本文整理汇总了Python中Network.cleanBeliefExporter方法的典型用法代码示例。如果您正苦于以下问题：Python Network.cleanBeliefExporter方法的具体用法？Python Network.cleanBeliefExporter怎么用？Python Network.cleanBeliefExporter使用的例子？那么恭喜您, 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Network的用法示例。
在下文中一共展示了Network.cleanBeliefExporter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: range
# 需要导入模块: import Network [as 别名]
# 或者: from Network import cleanBeliefExporter [as 别名]
#Initialize Network; there is is also a layer-wise initialization option
DESTIN.initNetwork()
#data.shape[0]
for I in range(data.shape[0]):# For Every image in the data set
if I%1000 == 0:
print("Training Iteration Number %d" % I)
for L in range(DESTIN.NumberOfLayers):
if L == 0:
img = data[I][:].reshape(32,32,3)
DESTIN.Layers[0][L].trainTypicalNode(img,[4,4])
else:
DESTIN.Layers[0][L].trainTypicalNode(DESTIN.Layers[0][L-1].Nodes,[2,2])
DESTIN.Layers[0][L].shareCentroids()
DESTIN.updateBeliefExporter()
DESTIN.dumpBelief(2)
DESTIN.cleanBeliefExporter()#Get rid-off accumulated training beliefs
print("Testing Started")
NetworkMode = False
DESTIN.setMode(NetworkMode)
del data, labels
[data, labels] = loadCifar(6)
#data = np.random.rand(5,32*32*3)
del labels
for I in range(data.shape[0]):# For Every image in the data set
if I%1000 == 0:
print("Testing Iteration Number %d" % I)
for L in range(DESTIN.NumberOfLayers):
if L == 0:
img = data[I][:].reshape(32,32,3)
DESTIN.Layers[0][L].loadInput(img,[4,4])# loadInput to Layer[0]
else: