In [1]:

    

import tensorflow as tf
import matplotlib.pyplot as plt

print(tf.__version__)


    

    




1.2.0

In [3]:

    

X = [1,2,3]
Y = [1,2,3]

W = tf.Variable(5.0)

hypothesis = W * X 

cost = tf.reduce_mean(tf.square(hypothesis - Y))


    

In [4]:

    

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train = optimizer.minimize(cost)


    

In [5]:

    

sess = tf.Session()
sess.run(tf.global_variables_initializer())


    

In [6]:

    

for step in range(100):
    print(step, sess.run(W))
    sess.run(train)


    

    




0 5.0
1 1.26667
2 1.01778
3 1.00119
4 1.00008
5 1.00001
6 1.0
7 1.0
8 1.0
9 1.0
10 1.0
11 1.0
12 1.0
13 1.0
14 1.0
15 1.0
16 1.0
17 1.0
18 1.0
19 1.0
20 1.0
21 1.0
22 1.0
23 1.0
24 1.0
25 1.0
26 1.0
27 1.0
28 1.0
29 1.0
30 1.0
31 1.0
32 1.0
33 1.0
34 1.0
35 1.0
36 1.0
37 1.0
38 1.0
39 1.0
40 1.0
41 1.0
42 1.0
43 1.0
44 1.0
45 1.0
46 1.0
47 1.0
48 1.0
49 1.0
50 1.0
51 1.0
52 1.0
53 1.0
54 1.0
55 1.0
56 1.0
57 1.0
58 1.0
59 1.0
60 1.0
61 1.0
62 1.0
63 1.0
64 1.0
65 1.0
66 1.0
67 1.0
68 1.0
69 1.0
70 1.0
71 1.0
72 1.0
73 1.0
74 1.0
75 1.0
76 1.0
77 1.0
78 1.0
79 1.0
80 1.0
81 1.0
82 1.0
83 1.0
84 1.0
85 1.0
86 1.0
87 1.0
88 1.0
89 1.0
90 1.0
91 1.0
92 1.0
93 1.0
94 1.0
95 1.0
96 1.0
97 1.0
98 1.0
99 1.0

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