本文整理汇总了Python中NeuralNetwork.NeuralNetwork.getAlpha方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNetwork.getAlpha方法的具体用法？Python NeuralNetwork.getAlpha怎么用？Python NeuralNetwork.getAlpha使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类NeuralNetwork.NeuralNetwork的用法示例。
在下文中一共展示了NeuralNetwork.getAlpha方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from NeuralNetwork import NeuralNetwork [as 别名]
# 或者: from NeuralNetwork.NeuralNetwork import getAlpha [as 别名]
class Classifier:
def __init__(self, classifier_type, **kwargs):
"""
Initialize a classifier for managing learning model.
Args:
classifier_type: the type of learning model. e.g. neural_network
**kwargs: store parameter in a dictionary
"""
self.classifier_type = classifier_type
self.params = kwargs
self.clf = None
self.file = open('result/trial_' + str(datetime.datetime.today()).replace("/", "_", -1) + ".txt", 'w', 0)
def train(self, training_data, testData, classNum, batchSize):
"""
Create a learning model. Train the model with the training data. Print the training accuracy every certain iterations.
If the learning rate is not chosen appropriately, let the user to enter a new
"""
# find the numbers for feature and label
featureNum = training_data.shape[1] - 1
# #this will find all the unique labels automatically, but will have problem when training data is lacking some labels
# labelNum = len(np.unique(training_data[:, :1]))
labelNum = classNum
# get the number of nodes for each layer
if "hidden_layer" in self.params and self.params["hidden_layer"] is not None:
nodeNum = [featureNum] + self.params["hidden_layer"] + [labelNum]
else:
nodeNum = [featureNum, featureNum * 2, labelNum]
# get the mode for initializing the weight
if "weightInitMode" in self.params and self.params["weightInitMode"] is not None:
weightInitMode = self.params["weightInitMode"]
else:
weightInitMode = None
# get the momentum factor
if "momentumFactor" in self.params:
momentumFactor = self.params["momentumFactor"]
else:
momentumFactor = 0.0
self.clf = NeuralNetwork(training_data, nodeNum, weightInitMode, momentumFactor)
iteration = 5
totalIter = 0
testSize = 100000
while iteration > 0:
if iteration < 10:
self.clf.train(iteration, batchSize)
totalIter += iteration
print "---------- Settings ----------"
print "Examples :", training_data.shape[0]
print "Batch size :", batchSize
print "Alpha :", self.clf.getAlpha()
print "Momentum factor :", momentumFactor
print "# of Nodes in all layers :", nodeNum
print "Training iteration so far:", totalIter
self.file.write("\n")
self.file.write("---------- Settings ----------" + "\n")
self.file.write("Examples : " + str(training_data.shape[0]) + "\n")
self.file.write("Batch size : " + str(batchSize) + "\n")
self.file.write("Alpha : " + str(self.clf.getAlpha()) + "\n")
self.file.write("Momentum factor : " + str(momentumFactor) + "\n")
self.file.write("# of Nodes in all layers : " + str(nodeNum) + "\n")
self.file.write("Training iteration so far: " + str(totalIter) + "\n")
self.test(training_data, "training")
self.test(testData, "testing")
iteration = 0
while iteration >= testSize:
self.clf.train(testSize, batchSize)
totalIter += testSize
print "---------- Settings ----------"
print "Examples :", training_data.shape[0]
print "Batch size :", batchSize
print "Alpha :", self.clf.getAlpha()
print "Momentum factor :", momentumFactor
print "# of Nodes in all layers :", nodeNum
print "Training iteration so far:", totalIter
self.file.write("\n")
self.file.write("---------- Settings ----------" + "\n")
self.file.write("Examples : " + str(training_data.shape[0]) + "\n")
self.file.write("Batch size : " + str(batchSize) + "\n")
self.file.write("Alpha : " + str(self.clf.getAlpha()) + "\n")
self.file.write("Momentum factor : " + str(momentumFactor) + "\n")
self.file.write("# of Nodes in all layers : " + str(nodeNum) + "\n")
self.file.write("Training iteration so far: " + str(totalIter) + "\n")
self.test(training_data, "training")
self.test(testData, "testing")
iteration -= testSize
if iteration > 0:
self.clf.train(iteration, batchSize)
totalIter += iteration
print "---------- Settings ----------"
print "Examples :", training_data.shape[0]
#.........这里部分代码省略.........