本文整理汇总了Python中blocks.utils.shared_floatx方法的典型用法代码示例。如果您正苦于以下问题：Python utils.shared_floatx方法的具体用法？Python utils.shared_floatx怎么用？Python utils.shared_floatx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blocks.utils的用法示例。
在下文中一共展示了utils.shared_floatx方法的5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: __init__
# 需要导入模块: from blocks import utils [as 别名]
# 或者: from blocks.utils import shared_floatx [as 别名]
def __init__(self, learning_rate=0.002,
beta1=0.1, beta2=0.001, epsilon=1e-8,
decay_factor=(1 - 1e-8)):
if isinstance(learning_rate, theano.compile.SharedVariable):
self.learning_rate = learning_rate
else:
self.learning_rate = shared_floatx(learning_rate, "learning_rate")
self.beta1 = shared_floatx(beta1, "beta1")
self.beta2 = shared_floatx(beta2, "beta2")
self.epsilon = shared_floatx(epsilon, "epsilon")
self.decay_factor = shared_floatx(decay_factor, "decay_factor")
for param in [self.learning_rate, self.beta1, self.beta2, self.epsilon,
self.decay_factor]:
add_role(param, ALGORITHM_HYPERPARAMETER) 示例2: compute_step
# 需要导入模块: from blocks import utils [as 别名]
# 或者: from blocks.utils import shared_floatx [as 别名]
def compute_step(self, parameter, previous_step):
mean = shared_floatx_zeros_matching(parameter, 'mean')
add_role(mean, ALGORITHM_BUFFER)
variance = shared_floatx_zeros_matching(parameter, 'variance')
add_role(variance, ALGORITHM_BUFFER)
time = shared_floatx(0., 'time')
add_role(time, ALGORITHM_BUFFER)
t1 = time + 1
learning_rate = (self.learning_rate *
tensor.sqrt((1. - (1. - self.beta2)**t1)) /
(1. - (1. - self.beta1)**t1))
beta_1t = 1 - (1 - self.beta1) * self.decay_factor ** (t1 - 1)
mean_t = beta_1t * previous_step + (1. - beta_1t) * mean
variance_t = (self.beta2 * tensor.sqr(previous_step) +
(1. - self.beta2) * variance)
step = (learning_rate * mean_t /
(tensor.sqrt(variance_t) + self.epsilon))
mean.name = 'OPT_'+parameter.name + '_mean'
variance.name = 'OPT_'+parameter.name + '_variance'
time.name = 'OPT_'+parameter.name + '_time'
updates = [(mean, mean_t),
(variance, variance_t),
(time, t1)]
return step, updates 示例3: __init__
# 需要导入模块: from blocks import utils [as 别名]
# 或者: from blocks.utils import shared_floatx [as 别名]
def __init__(self, initial_threshold=1.0, stdevs=4, decay=0.96,
clip_to_mean=True, quick_variance_convergence=True,
**kwargs):
super(AdaptiveStepClipping, self).__init__(**kwargs)
self.gnorm_log_ave = shared_floatx(numpy.log(initial_threshold),
name='gnorm_log_ave')
self.gnorm_log2_ave = shared_floatx(0, name='gnorm_log2_ave')
self.adapt_steps = shared_floatx(0, name='adapt_steps')
self.clip_threshold = shared_floatx(numpy.nan, name='clip_threshold')
self.clip_level = shared_floatx(numpy.nan, name='clip_level')
self.decay = decay
self.stdevs = stdevs
self.clip_to_mean = clip_to_mean
self.quick_variance_convergence = quick_variance_convergence 示例4: __init__
# 需要导入模块: from blocks import utils [as 别名]
# 或者: from blocks.utils import shared_floatx [as 别名]
def __init__(self, eta=0, gamma=0.55, seed=180891):
self.eta_sqrt = shared_floatx(sqrt(eta), "eta")
add_role(self.eta_sqrt, ALGORITHM_HYPERPARAMETER)
self.gamma_half = shared_floatx(gamma/2, "gamma")
add_role(self.gamma_half, ALGORITHM_HYPERPARAMETER)
self.theano_random = rng_mrg.MRG_RandomStreams(seed=seed) 示例5: compute_steps
# 需要导入模块: from blocks import utils [as 别名]
# 或者: from blocks.utils import shared_floatx [as 别名]
def compute_steps(self, previous_steps):
time = shared_floatx(0., 'time')
t = time+1
steps = OrderedDict(
(parameter, self.add_noise(step, t))
for parameter, step in previous_steps.items())
return steps, [(time, t)]