<jupyter_start><jupyter_text>Lab-03-3 Minimizing Cost TF Optimizer<jupyter_code>import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility<jupyter_output><empty_output><jupyter_text>X and Y data<jupyter_code># tf Graph Input
X = [1, 2, 3]
Y = [1, 2, 3]<jupyter_output><empty_output><jupyter_text>Variable<jupyter_code># Set wrong model weights
W = tf.Variable(-3.0)<jupyter_output><empty_output><jupyter_text>Our Model<jupyter_code># Linear model
hypothesis = X * W

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))<jupyter_output><empty_output><jupyter_text>Minimize<jupyter_code># Minimize: Gradient Descent Magic
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train = optimizer.minimize(cost)<jupyter_output><empty_output><jupyter_text>Prepare session<jupyter_code># Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())<jupyter_output><empty_output><jupyter_text>Fit the line<jupyter_code>for step in range(100):
    print(step, sess.run(W))
    sess.run(train)<jupyter_output>0 -3.0
1 0.733334
2 0.982222
3 0.998815
4 0.999921
5 0.999995
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
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65 1.0
66 1.0
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68 1.0
69 1.0
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