本文整理汇总了Python中utils.AttributeDict.labeled方法的典型用法代码示例。如果您正苦于以下问题：Python AttributeDict.labeled方法的具体用法？Python AttributeDict.labeled怎么用？Python AttributeDict.labeled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utils.AttributeDict的用法示例。
在下文中一共展示了AttributeDict.labeled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: encoder
# 需要导入模块: from utils import AttributeDict [as 别名]
# 或者: from utils.AttributeDict import labeled [as 别名]
def encoder(input_, path_name, input_noise_std=0, noise_std=[]):
h = input_
logger.info(' 0: noise %g' % input_noise_std)
if input_noise_std > 0.:
h = h + self.noise_like(h) * input_noise_std
d = AttributeDict()
d.unlabeled = self.new_activation_dict()
d.labeled = self.new_activation_dict()
d.labeled.z[0] = self.labeled(h)
d.unlabeled.z[0] = self.unlabeled(h)
prev_dim = input_dim
for i, (spec, _, act_f) in layers[1:]:
d.labeled.h[i - 1], d.unlabeled.h[i - 1] = self.split_lu(h)
noise = noise_std[i] if i < len(noise_std) else 0.
curr_dim, z, m, s, h = self.f(h, prev_dim, spec, i, act_f,
path_name=path_name,
noise_std=noise)
assert self.layer_dims.get(i) in (None, curr_dim)
self.layer_dims[i] = curr_dim
d.labeled.z[i], d.unlabeled.z[i] = self.split_lu(z)
d.unlabeled.s[i] = s
d.unlabeled.m[i] = m
prev_dim = curr_dim
d.labeled.h[i], d.unlabeled.h[i] = self.split_lu(h)
return d示例2: encoder
# 需要导入模块: from utils import AttributeDict [as 别名]
# 或者: from utils.AttributeDict import labeled [as 别名]
def encoder(self, input_, path_name, input_noise_std, noise_std):
h = input_
h = h + (self.rstream.normal(size=h.shape).astype(floatX) *
input_noise_std)
d = AttributeDict()
d.unlabeled = self.new_activation_dict()
d.labeled = self.new_activation_dict()
d.labeled.z[0], d.unlabeled.z[0] = self.split_lu(h)
prev_dim = self.input_dim
for i, (spec, act_f) in self.layers[1:]:
d.labeled.h[i - 1], d.unlabeled.h[i - 1] = self.split_lu(h)
noise = noise_std[i] if i < len(noise_std) else 0.
curr_dim, z, m, s, h = self.f(h, prev_dim, spec, i, act_f,
path_name=path_name,
noise_std=noise)
self.layer_dims[i] = curr_dim
d.labeled.z[i], d.unlabeled.z[i] = self.split_lu(z)
d.unlabeled.s[i] = s
d.unlabeled.m[i] = m
prev_dim = curr_dim
d.labeled.h[i], d.unlabeled.h[i] = self.split_lu(h)
return d