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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, expectArraysEqual} from '../test_util';\n\ndescribeWithFlags('add', ALL_ENVS, () => {\n it('c + A', async () => {\n const c = tf.scalar(5);\n const a = tf.tensor1d([1, 2, 3]);\n\n const result = tf.add(c, a);\n\n expectArraysClose(await result.data(), [6, 7, 8]);\n });\n\n it('c + A propagates NaNs', async () => {\n const c = tf.scalar(NaN);\n const a = tf.tensor1d([1, 2, 3]);\n\n const res = tf.add(c, a);\n\n expectArraysEqual(await res.data(), [NaN, NaN, NaN]);\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.add(a, b);\n\n expect(result.shape).toEqual([2, 2]);\n const expected = [3, 4, 0, -1];\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.add(a, b);\n\n expect(result.shape).toEqual([2, 2]);\n const expected = [2, 4, -2, -2];\n\n expectArraysClose(await result.data(), expected);\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.add(a, b);\n\n const expected = [6, 7, 0];\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.add(a, b);\n\n const expected = [6, 7, 0];\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.add(b);\n\n const expected = [6, 7, 0];\n expectArraysClose(await result.data(), expected);\n });\n\n it('A + B propagates NaNs', async () => {\n const a = tf.tensor1d([2, 5, NaN]);\n const b = tf.tensor1d([4, 2, -1]);\n\n const res = tf.add(a, b);\n expectArraysClose(await res.data(), [6, 7, NaN]);\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.add(a, b)).toThrowError();\n expect(() => tf.add(b, a)).toThrowError();\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.add(a, b);\n expect(res.shape).toEqual([2, 3]);\n expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);\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.add(a, b);\n expect(res.shape).toEqual([6]);\n expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);\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.add(a, b);\n expect(res.shape).toEqual([2, 3]);\n expectArraysClose(await res.data(), [8, 9, 12, 4, 5, 8]);\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.add(a, b);\n expect(res.shape).toEqual([2, 3]);\n expectArraysClose(await res.data(), [8, 9, 12, 7, 8, 9]);\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.add(a, b);\n expect(res.shape).toEqual([2, 3, 1]);\n expectArraysClose(await res.data(), [0, 1, 2, 3, 4, 5]);\n });\n\n it('6D+scalar', async () => {\n const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);\n const b = tf.scalar(-1);\n const res = tf.add(a, b);\n expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);\n const expectedResult = [\n -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,\n 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,\n 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,\n 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62\n ];\n expectArraysClose(await res.data(), expectedResult);\n });\n\n it('6D+2D', async () => {\n const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);\n const b = tf.tensor2d([11, 13, 17, 19], [2, 2]);\n const res = tf.add(a, b);\n expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);\n const expectedResult = [\n 11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,\n 27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,\n 43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,\n 59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82\n ];\n expectArraysClose(await res.data(), expectedResult);\n });\n\n it('add tensors with 0 in shape', async () => {\n const a = tf.tensor1d([1]);\n const b = tf.tensor3d([], [0, 0, 5]);\n const res = tf.add(a, b);\n expect(res.shape).toEqual([0, 0, 5]);\n expectArraysEqual(await res.data(), []);\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.add(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 with clones', 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.add(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(), [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([2, 3], [2, 1]);\n const b = tf.tensor2d([4, 5, 6, 7], [2, 2]);\n const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);\n\n const grads = tf.grads((a, b) => tf.add(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 addition', async () => {\n const real1 = tf.tensor1d([1]);\n const imag1 = tf.tensor1d([2]);\n const complex1 = tf.complex(real1, imag1);\n\n const real2 = tf.tensor1d([3]);\n const imag2 = tf.tensor1d([4]);\n const complex2 = tf.complex(real2, imag2);\n\n const result = complex1.add(complex2);\n\n expect(result.dtype).toBe('complex64');\n expect(result.shape).toEqual([1]);\n expectArraysClose(await result.data(), [4, 6]);\n });\n\n it('complex number reshape and then addition', async () => {\n const real1 = tf.tensor1d([1]);\n const imag1 = tf.tensor1d([2]);\n const complex1 = tf.complex(real1, imag1);\n\n const real2 = tf.tensor1d([3]);\n const imag2 = tf.tensor1d([4]);\n const complex2 = tf.complex(real2, imag2);\n\n const complex1Reshaped = complex1.reshape([1, 1, 1]);\n const complex2Reshaped = complex2.reshape([1, 1, 1]);\n\n const result = complex1Reshaped.add(complex2Reshaped);\n\n expect(result.dtype).toBe('complex64');\n expect(result.shape).toEqual([1, 1, 1]);\n expectArraysClose(await result.data(), [4, 6]);\n });\n\n it('complex number broadcasting addition', 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.add(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.add({} as tf.Tensor, tf.scalar(1)))\n .toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);\n });\n it('throws when passed b as a non-tensor', () => {\n expect(() => tf.add(tf.scalar(1), {} as tf.Tensor))\n .toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);\n });\n\n it('upcasts when dtypes dont match', async () => {\n let res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));\n expect(res.dtype).toBe('float32');\n expectArraysClose(await res.data(), [2]);\n\n res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));\n expect(res.dtype).toBe('int32');\n expectArraysClose(await res.data(), [2]);\n\n res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));\n expect(res.dtype).toBe('int32');\n expectArraysClose(await res.data(), [1]);\n\n res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));\n expect(res.dtype).toBe('complex64');\n expectArraysClose(await res.data(), [5, 7]);\n\n res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));\n expect(res.dtype).toBe('complex64');\n expectArraysClose(await res.data(), [5, 7]);\n });\n\n it('accepts a tensor-like object', async () => {\n const result = tf.add(5, [1, 2, 3]);\n expectArraysClose(await result.data(), [6, 7, 8]);\n });\n});\n"]} |