@tensorflow-models/coco-ssd
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Object detection model (coco-ssd) in TensorFlow.js
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JavaScript
"use strict";
/**
* @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 tensor_util_env_1 = require("../tensor_util_env");
var operation_1 = require("./operation");
/**
* 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, data, c, h) {
var $data = tensor_util_env_1.convertToTensor(data, 'data', 'multiRNNCell');
var $c = tensor_util_env_1.convertToTensorArray(c, 'c', 'multiRNNCell');
var $h = tensor_util_env_1.convertToTensorArray(h, 'h', 'multiRNNCell');
var input = $data;
var newStates = [];
for (var i = 0; i < lstmCells.length; i++) {
var output = lstmCells[i](input, $c[i], $h[i]);
newStates.push(output[0]);
newStates.push(output[1]);
input = output[1];
}
var newC = [];
var newH = [];
for (var i = 0; i < newStates.length; i += 2) {
newC.push(newStates[i]);
newH.push(newStates[i + 1]);
}
return [newC, newH];
}
/**
* Computes the next state and output of a BasicLSTMCell.
*
* Returns `[newC, newH]`.
*
* Derived from tf.contrib.rnn.BasicLSTMCell.
*
* @param forgetBias Forget bias for the cell.
* @param lstmKernel The weights for the cell.
* @param lstmBias The bias for the cell.
* @param data The input to the cell.
* @param c Previous cell state.
* @param h Previous cell output.
*/
/** @doc {heading: 'Operations', subheading: 'RNN'} */
function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
var $forgetBias = tensor_util_env_1.convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');
var $lstmKernel = tensor_util_env_1.convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');
var $lstmBias = tensor_util_env_1.convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');
var $data = tensor_util_env_1.convertToTensor(data, 'data', 'basicLSTMCell');
var $c = tensor_util_env_1.convertToTensor(c, 'c', 'basicLSTMCell');
var $h = tensor_util_env_1.convertToTensor(h, 'h', 'basicLSTMCell');
var combined = $data.concat($h, 1);
var weighted = combined.matMul($lstmKernel);
var res = weighted.add($lstmBias);
// i = input_gate, j = new_input, f = forget_gate, o = output_gate
var batchSize = res.shape[0];
var sliceCols = res.shape[1] / 4;
var sliceSize = [batchSize, sliceCols];
var i = res.slice([0, 0], sliceSize);
var j = res.slice([0, sliceCols], sliceSize);
var f = res.slice([0, sliceCols * 2], sliceSize);
var o = res.slice([0, sliceCols * 3], sliceSize);
var newC = i.sigmoid().mulStrict(j.tanh()).addStrict($c.mulStrict($forgetBias.add(f).sigmoid()));
var newH = newC.tanh().mulStrict(o.sigmoid());
return [newC, newH];
}
exports.basicLSTMCell = operation_1.op({ basicLSTMCell_: basicLSTMCell_ });
exports.multiRNNCell = operation_1.op({ multiRNNCell_: multiRNNCell_ });
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