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added loader
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‎vanishing_gradients/index.html‎

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<!--
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Copyright 2018 Google LLC. 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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http://www.apache.org/licenses/LICENSE-2.0
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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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<html>
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input {
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width: 75px;
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}
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.input-div {
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padding: 5px;
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font-family: monospace;
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<p class='section-head'>Description</p>
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<p class="content">
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This example explains the problem of vanishing gradients (which you may encounter when training a deep neural
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network) and also use some activation function to prevent it. It describes the situation where a deep
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network) and also use some activation functions to prevent it. It describes the situation where a deep
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multilayer feed-forward network or a recurrent neural network is unable to propagate useful gradient information
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from the output end of the model back to the layers near the input end of the model.
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</p>
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<li><p>It is strictly advised to keep batch size greater than 1, otherwise you may encounter exploding gradients
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problem showing black colour links all over in architecture.</p></li>
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<li><p>Wait for some time after clicking the button.</p></li>
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<li><p>Change the custom parameters and again press the button to train the changed neural network.</p></li>
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</ol>
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</p>
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</section>
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<canvas id="tea" height="25" width="500"></canvas>
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<div id="graph3"></div><br>
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</div>
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<div id="loader"></div><br><br>
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<button id="show-nn-architecture">Train and Show NN-architecture</button>
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</div><br>
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<body>
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<script src="index.js"></script>
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</body>
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</html>
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</html>

‎vanishing_gradients/index.js‎

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/**
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* @license
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* Copyright 2018 Google LLC. 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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import * as tf from '@tensorflow/tfjs';
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import * as tfvis from '@tensorflow/tfjs-vis';
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import Plotly from 'plotly.js-dist';
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import * as data from './data';
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import * as data from './data';
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var xte_a=Array();
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var yte_a=Array();
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document.getElementById('show-nn-architecture')
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.addEventListener('click', async() => {
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var load = document.getElementById("loader");
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load.style.fontSize = "30px";
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load.style.color = "black";
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load.style.fontFamily = "monospace";
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load.style.backgroundColor = "greenyellow";
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var callback1 = function ()
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{
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load.innerHTML = "Training......";
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}
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var callback2 = function()
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{
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xl=[];yl=[];
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xtr_a=[];ytr_a=[];
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xte_a=[];yte_a=[];
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total_loss+=value.arraySync();
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for(var j=0;j<layers;j++)
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tea.font="25px Arial";
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tea.fillText("Test accuracy(30 egs) vs epoch => ",5,20);
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} );
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load.innerHTML = "Model Trained"+String.fromCharCode(0xD83D, 0xDE04);
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}
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document.getElementById('show-nn-architecture')
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.addEventListener('click',show);
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function show()
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{
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setTimeout(callback1, 1000);
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setTimeout(callback2, 2000);
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}
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