@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';
import * as selu_util from './selu_util';
describeWithFlags('selu', ALL_ENVS, () => {
const scaleAlpha = selu_util.SELU_SCALEALPHA;
const scale = selu_util.SELU_SCALE;
it('calculate selu', async () => {
const a = tf.tensor1d([1, -1, 0]);
const result = tf.selu(a);
expect(result.shape).toEqual(a.shape);
expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);
});
it('selu propagates NaN', async () => {
const a = tf.tensor1d([1, NaN]);
const result = tf.selu(a);
expect(result.shape).toEqual(a.shape);
expectArraysClose(await result.data(), [1.0507, NaN]);
});
it('gradients: Scalar', async () => {
let aValue = 1;
let dyValue = 1;
let a = tf.scalar(aValue);
let dy = tf.scalar(dyValue);
let gradients = tf.grad(a => tf.selu(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [dyValue * scale]);
aValue = -1;
dyValue = 2;
a = tf.scalar(aValue);
dy = tf.scalar(dyValue);
gradients = tf.grad(a => tf.selu(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [dyValue * scaleAlpha * Math.exp(aValue)]);
});
it('gradient with clones', async () => {
const aValue = 1;
const dyValue = 1;
const a = tf.scalar(aValue);
const dy = tf.scalar(dyValue);
const gradients = tf.grad(a => tf.selu(a.clone()).clone())(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [dyValue * scale]);
});
it('gradients: Tensor1D', async () => {
const aValues = [1, -1, 0];
const dyValues = [1, 2, 3];
const a = tf.tensor1d(aValues);
const dy = tf.tensor1d(dyValues);
const gradients = tf.grad(a => tf.selu(a))(a, dy);
const expected = [];
for (let i = 0; i < a.size; i++) {
if (aValues[i] > 0) {
expected[i] = dyValues[i] * scale;
}
else {
expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
}
}
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), expected);
});
it('gradients: Tensor2D', async () => {
const aValues = [1, -1, 0, 0.5];
const dyValues = [1, 2, 3, 4];
const a = tf.tensor2d(aValues, [2, 2]);
const dy = tf.tensor2d(dyValues, [2, 2]);
const gradients = tf.grad(a => tf.selu(a))(a, dy);
const expected = [];
for (let i = 0; i < a.size; i++) {
if (aValues[i] > 0) {
expected[i] = dyValues[i] * scale;
}
else {
expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
}
}
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), expected);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.selu({}))
.toThrowError(/Argument 'x' passed to 'selu' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const result = tf.selu([1, -1, 0]);
expect(result.shape).toEqual([3]);
expectArraysClose(await result.data(), [1.0507, -1.1113, 0]);
});
it('throws for string tensor', () => {
expect(() => tf.selu('q'))
.toThrowError(/Argument 'x' passed to 'selu' must be numeric/);
});
});
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