Source Code for Module Bio.NeuralNetwork.Training

 1  """Provide classes for dealing with Training Neural Networks. 
 2  """ 
 3  # standard modules 
 4  import random 
 5   
 6   
 7 -class TrainingExample(object): 
 8      """Hold inputs and outputs of a training example. 
 9   
10      XXX Do I really need this? 
11      """ 
12 -    def __init__(self, inputs, outputs, name=""): 
13          self.name = name 
14          self.inputs = inputs 
15          self.outputs = outputs 
16   
17   
18 -class ExampleManager(object): 
19      """Manage a grouping of Training Examples. 
20   
21      This is meant to make it easy to split a bunch of training examples 
22      into three types of data: 
23   
24      o Training Data -- These are the data used to do the actual training 
25      of the network. 
26   
27      o Validation Data -- These data are used to validate the network 
28      while training. They provide an independent method to evaluate how 
29      the network is doing, and make sure the network gets trained independent 
30      of noise in the training data set. 
31   
32      o Testing Data -- The data which are used to verify how well a network 
33      works. They should not be used at all in the training process, so they 
34      provide a completely independent method of testing how well a network 
35      performs. 
36      """ 
37 -    def __init__(self, training_percent=.4, validation_percent=.4): 
38          """Initialize the manager with the training examples. 
39   
40          Arguments: 
41   
42          o training_percent - The percentage of the training examples that 
43          should be used for training the network. 
44   
45          o validation_percent - Percent of training examples for validating 
46          a network during training. 
47   
48          Attributes: 
49   
50          o train_examples - A randomly chosen set of examples for training 
51          purposes. 
52   
53          o valdiation_examples - Randomly chosesn set of examples for 
54          use in validation of a network during training. 
55   
56          o test_examples - Examples for training purposes. 
57          """ 
58          assert training_percent + validation_percent <= 1.0, \ 
59              "Training and validation percentages more than 100 percent" 
60   
61          self.train_examples = [] 
62          self.validation_examples = [] 
63          self.test_examples = [] 
64   
65          self.training_percent = training_percent 
66          self.validation_percent = validation_percent 
67   
68 -    def add_examples(self, training_examples): 
69          """Add a set of training examples to the manager. 
70   
71          Arguments: 
72   
73          o training_examples - A list of TrainingExamples to manage. 
74          """ 
75          placement_rand = random.Random() 
76   
77          # assign exact example randomly to the example types 
78          for example in training_examples: 
79              chance_num = placement_rand.random() 
80              # assign with the specified percentage 
81              if chance_num <= self.training_percent: 
82                  self.train_examples.append(example) 
83              elif chance_num <= (self.training_percent + 
84                                  self.validation_percent): 
85                  self.validation_examples.append(example) 
86              else: 
87                  self.test_examples.append(example) 
88