机器学习进阶笔记之一 | TensorFlow安装与入门

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　　以下代码来自GitHub - aymericdamien/TensorFlow-Examples: TensorFlow Tutorial and Examples for beginners,仅作学习用

1.  import tensorflow as tf
2.  import numpy
3.  import matplotlib.pyplot as plt
4.  rng = numpy.random
5. 
   
6.  # Parameters
7.  learning_rate = 0.01
8.  training_epochs = 2000
9.  display_step = 50
10. 
    
11.  # Training Data
12.  train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
13.  train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
14.  n_samples = train_X.shape[0]
15. 
    
16.  # tf Graph Input
17.  X = tf.placeholder("float")
18.  Y = tf.placeholder("float")
19. 
    
20.  # Create Model
21. 
    
22.  # Set model weights
23.  W = tf.Variable(rng.randn(), name="weight")
24.  b = tf.Variable(rng.randn(), name="bias")
25. 
    
26.  # Construct a linear model
27.  activation = tf.add(tf.mul(X, W), b)
28. 
    
29.  # Minimize the squared errors
30.  cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss
31.  optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
32. 
    
33.  # Initializing the variables
34.  init = tf.initialize_all_variables()
35. 
    
36.  # Launch the graph
37.  with tf.Session() as sess:
38.      sess.run(init)
39. 
    
40.      # Fit all training data
41.      for epoch in range(training_epochs):
42.          for (x, y) in zip(train_X, train_Y):
43.              sess.run(optimizer, feed_dict={X: x, Y: y})
44. 
    
45.          #Display logs per epoch step
46.          if epoch % display_step == 0:
47.              print "Epoch:", '%04d' % (epoch+1), "cost=", \
48.                  "{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \
49.                  "W=", sess.run(W), "b=", sess.run(b)
50. 
    
51.      print "Optimization Finished!"
52.      print "cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \
53.            "W=", sess.run(W), "b=", sess.run(b)
54. 
    
55.      #Graphic display
56.      plt.plot(train_X, train_Y, 'ro', label='Original data')
57.      plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
58.      plt.legend()
59.      plt.show()