In [1]:

    

import tensorflow as tf
import numpy as np
from matplotlib import animation
import matplotlib.pyplot as plt
from IPython.display import HTML
import seaborn as sns
import pandas as pd
sns.set()
df = pd.read_csv('poverty.csv')
df.head()


    

    
Out[1]:







  

    

      

      
Location
      
PovPct
      
Brth15to17
      
Brth18to19
      
ViolCrime
      
TeenBrth
    
  
  

    

      
0
      
Alabama
      
20.1
      
31.5
      
88.7
      
11.2
      
54.5
    
    

      
1
      
Alaska
      
7.1
      
18.9
      
73.7
      
9.1
      
39.5
    
    

      
2
      
Arizona
      
16.1
      
35.0
      
102.5
      
10.4
      
61.2
    
    

      
3
      
Arkansas
      
14.9
      
31.6
      
101.7
      
10.4
      
59.9
    
    

      
4
      
California
      
16.7
      
22.6
      
69.1
      
11.2
      
41.1
    
  

In [2]:

    

X = df.iloc[:, 1:2].values
Y = df.iloc[:, 2:3].values


    

In [3]:

    

class Linear:
    def __init__(self, learning_rate):
        self.X = tf.placeholder(tf.float32, (None, 1))
        self.Y = tf.placeholder(tf.float32, (None, 1))
        w = tf.Variable(tf.random_normal([1, 1]))
        b = tf.Variable(tf.random_normal([1]))
        self.logits = tf.matmul(self.X, w) + b
        self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))
        self.optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.cost)


    

In [4]:

    

tf.reset_default_graph()
sess = tf.InteractiveSession()
model = Linear(0.001)
sess.run(tf.global_variables_initializer())
for i in range(10):
    cost, _ = sess.run([model.cost, model.optimizer], feed_dict={model.X:X, model.Y:Y})
    print('epoch %d, MSE: %f'%(i+1, cost))


    

    




epoch 1, MSE: 767.497192
epoch 2, MSE: 312.644745
epoch 3, MSE: 138.829269
epoch 4, MSE: 72.407898
epoch 5, MSE: 47.025581
epoch 6, MSE: 37.325714
epoch 7, MSE: 33.618660
epoch 8, MSE: 32.201656
epoch 9, MSE: 31.659775
epoch 10, MSE: 31.452297

In [5]:

    

y_output = sess.run(model.logits, feed_dict={model.X:X})
plt.scatter(X[:,0],Y[:,0])
plt.plot(X,y_output, c='red')
plt.show()


    

In [6]:

    

tf.reset_default_graph()
sess = tf.InteractiveSession()
model = Linear(0.001)
sess.run(tf.global_variables_initializer())

fig = plt.figure(figsize=(10,5))
ax = plt.axes()
ax.scatter(X[:,0],Y[:,0], c='b')
cost, y_output = sess.run([model.cost, model.logits], feed_dict={model.X:X, model.Y:Y})
ax.set_xlabel('epoch: %d, MSE: %f'%(0,cost))
line, = ax.plot(X,y_output, lw=2, c='r')

def gradient_mean_square(epoch):
    cost, y_output, _ = sess.run([model.cost, model.logits, model.optimizer], feed_dict={model.X:X, model.Y:Y})
    line.set_data(X,y_output)
    ax.set_xlabel('epoch: %d, MSE: %f'%(epoch,cost))
    return line, ax

anim = animation.FuncAnimation(fig, gradient_mean_square, frames=50, interval=200)
anim.save('animation-linear-regression.gif', writer='imagemagick', fps=10)


    

In [ ]: