mnist/index.html

<!--
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.
==============================================================================
-->

<html>

<head>
  <title>MNIST in TensorFlow.js Layers API</title>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1">
  <link rel="stylesheet" href="../shared/tfjs-examples.css" />

  <style>
    #train {
      margin-top: 10px;
    }

    label {
      display: inline-block;
      width: 250px;
      padding: 6px 0 6px 0;
    }

    .canvases {
      display: inline-block;
    }

    .prediction-canvas {
      width: 100px;
    }

    .pred {
      font-size: 20px;
      line-height: 25px;
      width: 100px;
    }

    .pred-correct {
      background-color: #00cf00;
    }

    .pred-incorrect {
      background-color: red;
    }

    .pred-container {
      display: inline-block;
      width: 100px;
      margin: 10px;
    }
  </style>
</head>

<body>
  <div class="tfjs-example-container">
    <section class='title-area'>
      <h1>TensorFlow.js: Digit Recognizer with Layers</h1>
      <p class='subtitle'>Train a model to recognize handwritten digits from the MNIST database using the tf.layers
        api.
      </p>
    </section>

    <section>
      <p class='section-head'>Description</p>
      <p>
        This examples lets you train a handwritten digit recognizer using either a Convolutional Neural Network
        (also known as a ConvNet or CNN) or a Fully Connected Neural Network (also known as a DenseNet).
      </p>
      <p>The MNIST dataset is used as training data.</p>
    </section>

    <section>
      <p class='section-head'>Training Parameters</p>
      <div>
        <label>Model Type:</label>
        <select id="model-type">
          <option>ConvNet</option>
          <option>DenseNet</option>
        </select>
      </div>

      <div>
        <label># of training epochs:</label>
        <input id="train-epochs" value="3">
      </div>

      <button id="train">Load Data and Train Model</button>
    </section>

    <section>
      <p class='section-head'>Training Progress</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'>Inference Examples</p>
      <div id="images"></div>
    </section>


  </div>

  <!-- TODO(cais): Decide. DO NOT SUBMIT. -->
  <!-- <script src="https://cdn.plot.ly/plotly-latest.min.js"></script> -->
  <script src="index.js"></script>
</body>

</html>