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dropout.ts
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dropout.ts
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/**
* @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 {Tensor} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
import * as util from '../util';
import {add} from './add';
import {div} from './div';
import {getNoiseShape} from './dropout_util';
import {floor} from './floor';
import {mul} from './mul';
import {op} from './operation';
import {randomUniform} from './random_uniform';
/**
* Computes dropout.
*
* ```js
* const x = tf.tensor1d([1, 2, 2, 1]);
* const rate = 0.75;
* const output = tf.dropout(x, rate);
* output.print();
* ```
*
* @param x A floating point Tensor or TensorLike.
* @param rate A float in the range [0, 1). The probability that each element
* of x is discarded.
* @param noiseShape An array of numbers of type int32, representing the
* shape for randomly generated keep/drop flags. If the noiseShape has null
* value, it will be automatically replaced with the x's relative dimension
* size. Optional.
* @param seed Used to create random seeds. Optional.
* @returns A Tensor of the same shape of x.
*
* @doc {heading: 'Operations', subheading: 'Dropout'}
*/
function dropout_(
x: Tensor|TensorLike, rate: number, noiseShape?: number[],
seed?: number|string): Tensor {
const $x = convertToTensor(x, 'x', 'dropout');
util.assert(
$x.dtype === 'float32',
() => `x has to be a floating point tensor since it's going to be ` +
`scaled, but got a ${$x.dtype} tensor instead.`);
util.assert(
rate >= 0 && rate < 1,
() => `rate must be a float in the range [0, 1), but got ${rate}.`);
if (rate === 0) {
return x instanceof Tensor ? $x.clone() : $x;
}
const $noiseShape = getNoiseShape($x, noiseShape);
const keepProb = 1 - rate;
const multiplier = div(
floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)),
keepProb);
return mul($x, multiplier);
}
export const dropout = /* @__PURE__ */ op({dropout_});