@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose, expectArraysEqual } from '../test_util';
describeWithFlags('add', ALL_ENVS, () => {
it('c + A', async () => {
const c = tf.scalar(5);
const a = tf.tensor1d([1, 2, 3]);
const result = tf.add(c, a);
expectArraysClose(await result.data(), [6, 7, 8]);
});
it('c + A propagates NaNs', async () => {
const c = tf.scalar(NaN);
const a = tf.tensor1d([1, 2, 3]);
const res = tf.add(c, a);
expectArraysEqual(await res.data(), [NaN, NaN, NaN]);
});
it('A + B broadcasting same rank Tensors different shape', async () => {
const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
const b = tf.tensor2d([2, 3], [2, 1]);
const result = tf.add(a, b);
expect(result.shape).toEqual([2, 2]);
const expected = [3, 4, 0, -1];
expectArraysClose(await result.data(), expected);
});
it('A + B broadcast 2D + 1D', async () => {
const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
const b = tf.tensor1d([1, 2]);
const result = tf.add(a, b);
expect(result.shape).toEqual([2, 2]);
const expected = [2, 4, -2, -2];
expectArraysClose(await result.data(), expected);
});
it('A + B', async () => {
const a = tf.tensor1d([2, 5, 1]);
const b = tf.tensor1d([4, 2, -1]);
const result = tf.add(a, b);
const expected = [6, 7, 0];
expectArraysClose(await result.data(), expected);
});
it('TensorLike', async () => {
const a = [2, 5, 1];
const b = [4, 2, -1];
const result = tf.add(a, b);
const expected = [6, 7, 0];
expectArraysClose(await result.data(), expected);
});
it('TensorLike chained', async () => {
const a = tf.tensor1d([2, 5, 1]);
const b = [4, 2, -1];
const result = a.add(b);
const expected = [6, 7, 0];
expectArraysClose(await result.data(), expected);
});
it('A + B propagates NaNs', async () => {
const a = tf.tensor1d([2, 5, NaN]);
const b = tf.tensor1d([4, 2, -1]);
const res = tf.add(a, b);
expectArraysClose(await res.data(), [6, 7, NaN]);
});
it('A + B throws when passed tensors with different shape', () => {
const a = tf.tensor1d([2, 5, 1, 5]);
const b = tf.tensor1d([4, 2, -1]);
expect(() => tf.add(a, b)).toThrowError();
expect(() => tf.add(b, a)).toThrowError();
});
it('2D+scalar broadcast', async () => {
const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const b = tf.scalar(2);
const res = tf.add(a, b);
expect(res.shape).toEqual([2, 3]);
expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
});
it('scalar+1D broadcast', async () => {
const a = tf.scalar(2);
const b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
const res = tf.add(a, b);
expect(res.shape).toEqual([6]);
expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
});
it('2D+2D broadcast each with 1 dim', async () => {
const a = tf.tensor2d([1, 2, 5], [1, 3]);
const b = tf.tensor2d([7, 3], [2, 1]);
const res = tf.add(a, b);
expect(res.shape).toEqual([2, 3]);
expectArraysClose(await res.data(), [8, 9, 12, 4, 5, 8]);
});
it('2D+2D broadcast inner dim of b', async () => {
const a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
const b = tf.tensor2d([7, 3], [2, 1]);
const res = tf.add(a, b);
expect(res.shape).toEqual([2, 3]);
expectArraysClose(await res.data(), [8, 9, 12, 7, 8, 9]);
});
it('3D+scalar', async () => {
const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
const b = tf.scalar(-1);
const res = tf.add(a, b);
expect(res.shape).toEqual([2, 3, 1]);
expectArraysClose(await res.data(), [0, 1, 2, 3, 4, 5]);
});
it('6D+scalar', async () => {
const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
const b = tf.scalar(-1);
const res = tf.add(a, b);
expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
const expectedResult = [
-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
];
expectArraysClose(await res.data(), expectedResult);
});
it('6D+2D', async () => {
const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
const b = tf.tensor2d([11, 13, 17, 19], [2, 2]);
const res = tf.add(a, b);
expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
const expectedResult = [
11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,
27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,
43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,
59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82
];
expectArraysClose(await res.data(), expectedResult);
});
