@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 } from '../test_util';
describeWithFlags('leakyrelu', ALL_ENVS, () => {
it('basic', async () => {
const a = tf.tensor1d([0, 1, -2]);
const result = tf.leakyRelu(a);
expect(result.shape).toEqual(a.shape);
expectArraysClose(await result.data(), [0, 1, -0.4]);
});
it('propagates NaN', async () => {
const a = tf.tensor1d([0, 1, NaN]);
const result = tf.leakyRelu(a);
expect(result.shape).toEqual(a.shape);
expectArraysClose(await result.data(), [0, 1, NaN]);
});
it('gradients: Scalar', async () => {
const a = tf.scalar(-4);
const dy = tf.scalar(8);
const alpha = 0.1;
const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [8 * alpha]);
});
it('gradient with clones', async () => {
const a = tf.scalar(-4);
const dy = tf.scalar(8);
const alpha = 0.1;
const gradients = tf.grad((a) => tf.leakyRelu(a.clone(), alpha).clone())(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [8 * alpha]);
});
it('gradients: Tensor1D', async () => {
const aValues = [1, -1, 0.1];
const dyValues = [1, 2, 3];
const alpha = 0.1;
const a = tf.tensor1d(aValues);
const dy = tf.tensor1d(dyValues);
const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [1, 2 * alpha, 3]);
});
it('gradients: Tensor2D', async () => {
const aValues = [1, -1, 0.1, 0.5];
const dyValues = [1, 2, 3, 4];
const alpha = 0.1;
const a = tf.tensor2d(aValues, [2, 2]);
const dy = tf.tensor2d(dyValues, [2, 2]);
const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [1, 2 * alpha, 3, 4]);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.leakyRelu({}))
.toThrowError(/Argument 'x' passed to 'leakyRelu' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const result = tf.leakyRelu([0, 1, -2]);
expect(result.shape).toEqual([3]);
expectArraysClose(await result.data(), [0, 1, -0.4]);
});
it('throws for string tensor', () => {
expect(() => tf.leakyRelu('q'))
.toThrowError(/Argument 'x' passed to 'leakyRelu' must be numeric/);
});
});
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