@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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text/typescript
/**
* @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_});