@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2017 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('concat1d', ALL_ENVS, () => {
it('3 + 5', async () => {
const a = tf.tensor1d([3]);
const b = tf.tensor1d([5]);
const result = tf.concat1d([a, b]);
const expected = [3, 5];
expectArraysClose(await result.data(), expected);
});
it('TensorLike 3 + 5', async () => {
const a = [3];
const b = [5];
const result = tf.concat1d([a, b]);
const expected = [3, 5];
expectArraysClose(await result.data(), expected);
});
it('TensorLike Chained 3 + 5', async () => {
const a = tf.tensor1d([3]);
const b = [5];
const result = a.concat([b]);
const expected = [3, 5];
expectArraysClose(await result.data(), expected);
});
it('3 + [5,7]', async () => {
const a = tf.tensor1d([3]);
const b = tf.tensor1d([5, 7]);
const result = tf.concat1d([a, b]);
const expected = [3, 5, 7];
expectArraysClose(await result.data(), expected);
});
it('[3,5] + 7', async () => {
const a = tf.tensor1d([3, 5]);
const b = tf.tensor1d([7]);
const result = tf.concat1d([a, b]);
const expected = [3, 5, 7];
expectArraysClose(await result.data(), expected);
});
it('3 + 5 + 7 + 9', async () => {
const a = tf.tensor1d([3]);
const b = tf.tensor1d([5]);
const c = tf.tensor1d([7]);
const d = tf.tensor1d([9]);
const result = tf.concat1d([a, b, c, d]);
expectArraysClose(await result.data(), [3, 5, 7, 9]);
});
it('single tensor', async () => {
const a = tf.tensor1d([3]);
const result = tf.concat1d([a]);
expectArraysClose(await result.data(), [3]);
});
it('accepts a tensor-like object', async () => {
const a = [3];
const b = [5];
const result = tf.concat1d([a, b]);
const expected = [3, 5];
expectArraysClose(await result.data(), expected);
});
it('concat complex input', async () => {
// [1+1j, 2+2j]
const c1 = tf.complex([1, 2], [1, 2]);
// [3+3j, 4+4j]
const c2 = tf.complex([3, 4], [3, 4]);
const axis = 0;
const result = tf.concat([c1, c2], axis);
const expected = [1, 1, 2, 2, 3, 3, 4, 4];
expect(result.dtype).toEqual('complex64');
expectArraysClose(await result.data(), expected);
});
});
describeWithFlags('concat2d', ALL_ENVS, () => {
it('[[3]] + [[5]], axis=0', async () => {
const axis = 0;
const a = tf.tensor2d([3], [1, 1]);
const b = tf.tensor2d([5], [1, 1]);
const result = tf.concat2d([a, b], axis);
const expected = [3, 5];
expect(result.shape).toEqual([2, 1]);
expectArraysClose(await result.data(), expected);
});
it('TensorLike [[3]] + [[5]], axis=0', async () => {
const axis = 0;
const a = [[3]];
const b = [[5]];
const result = tf.concat2d([a, b], axis);
const expected = [3, 5];
expect(result.shape).toEqual([2, 1]);
expectArraysClose(await result.data(), expected);
});
it('TensorLike Chained [[3]] + [[5]], axis=0', async () => {
const axis = 0;
const a = tf.tensor2d([3], [1, 1]);
const b = [[5]];
const result = a.concat([b], axis);
const expected = [3, 5];
expect(result.shape).toEqual([2, 1]);
expectArraysClose(await result.data(), expected);
});
it('[[3]] + [[5]], axis=1', async () => {
const axis = 1;
const a = tf.tensor2d([3], [1, 1]);
const b = tf.tensor2d([5], [1, 1]);
const result = tf.concat2d([a, b], axis);
const expected = [3, 5];
expect(result.shape).toEqual([1, 2]);
expectArraysClose(await result.data(), expected);
});
it('[[1, 2], [3, 4]] + [[5, 6]], axis=0', async () => {
const axis = 0;
const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
const b = tf.tensor2d([[5, 6]], [1, 2]);
const result = tf.concat2d([a, b], axis);
const expected = [1, 2, 3, 4, 5, 6];
expect(result.shape).toEqual([3, 2]);
expectArraysClose(await result.data(), expected);
});
it('[[1, 2],[3, 4]] + [[5, 6]] + [[7, 8]], axis=0', async () => {
const axis = 0;
const a = tf.tensor2d([[1, 2], [3, 4]]);
const b = tf.tensor2d([[5, 6]]);
const c = tf.tensor2d([[7, 8]]);
const result = tf.concat2d([a, b, c], axis);
const expected = [1, 2, 3, 4, 5, 6, 7, 8];
expect(result.shape).toEqual([4, 2]);
expectArraysClose(await result.data(), expected);
});
it('[[1, 2], [3, 4]] + [[5, 6]], axis=1 throws error', () => {
const axis = 1;
const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
const b = tf.tensor2d([[5, 6]], [1, 2]);
expect(() => tf.concat2d([a, b], axis)).toThrowError();
});
it('[[1, 2], [3, 4]] + [[5, 6], [7, 8]], axis=1', async () => {
const axis = 1;
const a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
const b = tf.tensor2d([[5, 6], [7, 8]], [2, 2]);
const result = tf.concat2d([a, b], axis);
const expected = [1, 2, 5, 6, 3, 4, 7, 8];
expect(result.shape).toEqual([2, 4]);
expectArraysClose(await result.data(), expected);
});
it('[[1, 2],[3, 4]] + [[5, 6],[7, 8]] + [[9, 10],[11, 12]], axis=1', async () => {
const axis = 1;
const a = tf.tensor2d([[1, 2], [3, 4]]);
const b = tf.tensor2d([[5, 6], [7, 8]]);
const c = tf.tensor2d([[9, 10], [11, 12]]);
const result = tf.concat2d([a, b, c], axis);
const expected = [1, 2, 5, 6, 9, 10, 3, 4, 7, 8, 11, 12];
expect(result.shape).toEqual([2, 6]);
expectArraysClose(await result.data(), expected);
});
it('accepts a tensor-like object', async () => {
const axis = 0;
const a = [[3]];
const b = [[5]];
const result = tf.concat2d([a, b], axis);
const expected = [3, 5];
expect(result.shape).toEqual([2, 1]);
expectArraysClose(await result.data(), expected);
});
it('concat zero-sized tensors', async () => {
const a = tf.tensor2d([], [0, 5]);
const b = tf.tensor2d([], [0, 5]);
const c = tf.tensor2d([], [0, 5]);
const res = tf.concat([a, b, c], /* axis */ 0);
expect(res.shape).toEqual([0, 5]);
expectArraysEqual(await res.data(), []);
const res2 = tf.concat([a, b, c], /* axis */ 1);
expect(res2.shape).toEqual([0, 15]);
expectArraysEqual(await res2.data(), []);
});
it('concat complex input axis=0', async () => {
// [[1+1j, 2+2j], [3+3j, 4+4j]]
const c1 = tf.complex([[1, 2], [3, 4]], [[1, 2], [3, 4]]);
// [[5+5j, 6+6j], [7+7j, 8+8j]]
const c2 = tf.complex([[5, 6], [7, 8]], [[5, 6], [7, 8]]);
const axis = 0;
const result = tf.concat([c1, c2], axis);
const expected = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8];
expect(result.dtype).toEqual('complex64');
expectArraysClose(await result.data(), expected);
});
it('concat complex input axis=1', async () => {
// [[1+1j, 2+2j], [3+3j, 4+4j]]
const c1 = tf.complex([[1, 2], [3, 4]], [[1, 2], [3, 4]]);
// [[5+5j, 6+6j], [7+7j, 8+8j]]
const c2 = tf.complex([[5, 6], [7, 8]], [[5, 6], [7, 8]]);
const axis = 1;
const result = tf.concat([c1, c2], axis);
const expected = [1, 1, 2, 2, 5, 5, 6, 6, 3, 3, 4, 4, 7, 7, 8, 8];
expect(result.dtype).toEqual('complex64');
expectArraysClose(await result.data(), expected);
});
});
describeWithFlags('concat3d', ALL_ENVS, () => {
beforeAll(() => {
jasmine.DEFAULT_TIMEOUT_INTERVAL = 1000000;
});
it('shapes correct concat axis=-1', async () => {
const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
const values = tf.concat3d([tensor1, tensor2], -1);
expect(values.shape).toEqual([1, 1, 6]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
});
it('shapes correct concat axis=0', async () => {
const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
const values = tf.concat3d([tensor1, tensor2], 0);
expect(values.shape).toEqual([2, 1, 3]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
});
it('concat axis=0', async () => {
