本文整理汇总了Python中pylearn2.linear.matrixmul.MatrixMul.get_weights_topo方法的典型用法代码示例。如果您正苦于以下问题：Python MatrixMul.get_weights_topo方法的具体用法？Python MatrixMul.get_weights_topo怎么用？Python MatrixMul.get_weights_topo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pylearn2.linear.matrixmul.MatrixMul的用法示例。
在下文中一共展示了MatrixMul.get_weights_topo方法的1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: RBM
# 需要导入模块: from pylearn2.linear.matrixmul import MatrixMul [as 别名]
# 或者: from pylearn2.linear.matrixmul.MatrixMul import get_weights_topo [as 别名]
#.........这里部分代码省略.........
self.__dict__.update(nhid=nhid, nvis=nvis)
self._params = safe_union(self.transformer.get_params(), [self.bias_vis, self.bias_hid])
self.base_lr = base_lr
self.anneal_start = anneal_start
self.nchains = nchains
self.sml_gibbs_steps = sml_gibbs_steps
def get_input_dim(self):
if not isinstance(self.vis_space, VectorSpace):
raise TypeError("Can't describe "+str(type(self.vis_space))+" as a dimensionality number.")
return self.vis_space.dim
def get_output_dim(self):
if not isinstance(self.hid_space, VectorSpace):
raise TypeError("Can't describe "+str(type(self.hid_space))+" as a dimensionality number.")
return self.hid_space.dim
def get_input_space(self):
return self.vis_space
def get_output_space(self):
return self.hid_space
def get_params(self):
return [param for param in self._params]
def get_weights(self, borrow=False):
weights ,= self.transformer.get_params()
return weights.get_value(borrow=borrow)
def get_weights_topo(self):
return self.transformer.get_weights_topo()
def get_weights_format(self):
return ['v', 'h']
def get_monitoring_channels(self, data):
V = data
theano_rng = RandomStreams(42)
#TODO: re-enable this in the case where self.transformer
#is a matrix multiply
#norms = theano_norms(self.weights)
H = self.mean_h_given_v(V)
h = H.mean(axis=0)
return { 'bias_hid_min' : T.min(self.bias_hid),
'bias_hid_mean' : T.mean(self.bias_hid),
'bias_hid_max' : T.max(self.bias_hid),
'bias_vis_min' : T.min(self.bias_vis),
'bias_vis_mean' : T.mean(self.bias_vis),
'bias_vis_max': T.max(self.bias_vis),
'h_min' : T.min(h),
'h_mean': T.mean(h),
'h_max' : T.max(h),
#'W_min' : T.min(self.weights),
#'W_max' : T.max(self.weights),
#'W_norms_min' : T.min(norms),
#'W_norms_max' : T.max(norms),
#'W_norms_mean' : T.mean(norms),