);\n });\n\n it('2D-2D broadcast inner dim of b', async () => {\n const a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);\n const b = tf.tensor2d([7, 3], [2, 1]);\n const res = tf.sub(a, b);\n expect(res.shape).toEqual([2, 3]);\n expectArraysClose(await res.data(), [-6, -5, -2, 1, 2, 3]);\n });\n\n it('3D-scalar', async () => {\n const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);\n const b = tf.scalar(-1);\n const res = tf.sub(a, b);\n expect(res.shape).toEqual([2, 3, 1]);\n expectArraysClose(await res.data(), [2, 3, 4, 5, 6, 7]);\n });\n\n it('gradients: basic 1D arrays', async () => {\n const a = tf.tensor1d([1, 2, 3]);\n const b = tf.tensor1d([3, 2, 1]);\n const dy = tf.tensor1d([1, 10, 20]);\n\n const grads = tf.grads((a, b) => tf.sub(a, b));\n const [da, db] = grads([a, b], dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [1, 10, 20]);\n\n expect(db.shape).toEqual(b.shape);\n expect(db.dtype).toEqual('float32');\n expectArraysClose(await db.data(), [-1, -10, -20]);\n });\n\n it('gradient with clones', async () => {\n const a = tf.tensor1d([1, 2, 3]);\n const b = tf.tensor1d([3, 2, 1]);\n const dy = tf.tensor1d([1, 10, 20]);\n\n const grads = tf.grads((a, b) => tf.sub(a.clone(), b.clone()).clone());\n const [da, db] = grads([a, b], dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [1, 10, 20]);\n\n expect(db.shape).toEqual(b.shape);\n expect(db.dtype).toEqual('float32');\n expectArraysClose(await db.data(), [-1, -10, -20]);\n });\n\n it('gradients: basic 2D arrays', async () => {\n const a = tf.tensor2d([0, 1, 2, 3], [2, 2]);\n const b = tf.tensor2d([3, 2, 1, 0], [2, 2]);\n const dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);\n\n const grads = tf.grads((a, b) => tf.sub(a, b));\n const [da, db] = grads([a, b], dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [1, 10, 15, 20]);\n\n expect(db.shape).toEqual(b.shape);\n expect(db.dtype).toEqual('float32');\n expectArraysClose(await db.data(), [-1, -10, -15, -20]);\n });\n\n it('gradient: 1D - scalar broadcast', async () => {\n const a = tf.tensor1d([3, 4, 5]);\n const b = tf.scalar(2);\n const dy = tf.tensor1d([7, 8, 9]);\n\n const grads = tf.grads((a, b) => tf.sub(a, b));\n const [da, db] = grads([a, b], dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [7, 8, 9]);\n\n expect(db.shape).toEqual(b.shape);\n expect(db.dtype).toEqual('float32');\n expectArraysClose(await db.data(), [-7 - 8 - 9]);\n });\n\n it('gradient: scalar - 1D broadcast', async () => {\n const a = tf.scalar(2);\n const b = tf.tensor1d([3, 4, 5]);\n const dy = tf.tensor1d([7, 8, 9]);\n\n const grads = tf.grads((a, b) => tf.sub(a, b));\n const [da, db] = grads([a, b], dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [7 + 8 + 9]);\n\n expect(db.shape).toEqual(b.shape);\n expect(db.dtype).toEqual('float32');\n expectArraysClose(await db.data(), [-7, -8, -9]);\n });\n\n it('gradient: 2D - 2D broadcast', async () => {\n const a = tf.tensor2d([4, 5, 6, 7], [2, 2]);\n const b = tf.tensor2d([2, 3], [2, 1]);\n const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);\n\n const grads = tf.grads((a, b) => tf.sub(a, b));\n const [da, db] = grads([a, b], dy);\n\n expect(da.shape).toEqual(a.shape);\n expect(da.dtype).toEqual('float32');\n expectArraysClose(await da.data(), [5, 4, 3, 2]);\n\n expect(db.shape).toEqual(b.shape);\n expect(db.dtype).toEqual('float32');\n expectArraysClose(await db.data(), [-5 - 4, -3 - 2]);\n });\n\n it('complex number subtraction', async () => {\n const real1 = tf.tensor1d([3]);\n const imag1 = tf.tensor1d([5]);\n const complex1 = tf.complex(real1, imag1);\n\n const real2 = tf.tensor1d([1]);\n const imag2 = tf.tensor1d([0]);\n const complex2 = tf.complex(real2, imag2);\n\n const result = complex1.sub(complex2);\n\n expect(result.dtype).toBe('complex64');\n expect(result.shape).toEqual([1]);\n expectArraysClose(await result.data(), [2, 5]);\n });\n\n it('complex number broadcasting subtraction', async () => {\n const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);\n const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);\n const complex1 = tf.complex(real1, imag1);\n\n const real2 = tf.tensor1d([4]);\n const imag2 = tf.tensor1d([5]);\n const complex2 = tf.complex(real2, imag2);\n\n const result = tf.sub(complex1, complex2);\n\n expect(result.dtype).toEqual('complex64');\n expect(result.shape).toEqual([2, 2]);\n expectArraysClose(\n await result.data(),\n [1 - 4, 10 - 5, 2 - 4, 20 - 5, -3 - 4, -30 - 5, -4 - 4, -40 - 5]);\n });\n\n it('throws when passed a as a non-tensor', () => {\n expect(() => tf.sub({} as tf.Tensor, tf.scalar(1)))\n .toThrowError(/Argument 'a' passed to 'sub' must be a Tensor/);\n });\n it('throws when passed b as a non-tensor', () => {\n expect(() => tf.sub(tf.scalar(1), {} as tf.Tensor))\n .toThrowError(/Argument 'b' passed to 'sub' must be a Tensor/);\n });\n it('upcasts when dtypes dont match', async () => {\n let res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));\n expect(res.dtype).toBe('float32');\n expectArraysClose(await res.data(), [0]);\n\n res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));\n expect(res.dtype).toBe('int32');\n expectArraysClose(await res.data(), [0]);\n\n res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));\n expect(res.dtype).toBe('int32');\n expectArraysClose(await res.data(), [1]);\n\n res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'float32'));\n expect(res.dtype).toBe('complex64');\n expectArraysClose(await res.data(), [3, 7]);\n\n res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'int32'));\n expect(res.dtype).toBe('complex64');\n expectArraysClose(await res.data(), [3, 7]);\n });\n\n it('accepts a tensor-like object', async () => {\n const result = tf.sub(5, [7, 2, 3]);\n expectArraysClose(await result.data(), [-2, 3, 2]);\n });\n});\n"]} |