本文整理汇总了Python中Network.Network.save方法的典型用法代码示例。如果您正苦于以下问题：Python Network.save方法的具体用法？Python Network.save怎么用？Python Network.save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.Network的用法示例。
在下文中一共展示了Network.save方法的2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: distribute
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import save [as 别名]
def distribute(rate, sigmoid, hidden, examples, variables, layers, rule, dropout, table):
example = 0
not_learned = ""
tables = monotone_generator(variables)
print "Learning", tables[table-1],
learned = False
tries = 0
while not learned and tries < 200000:
tries += 1
model = Network(rate, sigmoid, hidden, examples, variables, layers, rule, dropout)
learned = model.train(tables[table-1])
if learned:
print "Learned with {0} models".format(tries)
model.save("hebb{0}.txt".format(table-1))
else:
print "Not Learned"
return示例2: run
# 需要导入模块: from Network import Network [as 别名]
# 或者: from Network.Network import save [as 别名]
def run(rate, sigmoid, hidden, examples, variables, layers, rule, dropout):
"""
Creates network and trains a model for each boolean function
Keyword arguments:
rate -- learning rate (float)
sigmoid -- sigmoid function for weights if rule is basic hebbian (int)
hidden -- number of hidden units, 0 removes hidden layer (int)
examples -- number of random boolean examples to present.
layers -- number of hidden layers 1 to N (int)
rule -- learning rule, "hebbian" or "oja" (str)
dropout -- percentage of edge weights to update
prints each function, whether it was able to learn it, and a summary.
"""
functions = []
example = 0
monotone_fxns = 0
#tables = truth_tables(variables)
tables = monotone_generator(variables)
not_learned = ""
for i in range(len(tables)):
print "Learning", tables[i],
example += 1
learned = False
tries = 0
while not learned and tries < 200000:
tries += 1
model = Network(rate, sigmoid, hidden, examples, variables, layers, rule, dropout)
learned = model.train(tables[i])
if learned:
print "Learned with {0} models".format(tries)
functions.append(bit_repr(tables[i]))
model.save("models/hebb{0}.txt".format(example))
#model.test("models/hebb{0}.txt".format(example))
else:
not_learned = not_learned+str(i)+","
print "Not Learned"
return "Learned:", len(functions),"Not Learned", not_learned