const tensor1 = tf.tensor3d([1, 11, 111, 2, 22, 222], [1, 2, 3]);
const tensor2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
const values = tf.concat3d([tensor1, tensor2], 0);
expect(values.shape).toEqual([3, 2, 3]);
expectArraysClose(await values.data(), [
1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
]);
});
it('TensorLike concat axis=0', async () => {
const tensor1 = [[[1, 11, 111], [2, 22, 222]]];
const tensor2 = [[[5, 55, 555], [6, 66, 666]], [[7, 77, 777], [8, 88, 888]]];
const values = tf.concat3d([tensor1, tensor2], 0);
expect(values.shape).toEqual([3, 2, 3]);
expectArraysClose(await values.data(), [
1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
]);
});
it('Accepts string tensor.', async () => {
const tensor1 = tf.tensor3d(['one', 'two', 'three'], [1, 1, 3], 'string');
const tensor2 = tf.tensor3d(['four', 'five', 'six'], [1, 1, 3], 'string');
const values = tf.concat3d([tensor1, tensor2], 0);
expect(values.shape).toEqual([2, 1, 3]);
expectArraysClose(await values.data(), ['one', 'two', 'three', 'four', 'five', 'six']);
});
it('TensorLike Chained concat axis=0', async () => {
const tensor1 = tf.tensor3d([1, 11, 111, 2, 22, 222], [1, 2, 3]);
const tensor2 = [[[5, 55, 555], [6, 66, 666]], [[7, 77, 777], [8, 88, 888]]];
const values = tensor1.concat([tensor2], 0);
expect(values.shape).toEqual([3, 2, 3]);
expectArraysClose(await values.data(), [
1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
]);
});
it('shapes correct concat axis=1', async () => {
const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
const values = tf.concat3d([tensor1, tensor2], 1);
expect(values.shape).toEqual([1, 2, 3]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
});
it('concat axis=1', async () => {
const tensor1 = tf.tensor3d([1, 11, 111, 3, 33, 333], [2, 1, 3]);
const tensor2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
const values = tf.concat3d([tensor1, tensor2], 1);
expect(values.shape).toEqual([2, 3, 3]);
expectArraysClose(await values.data(), [
1, 11, 111, 5, 55, 555, 6, 66, 666, 3, 33, 333, 7, 77, 777, 8, 88, 888
]);
});
it('shapes correct concat axis=2', async () => {
const tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
const tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
const values = tf.concat3d([tensor1, tensor2], 2);
expect(values.shape).toEqual([1, 1, 6]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
});
it('concat a large number of tensors, axis=0', async () => {
const tensors = [];
const expected = [];
for (let i = 0; i < 100; i++) {
tensors.push(tf.tensor([i], [1]));
expected.push(i);
}
const axis = 0;
const res = tf.concat(tensors, axis);
expect(res.shape).toEqual([100]);
expect(res.dtype).toBe('float32');
expectArraysClose(await res.data(), expected);
});
it('concat a large number of tensors, axis=1', async () => {
const tensors = [];
const expected = [];
for (let i = 0; i < 100; i++) {
tensors.push(tf.tensor([i], [1, 1]));
expected.push(i);
}
const axis = 1;
const res = tf.concat(tensors, axis);
expect(res.shape).toEqual([1, 100]);
expect(res.dtype).toBe('float32');
expectArraysClose(await res.data(), expected);
});
it('concat axis=2', async () => {
const tensor1 = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
const tensor2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
const values = tf.concat3d([tensor1, tensor2], 2);
expect(values.shape).toEqual([2, 2, 5]);
expectArraysClose(await values.data(), [
1, 11, 5, 55, 555, 2, 22, 6, 66, 666,
3, 33, 7, 77, 777, 4, 44, 8, 88, 888
]);
});
it('concat throws when invalid non-axis shapes, axis=0', () => {
const axis = 0;
