This repository has been archived by the owner on Aug 24, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 0
/
3-2-matrix.py
58 lines (46 loc) · 1.26 KB
/
3-2-matrix.py
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
import tensorflow as _tf
import matplotlib.pyplot as plt
tf = _tf.compat.v1
tf.disable_v2_behavior()
# X = [n, 3]
x_data = [
[73., 80., 75.],
[93., 88., 93.],
[89., 91., 90.],
[96., 98., 100.],
[73., 66., 70.]
]
# Y = [n, 1]
y_data = [
[152.],
[185.],
[180.],
[196.],
[142.]
]
# X = [n, 3]
X = tf.placeholder(tf.float32, shape=[None, 3])
# Y = [n, 1]
Y = tf.placeholder(tf.float32, shape=[None, 1])
# W = [3, 1]
W = tf.Variable(tf.random_normal([3, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
# H(X) = XW + b
hypothesis = tf.matmul(X, W) + b
# Math.pow(H(X) - Y, 2)
cost_fn = tf.square(hypothesis - Y)
cost = tf.reduce_mean(cost_fn)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for step in range(4001):
cost_val, hy_val, _ = sess.run(
[cost, hypothesis, train],
feed_dict={X: x_data, Y: y_data}
)
if step % 50 == 0:
print(step, 'Cost:', cost_val, 'Prediction:', hy_val)
print(sess.run(hypothesis, feed_dict={X: [[73., 80., 75.]]}))
print(sess.run(hypothesis, feed_dict={X: [[93., 88., 93.]]}))
print(sess.run(hypothesis, feed_dict={X: [[89., 91., 90.]]}))