1.工程组成

2.urls.py

"""Django_machine_learning_linear_regression URL Configuration

The `urlpatterns` list routes URLs to views. For more information please see:

https://docs.djangoproject.com/en/2.1/topics/http/urls/

Examples:

Function views

1. Add an import: from my_app import views

2. Add a URL to urlpatterns: path('', views.home, name='home')

Class-based views

1. Add an import: from other_app.views import Home

2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')

Including another URLconf

1. Import the include() function: from django.urls import include, path

2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))

"""

from django.contrib import admin

from django.urls import path

from app01 import views

urlpatterns = [

path('admin/', admin.site.urls),

path('index/', views.index),

path('tu/', views.tu),

]

3.views.py

from django.shortcuts import render, HttpResponse

from app01 import linear_regression

import numpy as np

import tensorflow as tf

import os

# Create your views here.

def index(request):

if request.method == 'POST':

num_points = 1000

vectors_set = []

for i in range(num_points):

x1 = np.random.normal(0.0, 0.55)

y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)

vectors_set.append([x1, y1])

x_data = [v[0] for v in vectors_set]

y_data = [v[1] for v in vectors_set]

result = linear_regression.linear_regression(x_data, y_data)

return render(request, 'index.html', {'result': result, 'range':range(1,21)})

else:

return render(request, 'index.html')

def tu(request):

num = request.GET.get('num')

print(num)

base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

d = base_dir

imagepath = os.path.join(d, "regression_res.gif")

image_data = open(imagepath, "rb").read()

return HttpResponse(image_data, content_type='gif')

4.index.py

Title

{% csrf_token %}

{% if result.W != None %}

{{ result.W }} x + {{ result.b }}

{% endif %}

5.linear_regression.py

def create_gif(image_list, gif_name):

import imageio

frames = []

for image_name in image_list:

frames.append(imageio.imread(image_name))

# Save them as frames into a gif

imageio.mimsave(gif_name, frames, 'GIF', duration=0.1)

def linear_regression(x_data, y_data):

import tensorflow as tf

import matplotlib.pyplot as plt

W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')

b = tf.Variable(tf.zeros([1]), name='b')

y = W*x_data + b

loss = tf.reduce_mean(tf.square(y - y_data), name='loss')

optimizer = tf.train.GradientDescentOptimizer(0.5)

train = optimizer.minimize(loss, name='train')

sess = tf.Session()

init = tf.global_variables_initializer()

sess.run(init)

print('W=', sess.run(W), 'b=', sess.run(b), 'loss=', sess.run(loss))

i = 0

image_list = []

for step in range(20):

i = i+1

sess.run(train)

print('W=', sess.run(W), 'b=', sess.run(b), 'loss=', sess.run(loss))

plt.xlim((-2, 2))

plt.ylim((0.1, 0.5))

plt.scatter(x_data, y_data, c='r')

plt.plot(x_data, sess.run(W)*x_data + sess.run(b))

plt.savefig("./static/"+str(i)+".png")

plt.close()

image_list.append("./static/"+str(i)+".png")

create_gif(image_list, 'regression_res.gif')

result = {'W': sess.run(W), 'b': sess.run(b), 'loss': sess.run(loss)}

return result