const x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
const x2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
});
it('concat throws when invalid non-axis shapes, axis=1', () => {
const axis = 1;
const x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
const x2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
});
it('concat throws when invalid non-axis shapes and zero size, axis=1', () => {
const axis = 1;
const x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
const x2 = tf.tensor3d([], [1, 0, 4]);
expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
});
it('concat throws when invalid non-axis shapes, axis=2', () => {
const axis = 2;
const x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
const x2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
expect(() => tf.concat3d([x1, x2], axis)).toThrowError();
});
it('gradient concat axis=0', async () => {
const x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
const dy = tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [3, 2, 2]);
const axis = 0;
const grads = tf.grads((x1, x2) => tf.concat3d([x1, x2], axis));
const [dx1, dx2] = grads([x1, x2], dy);
expect(dx1.shape).toEqual(x1.shape);
expectArraysClose(await dx1.data(), [66, 6, 55, 5]);
expect(dx2.shape).toEqual(x2.shape);
expectArraysClose(await dx2.data(), [44, 4, 33, 3, 22, 2, 11, 1]);
});
it('gradient with clones', async () => {
const x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
const dy = tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [3, 2, 2]);
const axis = 0;
const grads = tf.grads((x1, x2) => tf.concat3d([x1.clone(), x2.clone()], axis).clone());
const [dx1, dx2] = grads([x1, x2], dy);
expect(dx1.shape).toEqual(x1.shape);
expectArraysClose(await dx1.data(), [66, 6, 55, 5]);
expect(dx2.shape).toEqual(x2.shape);
expectArraysClose(await dx2.data(), [44, 4, 33, 3, 22, 2, 11, 1]);
});
it('gradient concat axis=1', async () => {
const x1 = tf.tensor3d([1, 11, 2, 22], [2, 1, 2]);
const x2 = tf.tensor3d([3, 33, 4, 44, 5, 55, 6, 66], [2, 2, 2]);
const dy = tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [2, 3, 2]);
const axis = 1;
const grads = tf.grads((x1, x2) => tf.concat3d([x1, x2], axis));
const [dx1, dx2] = grads([x1, x2], dy);
expect(dx1.shape).toEqual(x1.shape);
expectArraysClose(await dx1.data(), [66, 6, 33, 3]);
expect(dx2.shape).toEqual(x2.shape);
expectArraysClose(await dx2.data(), [55, 5, 44, 4, 22, 2, 11, 1]);
});
it('gradient concat axis=2', async () => {
const x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
const dy = tf.tensor3d([4, 40, 400, 3, 30, 300, 2, 20, 200, 1, 10, 100], [2, 2, 3]);
const axis = 2;
const grads = tf.grads((x1, x2) => tf.concat3d([x1, x2], axis));
const [dx1, dx2] = grads([x1, x2], dy);
expect(dx1.shape).toEqual(x1.shape);
expectArraysClose(await dx1.data(), [4, 3, 2, 1]);
expect(dx2.shape).toEqual(x2.shape);
expectArraysClose(await dx2.data(), [40, 400, 30, 300, 20, 200, 10, 100]);
});
it('gradient concat axis=-1', async () => {
const x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
const x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
const dy = tf.tensor3d([4, 40, 400, 3, 30, 300, 2, 20, 200, 1, 10, 100], [2, 2, 3]);
const axis = -1;
const grads = tf.grads((x1, x2) => tf.concat3d([x1, x2], axis));
const [dx1, dx2] = grads([x1, x2], dy);
expect(dx1.shape).toEqual(x1.shape);
expectArraysClose(await dx1.data(), [4, 3, 2, 1]);
expect(dx2.shape).toEqual(x2.shape);
expectArraysClose(await dx2.data(), [40, 400, 30, 300, 20, 200, 10, 100]);
});
it('accepts a tensor-like object', async () => {
const tensor1 = [[[1, 2, 3]]]; // 1x1x3
