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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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/** * @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 {Tensor2D} from '../tensor'; import {convertToTensor, convertToTensorArray} from '../tensor_util_env'; import {TensorLike} from '../types'; import {op} from './operation'; /** * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D] */ export type LSTMCellFunc = { (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]; }; /** * Computes the next states and outputs of a stack of LSTMCells. * * Each cell output is used as input to the next cell. * * Returns `[cellState, cellOutput]`. * * Derived from tf.contrib.rn.MultiRNNCell. * * @param lstmCells Array of LSTMCell functions. * @param data The input to the cell. * @param c Array of previous cell states. * @param h Array of previous cell outputs. * * @doc {heading: 'Operations', subheading: 'RNN'} */ function multiRNNCell_( lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike, c: Array<Tensor2D|TensorLike>, h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] { const $data = convertToTensor(data, 'data', 'multiRNNCell'); const $c = convertToTensorArray(c, 'c', 'multiRNNCell'); const $h = convertToTensorArray(h, 'h', 'multiRNNCell'); let input = $data; const newStates = []; for (let i = 0; i < lstmCells.length; i++) { const output = lstmCells[i](input, $c[i], $h[i]); newStates.push(output[0]); newStates.push(output[1]); input = output[1]; } const newC: Tensor2D[] = []; const newH: Tensor2D[] = []; for (let i = 0; i < newStates.length; i += 2) { newC.push(newStates[i]); newH.push(newStates[i + 1]); } return [newC, newH]; } export const multiRNNCell = op({multiRNNCell_});