本文整理汇总了Python中neuralnet.NeuralNet.print_test方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNet.print_test方法的具体用法？Python NeuralNet.print_test怎么用？Python NeuralNet.print_test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neuralnet.NeuralNet的用法示例。
在下文中一共展示了NeuralNet.print_test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: scaled_conjugate_gradient
# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import print_test [as 别名]
network,
training_one,
method = "Newton-CG",
ERROR_LIMIT = 1e-4
)
# Train the network using Scaled Conjugate Gradient
scaled_conjugate_gradient(
network,
training_one,
ERROR_LIMIT = 1e-4
)
# Train the network using resilient backpropagation
resilient_backpropagation(
network,
training_one, # specify the training set
ERROR_LIMIT = 1e-3, # define an acceptable error limit
#max_iterations = (), # continues until the error limit is reach if this argument is skipped
# optional parameters
weight_step_max = 50.,
weight_step_min = 0.,
start_step = 0.5,
learn_max = 1.2,
learn_min = 0.5
)
network.print_test( training_one )示例2: scaled_conjugate_gradient
# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import print_test [as 别名]
network,
training_one,
method = "Newton-CG",
ERROR_LIMIT = 1e-4
)
# Train the network using Scaled Conjugate Gradient
scaled_conjugate_gradient(
network,
training_one,
ERROR_LIMIT = 1e-4
)
# Train the network using resilient backpropagation
resilient_backpropagation(
network,
training_one, # specify the training set
ERROR_LIMIT = 1e-3, # define an acceptable error limit
#max_iterations = (), # continues until the error limit is reach if this argument is skipped
# optional parameters
weight_step_max = 50.,
weight_step_min = 0.,
start_step = 0.5,
learn_max = 1.2,
learn_min = 0.5
)
network.print_test( lst)