const tensor2 = [[[4, 5, 6]]]; // 1x1x3
const values = tf.concat3d([tensor1, tensor2], 0);
expect(values.shape).toEqual([2, 1, 3]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
});
it('concat tensors with 0 in their shape', async () => {
const tensor1 = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
const tensor2 = tf.tensor3d([], [0, 3, 1]);
const values = tf.concat3d([tensor1, tensor2], 0);
expect(values.shape).toEqual([2, 3, 1]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 5, 6]);
});
it('concat complex input axis=0', async () => {
// [[[1+1j, 2+2j], [3+3j, 4+4j], [5+5j, 6+6j]]]
const c1 = tf.complex([[[1, 2], [3, 4], [5, 6]]], [[[1, 2], [3, 4], [5, 6]]]);
// [[[7+7j, 8+8j], [9+9j, 10+10j], [11+11j, 12+12j]]]
const c2 = tf.complex([[[7, 8], [9, 10], [11, 12]]], [[[7, 8], [9, 10], [11, 12]]]);
const axis = 0;
const result = tf.concat([c1, c2], axis);
const expected = [
1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6,
7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12
];
expect(result.dtype).toEqual('complex64');
expectArraysClose(await result.data(), expected);
});
it('concat complex input axis=1', async () => {
// [[[1+1j, 2+2j], [3+3j, 4+4j], [5+5j, 6+6j]]]
const c1 = tf.complex([[[1, 2], [3, 4], [5, 6]]], [[[1, 2], [3, 4], [5, 6]]]);
// [[[7+7j, 8+8j], [9+9j, 10+10j], [11+11j, 12+12j]]]
const c2 = tf.complex([[[7, 8], [9, 10], [11, 12]]], [[[7, 8], [9, 10], [11, 12]]]);
const axis = 1;
const result = tf.concat([c1, c2], axis);
const expected = [
1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6,
7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12
];
expect(result.dtype).toEqual('complex64');
expectArraysClose(await result.data(), expected);
});
it('concat complex input axis=1', async () => {
// [[[1+1j, 2+2j], [3+3j, 4+4j], [5+5j, 6+6j]]]
const c1 = tf.complex([[[1, 2], [3, 4], [5, 6]]], [[[1, 2], [3, 4], [5, 6]]]);
// [[[7+7j, 8+8j], [9+9j, 10+10j], [11+11j, 12+12j]]]
const c2 = tf.complex([[[7, 8], [9, 10], [11, 12]]], [[[7, 8], [9, 10], [11, 12]]]);
const axis = 2;
const result = tf.concat([c1, c2], axis);
const expected = [
1, 1, 2, 2, 7, 7, 8, 8, 3, 3, 4, 4,
9, 9, 10, 10, 5, 5, 6, 6, 11, 11, 12, 12
];
expect(result.dtype).toEqual('complex64');
expectArraysClose(await result.data(), expected);
});
});
describeWithFlags('concat throws for non-tensors', ALL_ENVS, () => {
it('throws when passed a non-tensor', () => {
expect(() => tf.concat([{}]))
.toThrowError(/Argument 'tensors\[0\]' passed to 'concat' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const tensor1 = [[[1, 2, 3, 4]]]; // 1x1x4
const tensor2 = [[[4, 5, 6, 7]]]; // 1x1x4
const values = tf.concat([tensor1, tensor2], 0);
expect(values.shape).toEqual([2, 1, 4]);
expectArraysClose(await values.data(), [1, 2, 3, 4, 4, 5, 6, 7]);
});
});
describeWithFlags('memory test', ALL_ENVS, () => {
it('returns a new tensor when op is effectively a no-op.', async () => {
const a = tf.tensor1d([]);
const b = tf.tensor1d([3]);
const result = tf.concat([a, b]);
a.dispose();
b.dispose();
expectArraysClose(await result.data(), [3]);
});
it('ensure no memory leak', async () => {
const numTensorsBefore = tf.memory().numTensors;
const numDataIdBefore = tf.engine().backend.numDataIds();
const a = tf.tensor1d([]);
const b = tf.tensor1d([3]);
const result = tf.concat([a, b]);
a.dispose();
b.dispose();
result.dispose();
const numTensorsAfter = tf.memory().numTensors;
const numDataIdAfter = tf.engine().backend.numDataIds();
expect(numTensorsAfter).toBe(numTensorsBefore);
expect(numDataIdAfter).toBe(numDataIdBefore);
});
});
//# 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