Hello World

blake

1. import tensorflow as tf
   
2. import numpy as np
   
3. 
   
4. # Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
   
5. x_data = np.random.rand(100).astype(np.float32)
   
6. y_data = x_data * 0.1 + 0.3
   
7. 
   
8. # Try to find values for W and b that compute y_data = W * x_data + b
   
9. # (We know that W should be 0.1 and b 0.3, but TensorFlow will
   
10. # figure that out for us.)
    
11. W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
    
12. b = tf.Variable(tf.zeros([1]))
    
13. y = W * x_data + b
    
14. 
    
15. # Minimize the mean squared errors.
    
16. loss = tf.reduce_mean(tf.square(y - y_data))
    
17. optimizer = tf.train.GradientDescentOptimizer(0.5)
    
18. train = optimizer.minimize(loss)
    
19. 
    
20. # Before starting, initialize the variables.  We will 'run' this first.
    
21. init = tf.global_variables_initializer()
    
22. 
    
23. # Launch the graph.
    
24. sess = tf.Session()
    
25. sess.run(init)
    
26. 
    
27. # Fit the line.
    
28. for step in range(201):
    
29.     sess.run(train)
    
30.     if step % 20 == 0:
    
31.         print(step, sess.run(W), sess.run(b))
    
32. 
    
33. # Learns best fit is W: [0.1], b: [0.3]

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