本文整理匯總了Python中utils.AttributeDict.denois[i]方法的典型用法代碼示例。如果您正苦於以下問題：Python AttributeDict.denois[i]方法的具體用法？Python AttributeDict.denois[i]怎麽用？Python AttributeDict.denois[i]使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils.AttributeDict的用法示例。
在下文中一共展示了AttributeDict.denois[i]方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: decoder
# 需要導入模塊: from utils import AttributeDict [as 別名]
# 或者: from utils.AttributeDict import denois[i] [as 別名]
def decoder(self, clean, corr):
est = self.new_activation_dict()
costs = AttributeDict()
costs.denois = AttributeDict()
for i, ((_, spec), act_f) in self.layers[::-1]:
z_corr = corr.unlabeled.z[i]
z_clean = clean.unlabeled.z[i]
z_clean_s = clean.unlabeled.s.get(i)
z_clean_m = clean.unlabeled.m.get(i)
# It's the last layer
if i == len(self.layers) - 1:
fspec = (None, None)
ver = corr.unlabeled.h[i]
ver_dim = self.layer_dims[i]
top_g = True
else:
fspec = self.layers[i + 1][1][0]
ver = est.z.get(i + 1)
ver_dim = self.layer_dims.get(i + 1)
top_g = False
z_est = self.g(z_lat=z_corr,
z_ver=ver,
in_dims=ver_dim,
out_dims=self.layer_dims[i],
num=i,
fspec=fspec,
top_g=top_g)
# The first layer
if z_clean_s:
z_est_norm = (z_est - z_clean_m) / z_clean_s
else:
z_est_norm = z_est
se = SquaredError('denois' + str(i))
costs.denois[i] = se.apply(z_est_norm.flatten(2),
z_clean.flatten(2)) \
/ np.prod(self.layer_dims[i], dtype=floatX)
costs.denois[i].name = 'denois' + str(i)
# Store references for later use
est.z[i] = z_est
est.h[i] = apply_act(z_est, act_f)
est.s[i] = None
est.m[i] = None
return est, costs示例2: decoder
# 需要導入模塊: from utils import AttributeDict [as 別名]
# 或者: from utils.AttributeDict import denois[i] [as 別名]
def decoder(self, clean, corr, batch_size):
get_unlabeled = lambda x: x[batch_size:] if x is not None else x
est = self.new_activation_dict()
costs = AttributeDict()
costs.denois = AttributeDict()
for i, ((_, spec), act_f) in self.layers[::-1]:
z_corr = get_unlabeled(corr.z[i])
z_clean = get_unlabeled(clean.z[i])
z_clean_s = get_unlabeled(clean.s.get(i))
z_clean_m = get_unlabeled(clean.m.get(i))
# It's the last layer
if i == len(self.layers) - 1:
fspec = (None, None)
ver = get_unlabeled(corr.h[i])
ver_dim = self.layer_dims[i]
top_g = True
else:
fspec = self.layers[i + 1][1][0]
ver = est.z.get(i + 1)
ver_dim = self.layer_dims.get(i + 1)
top_g = False
z_est = self.g(
z_lat=z_corr, z_ver=ver, in_dims=ver_dim, out_dims=self.layer_dims[i], num=i, fspec=fspec, top_g=top_g
)
# For semi-supervised version
if z_clean_s:
z_est_norm = (z_est - z_clean_m) / z_clean_s
else:
z_est_norm = z_est
z_est_norm = z_est
se = SquaredError("denois" + str(i))
costs.denois[i] = se.apply(z_est_norm.flatten(2), z_clean.flatten(2)) / np.prod(
self.layer_dims[i], dtype=floatX
)
costs.denois[i].name = "denois" + str(i)
# Store references for later use
est.z[i] = z_est
est.h[i] = apply_act(z_est, act_f)
est.s[i] = None
est.m[i] = None
return est, costs