使用Django和heroku搭建一个简易网站
最近由于毕设的需要，想要做一个通过输入各种参数，预测冷热负荷的机器学习系统。可以在网页上直接输入房屋参数，预测出房屋所需冷热负荷和能耗水平。关于机器学习那块相信网上已经有不少教程，这里就不再赘述，本文主要总结一下Django和heroku搭配来建立网站。
Django
这个是根据tutorial建立起来的网站的文件目录。
mysite/
manage.py
mysite/
__init__.py
settings.py
urls.py
wsgi.py
polls/
__init__.py
admin.py
migrations/
__init__.py
0001_initial.py
models.py
static/
polls/
images/
background.gif
style.css
templates/
polls/
detail.html
index.html
results.html
tests.py
urls.py
views.py
templates/
admin/
base_site.html
最顶层是mysite使我们的project，然后在这个project下面添加了一个app叫做polls。
在project下面又有一个文件夹叫mysite，里面放了一些setting，urls的重要文件。
url是指明Uniform Resource Locator的文件，即各种资源的索引位置。
wsgi.py是方便之后部署的文件。
在polls文件夹中，migration文件夹主要是负责数据库的连接。
models文件里定义了一些类，是我们数据库中会记录的东西。
views是我们给访问者呈现什么数据，它和templates是搭配的。
templates文件夹里，还有一个polls文件夹（虽然我自己后来建的时候放在polls文件夹中的html文件总是找不到就不再加这个polls子文件夹了，但是合理的情况是放在polls中的），存放着html格式的文件，和views搭配负责页面如何呈现。
其中在我自己的project里面最重要的是表单，即用户输入数据然后表单通过request接收，views.py处理，return回去。
这是我views.py文件的内容:
# -*- coding: utf-8 -*-
from django.shortcuts import render, render_to_response
from django.views.decorators import csrf
import tensorflow as tf
import numpy as np
def add_layer(inputs, insize, outsize, n, activation_function=None):
layer_name = 'layer%s' % n
with tf.name_scope(layer_name):
Weights = tf.Variable(tf.random_normal(), name='w')
tf.summary.histogram(layer_name + 'Weights', Weights)
bias = tf.Variable(tf.zeros())
tf.summary.histogram(layer_name + 'bias', bias)
wx_b = tf.add(tf.matmul(inputs, Weights), bias)
if activation_function is None:
output = wx_b
else:
output = activation_function(wx_b, )
return output
def input(request):
return render_to_response('get.html')
def calculate(request):
# xdata =
request.encoding = 'utf-8'
context = {}
xdata = []
if request.GET:
for num in range(1, 9):
xdata.append(float(request.GET['X%i' % num]))
xdata = np.array(xdata).reshape()
xdata = 1.0 / (1 + np.exp(xdata))
# print('xdata: ', xdata)
# context['heatload'] = xdata
# context['coolload'] = xdata
tf.reset_default_graph()
xs = tf.placeholder(tf.float32, xdata.shape, name='xinput')
l1 = add_layer(xs, xdata.shape, 10, 1, activation_function=tf.nn.relu)# 10 is layer units
l2 = add_layer(l1, 10, 10, 2, activation_function=tf.nn.relu)# 10 is layer units
prediction = add_layer(l2, 10, 2, 3, activation_function=None)# predicted output
saver = tf.train.Saver()
with tf.Session() as sess:
# you cannot initialize here
saver.restore(sess, './my_net/save_net.ckpt')
rlt = sess.run(prediction, feed_dict={xs: xdata})
# print('result: ', rlt)
context['heatload'] = rlt
context['coolload'] = rlt
print('context', context)
return render(request, "result.html", context)
get.html
<!DOCTYPE html>
<html lang="en" xmlns="http://www.w3.org/1999/html">
<head>
<meta charset="UTF-8">
<title>Calculate the hear load and cool load</title>
</head>
<body>
<form action = "/result/" method = "get">
{%csrf_token %}
Relative Compactness (surface-area-to-volume ratio): <input type="number" step = "any" name="X1" value = 0.764167> <br />
Surface Area: <input type="number" name="X2" step = "any" value = 671.708333> <br />
Wall Area: <input type="number" name="X3" step = "any" value = 318.50000> <br />
Roof Area: <input type="number" name="X4" step = "any" value = 176.604167> <br />
Overall Height: <input type="number" name="X5" step = "any" value = 5.25> <br />
Orientation: <input type="number" name="X6" step = "any" value = 3.5> <br />
Glazing Area: <input type="number" name="X7" step = "any" value = 0.234375> <br />
Glazing Area Distribution: <input type="text" name="X8" step = "any" value = 2.812500> <br />
<input type="submit" value="Submit">
</form>
<p></p>
</body>
</html>
返回结果的页面get.html。里面还用到一些html判断语句来判断能耗等级。
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>result</title>
</head>
<body>
heat load: <p> {{ heatload }} BTU </p> <br />
cool load: <p> {{ coolload }} BTU </p> <br />
{% if heatload < 11.67 and coolload < 14.52 %}
the efficiency classification: very low heating and cooling requirement
{% elif 15.92 > heatload and heatload >= 11.67 and 18.65 > coolload and coolload >= 14.52%}
the efficiency classification: low heating and cooling requirement
{% elif 26.27 > heatload and heatload >= 15.92 and 28.27 > coolload and coolload >= 18.65%}
the efficiency classification: medium heating and cooling requirement
{% elif 32.32 > heatload and heatload >= 26.27 and 34.03 > coolload and coolload >= 28.27%}
the efficiency classification: high heating and cooling requirement
{% else %}
the efficiency classification: very high heating and cooling requirement
{% endif %}
</body>
</html>
然后下面这个是mysite/urls，是指明了url和views或者下面app的url关联关系
from django.contrib import admin
from django.urls import path, include
from energy.views import calculate
urlpatterns = [
path('', include('energy.urls')),
path('result/', calculate),
path('admin/', admin.site.urls),
]
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