tensorflow训练手写数字并识别手写数字的全过程代码
代码语言：html
所属分类：其他
代码描述：tensorflow训练手写数字并识别手写数字的全过程代码
下面为部分代码预览，完整代码请点击下载或在bfwstudio webide中打开
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<style>
#container {
width: 300px;
/*text-align: center;*/
/*display: flex;*/
}
.canvas-container {}
.button-container {
display: flex;
/*justify-content: center;*/
}
.btn {
width: 140px;
}
</style>
</head>
<body>
<div class="tfjs-example-container">
<section class='title-area'>
<h1>TensorFlow.js:手写数字识别</h1>
<p class='subtitle'>
使用tf.layers来训练MNIST手写数据并书别手写数据
api.
</p>
</section>
<section>
<p class='section-head'>
描述
</p>
<p>
此示例允许您使用卷积神经网络（也称为ConvNet或CNN）或完全连接的神经网络（也称为DenseNet）来训练手写数字识别器。
MNIST数据集用作训练数据。
</p>
</section>
<section>
<p class='section-head'>
训练参数
</p>
<div>
<label>Model Type:</label>
<select id="model-type">
<option>Logistic</option>
<option>DenseNet</option>
<option>ConvNet</option>
</select>
</div>
<div>
<label># of training epochs:</label>
<input id="train-epochs" value="3">
</div>
<button id="train">加载数据训练模型</button>
</section>
<section>
<p class='section-head'>
训练过程
</p>
<p id="status"></p>
<p id="message"></p>
<div id="stats">
<div class="canvases">
<label id="loss-label"></label>
<div id="loss-canvas"></div>
</div>
<div class="canvases">
<label id="accuracy-label"></label>
<div id="accuracy-canvas"></div>
</div>
</div>
</section>
<section>
<p class='section-head'>
推理例子
</p>
<div id="images"></div>
</section>
</div>
<script src='http://cdnjs.cloudflare.com/ajax/libs/fabric.js/1.4.0/fabric.min.js'></script>
<script src='https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.3'></script>
<script src='https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis'></script>
<script>
/**
* @license
* Copyright 2018 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.
* =============================================================================
*/
const IMAGE_H = 28;
const IMAGE_W = 28;
const IMAGE_SIZE = IMAGE_H * IMAGE_W;
const NUM_CLASSES = 10;
const NUM_DATASET_ELEMENTS = 65000;
const NUM_TRAIN_ELEMENTS = 55000;
const NUM_TEST_ELEMENTS = NUM_DATASET_ELEMENTS - NUM_TRAIN_ELEMENTS;
const MNIST_IMAGES_SPRITE_PATH =
'https://storage.googleapis.com/learnjs-data/model-.........完整代码请登录后点击上方下载按钮下载查看
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