@tensorflow/tfjs-core/dist/ops/mean_test.js

/**
 * @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('mean', ALL_ENVS, () => {
    it('basic', async () => {
        const a = tf.tensor2d([
            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
        ], [16, 2]);
        const r = tf.mean(a);
        expect(r.dtype).toBe('float32');
        expectArraysClose(await r.data(), 15.5);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
        const r = tf.mean(a);
        expect(r.dtype).toBe('float32');
        expectArraysEqual(await r.data(), NaN);
    });
    it('mean(int32) => float32', async () => {
        const a = tf.tensor1d([1, 5, 7, 3], 'int32');
        const r = tf.mean(a);
        expect(r.dtype).toBe('float32');
        expectArraysClose(await r.data(), 4);
    });
    it('mean(bool) => float32', async () => {
        const a = tf.tensor1d([true, false, false, true, true], 'bool');
        const r = tf.mean(a);
        expect(r.dtype).toBe('float32');
        expectArraysClose(await r.data(), 3 / 5);
    });
    it('2D array with keep dim', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.mean(a, null, true /* keepDims */);
        expect(res.shape).toEqual([1, 1]);
        expect(res.dtype).toBe('float32');
        expectArraysClose(await res.data(), [7 / 6]);
    });
    it('axis=0 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.mean(a, [0]);
        expect(res.shape).toEqual([2]);
        expect(res.dtype).toBe('float32');
        expectArraysClose(await res.data(), [4 / 3, 1]);
    });
    it('axis=0 in 2D array, keepDims', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.mean(a, [0], true /* keepDims */);
        expect(res.shape).toEqual([1, 2]);
        expect(res.dtype).toBe('float32');
        expectArraysClose(await res.data(), [4 / 3, 1]);
    });
    it('axis=1 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.mean(a, [1]);
        expect(res.dtype).toBe('float32');
        expect(res.shape).toEqual([3]);
        expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);
    });
    it('axis = -1 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.mean(a, [-1]);
        expect(res.dtype).toBe('float32');
        expect(res.shape).toEqual([3]);
        expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);
    });
    it('2D, axis=1 provided as number', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
        const res = tf.mean(a, 1);
        expect(res.shape).toEqual([2]);
        expect(res.dtype).toBe('float32');
        expectArraysClose(await res.data(), [2, 1 / 3]);
    });
    it('axis=0,1 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.mean(a, [0, 1]);
        expect(res.shape).toEqual([]);
        expect(res.dtype).toBe('float32');
        expectArraysClose(await res.data(), [7 / 6]);
    });
    it('gradients', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const dy = tf.scalar(1.5);
        const da = tf.grad(a => a.mean())(a, dy);
        const dyVal = await dy.array();
        expect(da.shape).toEqual(a.shape);
        expectArraysClose(await da.data(), [
            dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,
            dyVal / a.size, dyVal / a.size
        ]);
    });
    it('gradient with clones', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const dy = tf.scalar(1.5);
        const da = tf.grad(a => a.clone().mean().clone())(a, dy);
        const dyVal = await dy.array();
        expect(da.shape).toEqual(a.shape);
        expectArraysClose(await da.data(), [
            dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,
            dyVal / a.size, dyVal / a.size
        ]);
    });
    it('gradients throws for defined axis', () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const dy = tf.scalar(1.5);
        expect(() => tf.grad(a => a.mean(1))(a, dy)).toThrowError();
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.mean({}))
            .toThrowError(/Argument 'x' passed to 'mean' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const r = tf.mean([[1, 2, 3], [0, 0, 1]]);
        expect(r.dtype).toBe('float32');
        expectArraysClose(await r.data(), 7 / 6);
    });
    it('throws error for string tensor', () => {
        expect(() => tf.mean(['a']))
            .toThrowError(/Argument 'x' passed to 'mean' must be numeric tensor/);
    });
});
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