-
Notifications
You must be signed in to change notification settings - Fork 2.3k
/
index.html
111 lines (87 loc) · 3.28 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
<!--
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.
==============================================================================
-->
<!doctype html>
<head>
<title>TensorFlow.js Layers: Sentiment Analysis Demo</title>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="../shared/tfjs-examples.css" />
<style>
#review-text {
font-size: 120%;
width: 60%;
height: 200px;
}
select {
display: block;
margin-bottom: 20px;
}
</style>
</head>
<body>
<div class="tfjs-example-container centered-container">
<section class='title-area'>
<h1>TensorFlow.js Layers: Sentiment Analysis Demo</h1>
</section>
<section>
<p class='section-head'>Description</p>
<p>
This example demonstrated loading a pre-trained model and using it in the browser. This model is trained to
predict the sentiment of a short movie review (as a score between 0 and 1). The training is done server side
using Python and then converted into a TensorFlow.js model.
</p>
<p>
The model is trained using IMDB reviews that have been truncated to a maximum of 200 words, only the
20000 most used words in the reviews are used. You can experiment with the model on this page or experiment
with training your model by looking at the <a href="https://github.com/tensorflow/tfjs-examples/tree/master/sentiment/python">training
code</a> in the repository.
</p>
<p>This is not intended to be a production level sentiment analysis model.</p>
</section>
<section>
<p class='section-head'>Load Model</p>
<div class="create-model">
<button id="load-pretrained-remote" style="display:none">Load hosted pretrained model</button>
<button id="load-pretrained-local" style="display:none">Load local pretrained model</button>
</div>
<div>
<div>
<span>Model type: </span>
<span id="modelType"></span>
</div>
<div>
<span>Vocabulary size: </span>
<span id="vocabularySize"></span>
</div>
<div>
<span>Max length: </span>
<span id="maxLen"></span>
</div>
</div>
</section>
<section>
<p class='section-head'>Input Sample</p>
<select id="test-example-select" class="form-control">
<option value="positive">Positive example</option>
<option value="negative">Negative example</option>
</select>
<textarea id="review-text"></textarea>
</section>
<section>
<p class='section-head'>Status</p>
<span id="status">Standing by.</span>
</section>
</div>
<script type="module" src='index.js'></script>
</body>