@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @license
* Copyright 2018 Google Inc. 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.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var util_1 = require("../util");
var array_ops_1 = require("./array_ops");
var binary_ops_1 = require("./binary_ops");
var operation_1 = require("./operation");
/**
* Sets entries in `x` to zero at random, while scaling the entire tensor.
* ```js
* const x = tf.range(1, 21).reshape([10, 2]);
* const rate = 0.5;
* const seed = 23;
* const noiseShape = null || x.shape;
* const tensor = tf.dropout(x, rate, noiseShape, seed);
* ```
* @param x input tensor.
* @param level fraction of the entries in the tensor that will be set to 0.
* @param noiseShape shape of randomly generated keep/drop flags, must be
* broadcastable to the shape of `x`.
* @param seed random seed to ensure determinism.
* @returns Result of the dropout operation.
*/
function dropout_(x, rate, noiseShape, seed) {
if (noiseShape != null && !util_1.arraysEqual(x.shape, noiseShape)) {
// TODO(VariableVasasMT): implement non default noise shape
throw new Error('Non-default noise shape is not implemented yet: ' +
JSON.stringify(noiseShape));
}
var multiplier = array_ops_1.randomUniform(x.shape, 0, 1, 'float32', seed).greater(rate);
// Scale the kept elements, so the expected sum is unchanged.
multiplier = multiplier.div(binary_ops_1.sub(1, rate));
return x.mul(multiplier);
}
exports.dropout = operation_1.op({ dropout_: dropout_ });
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