@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2018 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 } from '../test_util';
describeWithFlags('greaterEqual', ALL_ENVS, () => {
// Tensor1D:
it('Tensor1D - int32', async () => {
let a = tf.tensor1d([1, 4, 5], 'int32');
let b = tf.tensor1d([2, 3, 5], 'int32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 1]);
a = tf.tensor1d([2, 2, 2], 'int32');
b = tf.tensor1d([2, 2, 2], 'int32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1]);
a = tf.tensor1d([0, 0], 'int32');
b = tf.tensor1d([3, 3], 'int32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 0]);
});
it('Tensor1D - float32', async () => {
let a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
let b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 1]);
a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1]);
a = tf.tensor1d([0.45, 0.123], 'float32');
b = tf.tensor1d([3.123, 3.321], 'float32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 0]);
});
it('upcasts when dtypes dont match', async () => {
const a = [1.1, 4.1, 5];
const b = [2.2, 3.2, 5];
let res = tf.greaterEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([3]);
expectArraysClose(await res.data(), [0, 1, 1]);
res =
tf.greaterEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
expect(res.dtype).toBe('bool');
expect(res.shape).toEqual([3]);
expectArraysClose(await res.data(), [1, 1, 1]);
});
it('mismatched Tensor1D shapes - int32', () => {
const a = tf.tensor1d([1, 2], 'int32');
const b = tf.tensor1d([1, 2, 3], 'int32');
const f = () => {
tf.greaterEqual(a, b);
};
expect(f).toThrowError();
});
it('mismatched Tensor1D shapes - float32', () => {
const a = tf.tensor1d([1.1, 2.1], 'float32');
const b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
const f = () => {
tf.greaterEqual(a, b);
};
expect(f).toThrowError();
});
it('NaNs in Tensor1D - float32', async () => {
const a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
const b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 0, 0]);
});
// Tensor2D:
it('Tensor2D - int32', async () => {
let a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
let b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1, 1]);
});
it('Tensor2D - float32', async () => {
let a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
let b = tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1, 1]);
});
it('broadcasting Tensor2D shapes - int32', async () => {
const a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
const b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 0, 1, 0, 0]);
});
it('broadcasting Tensor2D shapes - float32', async () => {
const a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
const b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 0, 1, 0, 0]);
});
it('NaNs in Tensor2D - float32', async () => {
const a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
const b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 0, 0]);
});
// Tensor3D:
it('Tensor3D - int32', async () => {
let a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
let b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1]);
});
it('Tensor3D - float32', async () => {
let a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
let b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 0, 1, 0, 1]);
a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1, 1, 1, 1]);
});
it('broadcasting Tensor3D shapes - int32', async () => {
const a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
const b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1]);
});
it('broadcasting Tensor3D shapes - float32', async () => {
const a = tf.tensor3d([
[[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
[[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
], [2, 3, 2], 'float32');
const b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1]);
});
it('NaNs in Tensor3D - float32', async () => {
const a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
const b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 1, 0, 1, 0]);
});
// Tensor4D:
it('Tensor4D - int32', async () => {
let a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
let b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 0, 1]);
a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1, 1]);
a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 0, 0, 0]);
});
it('Tensor4D - float32', async () => {
let a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
let b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
let res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 0, 1]);
a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 1, 1, 1]);
a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 0, 0, 0]);
});
it('broadcasting Tensor4D shapes - int32', async () => {
const a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
const b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 0, 0, 1, 0, 1, 1]);
});
it('broadcasting Tensor4D shapes - float32', async () => {
const a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
const b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 0, 0, 1, 0, 1, 1]);
});
it('NaNs in Tensor4D - float32', async () => {
const a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
const b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [1, 0, 0, 0]);
});
it('throws when passed a as a non-tensor', () => {
expect(() => tf.greaterEqual({}, tf.scalar(1)))
.toThrowError(/Argument 'a' passed to 'greaterEqual' must be a Tensor/);
});
it('throws when passed b as a non-tensor', () => {
expect(() => tf.greaterEqual(tf.scalar(1), {}))
.toThrowError(/Argument 'b' passed to 'greaterEqual' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const a = [1, 4, 5];
const b = [2, 3, 5];
const res = tf.greaterEqual(a, b);
expect(res.dtype).toBe('bool');
expectArraysClose(await res.data(), [0, 1, 1]);
});
it('has gradient', async () => {
const a = tf.tensor1d([3, 2, 5]);
const b = tf.tensor1d([4, 1, 5]);
const dy = tf.ones([3], 'float32');
const da = tf.grad((a) => tf.greaterEqual(a, b))(a, dy);
expect(da.dtype).toBe('float32');
expect(da.shape).toEqual([3]);
expectArraysClose(await da.data(), [0, 0, 0]);
});
it('gradient with clones', async () => {
const a = tf.tensor1d([3, 2, 5]);
const b = tf.tensor1d([4, 1, 5]);
const dy = tf.ones([3], 'float32');
const da = tf.grad((a) => tf.greaterEqual(a.clone(), b.clone()).clone())(a, dy);
expect(da.dtype).toBe('float32');
expect(da.shape).toEqual([3]);
expectArraysClose(await da.data(), [0, 0, 0]);
});
});
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