机器学习进阶笔记之一 | TensorFlow安装与入门

 逻辑回归

1.  import tensorflow as tf
2.  # Import MINST data
3.  from tensorflow.examples.tutorials.mnist import input_data
4.  mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
5. 
   
6.  # Parameters
7.  learning_rate = 0.01
8.  training_epochs = 25
9.  batch_size = 100
10.  display_step = 1
11. 
    
12.  # tf Graph Input
13.  x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
14.  y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes
15. 
    
16.  # Set model weights
17.  W = tf.Variable(tf.zeros([784, 10]))
18.  b = tf.Variable(tf.zeros([10]))
19. 
    
20.  # Construct model
21.  pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
22. 
    
23.  # Minimize error using cross entropy
24.  cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
25.  # Gradient Descent
26.  optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
27. 
    
28.  # Initializing the variables
29.  init = tf.initialize_all_variables()
30. 
    
31.  # Launch the graph
32.  with tf.Session() as sess:
33.      sess.run(init)
34. 
    
35.      # Training cycle
36.      for epoch in range(training_epochs):
37.          avg_cost = 0.
38.          total_batch = int(mnist.train.num_examples/batch_size)
39.          # Loop over all batches
40.          for i in range(total_batch):
41.              batch_xs, batch_ys = mnist.train.next_batch(batch_size)
42.              # Run optimization op (backprop) and cost op (to get loss value)
43.              _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
44.                                                            y: batch_ys})
45.              # Compute average loss
46.              avg_cost += c / total_batch
47.          # Display logs per epoch step
48.          if (epoch+1) % display_step == 0:
49.              print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
50. 
    
51.      print "Optimization Finished!"
52. 
    
53.      # Test model
54.      correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
55.      # Calculate accuracy
56.      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
57.      print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
58. 
    
59.      # result :
60.      Epoch: 0001 cost= 29.860467369
61.      Epoch: 0002 cost= 22.001451784
62.      Epoch: 0003 cost= 21.019925554
63.      Epoch: 0004 cost= 20.561320320
64.      Epoch: 0005 cost= 20.109135756
65.      Epoch: 0006 cost= 19.927862290
66.      Epoch: 0007 cost= 19.548687116
67.      Epoch: 0008 cost= 19.429119071
68.      Epoch: 0009 cost= 19.397068211
69.      Epoch: 0010 cost= 19.180813479
70.      Epoch: 0011 cost= 19.026808132
71.      Epoch: 0012 cost= 19.057875510
72.      Epoch: 0013 cost= 19.009575057
73.      Epoch: 0014 cost= 18.873240641
74.      Epoch: 0015 cost= 18.718575359
75.      Epoch: 0016 cost= 18.718761925
76.      Epoch: 0017 cost= 18.673640560
77.      Epoch: 0018 cost= 18.562128253
78.      Epoch: 0019 cost= 18.458205289
79.      Epoch: 0020 cost= 18.538211225
80.      Epoch: 0021 cost= 18.443384213
81.      Epoch: 0022 cost= 18.428727668
82.      Epoch: 0023 cost= 18.304270616
83.      Epoch: 0024 cost= 18.323529782
84.      Epoch: 0025 cost= 18.247192113
85.      Optimization Finished!
86.      (10000, 784)
87.      Accuracy 0.9206