本文整理汇总了Python中network.Network.backpropagation方法的典型用法代码示例。如果您正苦于以下问题：Python Network.backpropagation方法的具体用法？Python Network.backpropagation怎么用？Python Network.backpropagation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.backpropagation方法的3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: from network import Network [as 别名]
# 或者: from network.Network import backpropagation [as 别名]
[1.1, 3.5, 4.5, 7.6],
[2.1, 3.5, 5.5, 8.6],
[3.1, 5.5, 7.5, 9.6],
[0.1, 1.5, 2.5, 6.6],
[9.5, 8.1, 5.5, 3.6],
[5.5, 4.1, 3.5, 1.6],
[8.5, 7.1, 1.5, 1.2],
[6.5, 3.1, 2.1, 1.9],
]
yTrain = [
[1], [1], [0], [1], [1], [0], [1],
[1], [0], [0], [0], [1], [0], [0],
[1],[1],[1],[1],
[0],[0],[0],[0]
]
xTest= [[0.4, 1.9, 2.5, 3.1], [1.51, 2.0, 2.4, 3.8], [2.6, 5.1, 6.2, 7.2], [3.23, 4.1, 4.3, 4.9], [7.1, 7.6, 8.2, 9.3],
[5.78, 5.1, 4.5, 3.55], [6.33, 4.8, 3.4, 2.5], [7.67, 6.45, 5.8, 4.31], [8.22, 6.32, 5.87, 3.59], [9.1, 8.5, 7.7, 6.1]]
yTest = [[1], [1], [1], [1], [1],
[0], [0], [0], [0], [0]]
i = 0
while cost>0:
cost=nt.costTotal(False, nn, xTrain, yTrain, lamb)
costTest=nt.costTotal(False, nn, xTest, yTest, lamb)
delta=nt.backpropagation(False, nn, xTrain, yTrain, lamb)
nn['theta']=[nn['theta'][i]-alf*delta[i] for i in range(0,len(nn['theta']))]
i = i + 1
print('Train cost ', cost[0,0], 'Test cost ', costTest[0,0], 'Iteration ', i)
print(nt.runAll(nn, xTest))示例2: range
# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import backpropagation [as 别名]
from network import Network
nn=Network.create([4, 1000, 1])
lamb=0.3
cost=1
alf = 0.2
xTrain = [[1, 2.3, 4.5, 5.3], [1.1, 1.3, 2.4, 2.4], [1.9, 1.7, 1.5, 1.3], [2.3, 2.9, 3.3, 4.9], [3, 5.2, 6.1, 8.2], [3.31, 2.9, 2.4, 1.5], [4.9, 5.7, 6.1, 6.3],
[4.85, 5.0, 7.2, 8.1], [5.9, 5.3, 4.2, 3.3], [7.7, 5.4, 4.3, 3.9], [6.7, 5.3, 3.2, 1.4], [7.1, 8.6, 9.1, 9.9], [8.5, 7.4, 6.3, 4.1], [9.8, 5.3, 3.1, 2.9]]
yTrain = [[1], [1], [0], [1], [1], [0], [1],
[1], [0], [0], [0], [1], [0], [0]]
xTest= [[0.4, 1.9, 2.5, 3.1], [1.51, 2.0, 2.4, 3.8], [2.6, 5.1, 6.2, 7.2], [3.23, 4.1, 4.3, 4.9], [7.1, 7.6, 8.2, 9.3],
[5.78, 5.1, 4.5, 3.55], [6.33, 4.8, 3.4, 2.5], [7.67, 6.45, 5.8, 4.31], [8.22, 6.32, 5.87, 3.59], [9.1, 8.5, 7.7, 6.1]]
yTest = [[1], [1], [1], [1], [1],
[0], [0], [0], [0], [0]]
while cost>0:
cost=Network.costTotal(False, nn, xTrain, yTrain, lamb)
costTest=Network.costTotal(False, nn, xTest, yTest, lamb)
delta=Network.backpropagation(False, nn, xTrain, yTrain, lamb)
nn['theta']=[nn['theta'][i]-alf*delta[i] for i in range(0,len(nn['theta']))]
print('Train cost ', cost[0,0], 'Test cost ', costTest[0,0])
print(Network.runAll(nn, xTest))示例3: plot_digit
# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import backpropagation [as 别名]
# X_train = X_train[:100]
# http://rasbt.github.io/mlxtend/docs/data/mnist/
# def plot_digit(X, y, idx):
# img = X[idx].reshape(28,28)
# plt.imshow(img, cmap='Greys', interpolation='nearest')
# plt.title('true label: %d' % y[idx])
# plt.show()
# plot_digit(X_train, y_train, 4)
nt = Network()
nn = nt.create([784, 100, 1])
lamb = 0.3
cost = 1
alf = 0.005
i = 0
results = []
while cost > 0:
cost = nt.costTotal(False, nn, X_train, y_train, lamb)
delta = nt.backpropagation(False, nn, X_train, y_train, lamb)
nn["theta"] = [nn["theta"][i] - alf * delta[i] for i in range(0, len(nn["theta"]))]
i = i + 1
print("Train cost ", cost[0, 0], "Iteration ", i)
results = nt.runAll(nn, X_test)
print(results)
np.savetxt("results.csv", results, delimiter=",")