it('add tensors with 0 in shape', async () => {
const a = tf.tensor1d([1]);
const b = tf.tensor3d([], [0, 0, 5]);
const res = tf.add(a, b);
expect(res.shape).toEqual([0, 0, 5]);
expectArraysEqual(await res.data(), []);
});
it('gradient: scalar + 1D broadcast', async () => {
const a = tf.scalar(2);
const b = tf.tensor1d([3, 4, 5]);
const dy = tf.tensor1d([7, 8, 9]);
const grads = tf.grads((a, b) => tf.add(a, b));
const [da, db] = grads([a, b], dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [7 + 8 + 9]);
expect(db.shape).toEqual(b.shape);
expect(db.dtype).toEqual('float32');
expectArraysClose(await db.data(), [7, 8, 9]);
});
it('gradient with clones', async () => {
const a = tf.scalar(2);
const b = tf.tensor1d([3, 4, 5]);
const dy = tf.tensor1d([7, 8, 9]);
const grads = tf.grads((a, b) => tf.add(a.clone(), b.clone()).clone());
const [da, db] = grads([a, b], dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [7 + 8 + 9]);
expect(db.shape).toEqual(b.shape);
expect(db.dtype).toEqual('float32');
expectArraysClose(await db.data(), [7, 8, 9]);
});
it('gradient: 2D + 2D broadcast', async () => {
const a = tf.tensor2d([2, 3], [2, 1]);
const b = tf.tensor2d([4, 5, 6, 7], [2, 2]);
const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
const grads = tf.grads((a, b) => tf.add(a, b));
const [da, db] = grads([a, b], dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [5 + 4, 3 + 2]);
expect(db.shape).toEqual(b.shape);
expect(db.dtype).toEqual('float32');
expectArraysClose(await db.data(), [5, 4, 3, 2]);
});
it('complex number addition', async () => {
const real1 = tf.tensor1d([1]);
const imag1 = tf.tensor1d([2]);
const complex1 = tf.complex(real1, imag1);
const real2 = tf.tensor1d([3]);
const imag2 = tf.tensor1d([4]);
const complex2 = tf.complex(real2, imag2);
const result = complex1.add(complex2);
expect(result.dtype).toBe('complex64');
expect(result.shape).toEqual([1]);
expectArraysClose(await result.data(), [4, 6]);
});
it('complex number reshape and then addition', async () => {
const real1 = tf.tensor1d([1]);
const imag1 = tf.tensor1d([2]);
const complex1 = tf.complex(real1, imag1);
const real2 = tf.tensor1d([3]);
const imag2 = tf.tensor1d([4]);
const complex2 = tf.complex(real2, imag2);
const complex1Reshaped = complex1.reshape([1, 1, 1]);
const complex2Reshaped = complex2.reshape([1, 1, 1]);
const result = complex1Reshaped.add(complex2Reshaped);
expect(result.dtype).toBe('complex64');
expect(result.shape).toEqual([1, 1, 1]);
expectArraysClose(await result.data(), [4, 6]);
});
it('complex number broadcasting addition', async () => {
const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
const complex1 = tf.complex(real1, imag1);
const real2 = tf.tensor1d([4]);
const imag2 = tf.tensor1d([5]);
const complex2 = tf.complex(real2, imag2);
const result = tf.add(complex1, complex2);
expect(result.dtype).toEqual('complex64');
expect(result.shape).toEqual([2, 2]);
expectArraysClose(await result.data(), [1 + 4, 10 + 5, 2 + 4, 20 + 5, -3 + 4, -30 + 5, -4 + 4, -40 + 5]);
});
it('throws when passed a as a non-tensor', () => {
expect(() => tf.add({}, tf.scalar(1)))
.toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);
});
it('throws when passed b as a non-tensor', () => {
expect(() => tf.add(tf.scalar(1), {}))
.toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);
});
it('upcasts when dtypes dont match', async () => {
let res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
expect(res.dtype).toBe('float32');
expectArraysClose(await res.data(), [2]);
res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
expect(res.dtype).toBe('int32');
expectArraysClose(await res.data(), [2]);
res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
expect(res.dtype).toBe('int32');
expectArraysClose(await res.data(), [1]);
res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));
expect(res.dtype).toBe('complex64');
expectArraysClose(await res.data(), [5, 7]);
res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));
expect(res.dtype).toBe('complex64');
expectArraysClose(await res.data(), [5, 7]);
});
it('accepts a tensor-like object', async () => {
const result = tf.add(5, [1, 2, 3]);
expectArraysClose(await result.data(), [6, 7, 8]);
});
});
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