-
Notifications
You must be signed in to change notification settings - Fork 0
/
mnist.html
129 lines (109 loc) · 5.06 KB
/
mnist.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
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link href="{{ url_for('static', filename='vendor/bootstrap/css/bootstrap.min.css') }}" rel="stylesheet">
<link href="{{ url_for('static', filename='vendor/font-awesome/css/font-awesome.min.css') }}" rel="stylesheet"
type="text/css">
<link href='https://fonts.googleapis.com/css?family=Lora:400,700,400italic,700italic' rel='stylesheet'
type='text/css'>
<link
href='https://fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800'
rel='stylesheet' type='text/css'>
<!-- Custom styles for this template -->
<link href="{{ url_for('static', filename='css/clean-blog.min.css') }}" rel="stylesheet">
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet"
integrity="sha384-+0n0xVW2eSR5OomGNYDnhzAbDsOXxcvSN1TPprVMTNDbiYZCxYbOOl7+AMvyTG2x" crossorigin="anonymous" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/font/bootstrap-icons.css" />
<link href="https://api.mapbox.com/mapbox-gl-js/v2.1.1/mapbox-gl.css" rel="stylesheet" />
<link rel="stylesheet" href="style.css" />
<title>MNIST</title>
</head>
<body>
<!-- Navbar -->
<nav class="navbar navbar-expand-lg bg-dark navbar-dark py-10 fixed-top">
<div class="container">
<a href="/" class="navbar-brand">Machine learning Bootcamp</a>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navmenu">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navmenu">
<ul class="navbar-nav ms-auto">
<li class="nav-item">
<a href="/ml" class="nav-link">Machine Learning</a>
</li>
<li class="nav-item">
<a href="/contact" class="nav-link">Contact Us</a>
</li>
</ul>
</div>
</div>
</nav> <br> <br> <br>
<div style="text-align: center;">
<h1> Mnist</h1>
</div>
<div style="margin: left 20px;">
<ul>
<li> Set of 70k small images of digits handwritten by high school students and employess of the us census bureau</li>
<li>All images are labelled with the respective digits they represent</li>
<li>Mnist is the hello world of machine learning</li>
<li>There are 70k images and each images has 28*28 features[784] </li>
<li>Each features simply represnts 1 pixel-intensity from 0 to 255. if the intensity is 0, it means that the pixel is white and if it's 255 it means it's black</li>
</ul>
</div>
<div style="margin-left: 20px;">
<h4>Import modules</h4>
<pre style="background-color: black;">
<code style="color: white;">
import numpy as np
import pandas as pd
from sklearn.datasets import load_digits
</code>
</pre>
<h4>
load the data (This data is already install in sklearn)
</h4>
<pre style="background-color: black;" >
<code style="color: whitesmoke;">
mnist = load_digits()
mnist.data.shape <strong>#to check shape of data</strong>
output: (1797, 64)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(mnist.data,mnist.target,train_size=0.8,random_state=0)
<h4>Now check shape of xtrain and xtest</h4>
x_train.shape, x_test.shape
output: ((1437, 64), (360, 64))
<h4>Train the model using logistic regression</h4>
from sklearn.linear_model import LogisticRegression <strong>#Don't worry in future we learned about logistic regression</strong>
clf = LogisticRegression(tol=0.1)
clf.fit(x_train, y_train)
<h4>Now, predict your model for top 10 values </h4>
clf.predict(x_test[0:10])
output: array([2, 8, 2, 6, 6, 7, 1, 9, 8, 5])
</code>
</pre>
</div>
<!-- pagination -->
<nav aria-label="Page navigation example" my=100>
<ul class="pagination justify-content-center" center ="right">
<li class="page-item"><a class="page-link bg-dark text-light" href="/logistic2">Next</a></li>
</ul>
</nav>
<!-- Footer -->
<footer class="p-5 bg-dark text-white text-center position-relative">
<div class="container">
<p class="lead">Copyright © 2021 Machine learning Bootcamp</p>
<a href="#" class="position-absolute bottom-0 end-0 p-5">
<i class="bi bi-arrow-up-circle h1"></i>
</a>
</div>
</footer>
<script
src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"
integrity="sha384-gtEjrD/SeCtmISkJkNUaaKMoLD0//ElJ19smozuHV6z3Iehds+3Ulb9Bn9Plx0x4"
crossorigin="anonymous"
></script>
</body>
</html>