"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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var tensor_1 = require("../tensor");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var array_ops_1 = require("./array_ops");
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var dropout_util_1 = require("./dropout_util");
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var operation_1 = require("./operation");
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/**
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* Computes dropout.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 2, 1]);
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* const rate = 0.75;
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* const output = tf.dropout(x, rate);
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* output.print();
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* ```
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*
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* @param x A floating point Tensor or TensorLike.
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* @param rate A float in the range [0, 1). The probability that each element
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* of x is discarded.
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* @param noiseShape An array of numbers of type int32, representing the
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* shape for randomly generated keep/drop flags. If the noiseShape has null
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* value, it will be automatically replaced with the x's relative dimension
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* size. Optional.
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* @param seed Used to create random seeds. Optional.
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* @returns A Tensor of the same shape of x.
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*/
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/** @doc {heading: 'Operations', subheading: 'Dropout'} */
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function dropout_(x, rate, noiseShape, seed) {
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'dropout');
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util.assert($x.dtype === 'float32', function () { return "x has to be a floating point tensor since it's going to be " +
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("scaled, but got a " + $x.dtype + " tensor instead."); });
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util.assert(rate >= 0 && rate < 1, function () { return "rate must be a float in the range [0, 1), but got " + rate + "."; });
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if (rate === 0) {
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return x instanceof tensor_1.Tensor ? $x.clone() : $x;
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}
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var $noiseShape = dropout_util_1.getNoiseShape($x, noiseShape);
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var keepProb = 1 - rate;
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var multiplier = array_ops_1.randomUniform($noiseShape, 0, 1, 'float32', seed)
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.add(keepProb)
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.floor()
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.div(keepProb);
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return $x.mul(multiplier);
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}
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exports.dropout = operation_1.op({ dropout_: dropout_ });
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//# sourceMappingURL=dropout.js.map
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