"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_util_env_1 = require("../tensor_util_env");
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var operation_1 = require("./operation");
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/**
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* Computes the next states and outputs of a stack of LSTMCells.
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*
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* Each cell output is used as input to the next cell.
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*
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* Returns `[cellState, cellOutput]`.
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*
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* Derived from tf.contrib.rn.MultiRNNCell.
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*
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* @param lstmCells Array of LSTMCell functions.
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* @param data The input to the cell.
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* @param c Array of previous cell states.
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* @param h Array of previous cell outputs.
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*/
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/** @doc {heading: 'Operations', subheading: 'RNN'} */
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function multiRNNCell_(lstmCells, data, c, h) {
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var $data = tensor_util_env_1.convertToTensor(data, 'data', 'multiRNNCell');
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var $c = tensor_util_env_1.convertToTensorArray(c, 'c', 'multiRNNCell');
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var $h = tensor_util_env_1.convertToTensorArray(h, 'h', 'multiRNNCell');
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var input = $data;
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var newStates = [];
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for (var i = 0; i < lstmCells.length; i++) {
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var output = lstmCells[i](input, $c[i], $h[i]);
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newStates.push(output[0]);
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newStates.push(output[1]);
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input = output[1];
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}
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var newC = [];
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var newH = [];
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for (var i = 0; i < newStates.length; i += 2) {
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newC.push(newStates[i]);
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newH.push(newStates[i + 1]);
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}
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return [newC, newH];
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}
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/**
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* Computes the next state and output of a BasicLSTMCell.
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*
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* Returns `[newC, newH]`.
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*
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* Derived from tf.contrib.rnn.BasicLSTMCell.
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*
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* @param forgetBias Forget bias for the cell.
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* @param lstmKernel The weights for the cell.
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* @param lstmBias The bias for the cell.
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* @param data The input to the cell.
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* @param c Previous cell state.
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* @param h Previous cell output.
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*/
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/** @doc {heading: 'Operations', subheading: 'RNN'} */
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function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
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var $forgetBias = tensor_util_env_1.convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');
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var $lstmKernel = tensor_util_env_1.convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');
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var $lstmBias = tensor_util_env_1.convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');
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var $data = tensor_util_env_1.convertToTensor(data, 'data', 'basicLSTMCell');
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var $c = tensor_util_env_1.convertToTensor(c, 'c', 'basicLSTMCell');
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var $h = tensor_util_env_1.convertToTensor(h, 'h', 'basicLSTMCell');
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var combined = $data.concat($h, 1);
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var weighted = combined.matMul($lstmKernel);
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var res = weighted.add($lstmBias);
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// i = input_gate, j = new_input, f = forget_gate, o = output_gate
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var batchSize = res.shape[0];
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var sliceCols = res.shape[1] / 4;
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var sliceSize = [batchSize, sliceCols];
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var i = res.slice([0, 0], sliceSize);
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var j = res.slice([0, sliceCols], sliceSize);
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var f = res.slice([0, sliceCols * 2], sliceSize);
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var o = res.slice([0, sliceCols * 3], sliceSize);
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var newC = i.sigmoid().mulStrict(j.tanh()).addStrict($c.mulStrict($forgetBias.add(f).sigmoid()));
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var newH = newC.tanh().mulStrict(o.sigmoid());
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return [newC, newH];
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}
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exports.basicLSTMCell = operation_1.op({ basicLSTMCell_: basicLSTMCell_ });
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exports.multiRNNCell = operation_1.op({ multiRNNCell_: multiRNNCell_ });
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//# sourceMappingURL=lstm.js.map
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