本文整理汇总了Python中nnabla.functions.squared_error函数的典型用法代码示例。如果您正苦于以下问题：Python squared_error函数的具体用法？Python squared_error怎么用？Python squared_error使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了squared_error函数的11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: sigma_regularization
def sigma_regularization(ctx, log_var, one):
with nn.context_scope(ctx):
h = F.exp(log_var)
h = F.pow_scalar(h, 0.5)
b = log_var.shape[0]
r = F.sum(F.squared_error(h, one)) / b
return r示例2: sigma_regularization
def sigma_regularization(ctx, log_var, one):
with nn.context_scope(ctx):
h = F.exp(log_var)
h = F.pow_scalar(h, 0.5)
h = F.mean(h, axis=1)
r = F.mean(F.squared_error(h, one))
return r示例3: sr_loss_with_uncertainty
def sr_loss_with_uncertainty(ctx, pred0, pred1, log_var0, log_var1):
#TODO: squared error/absolute error
s0 = F.exp(log_var0)
s1 = F.exp(log_var1)
squared_error = F.squared_error(pred0, pred1)
with nn.context_scope(ctx):
loss_sr = F.mean(squared_error * (1 / s0 + 1 / s1) + (s0 / s1 + s1 / s0)) * 0.5
return loss_sr示例4: sigmas_regularization
def sigmas_regularization(ctx, log_var0, log_var1):
with nn.context_scope(ctx):
h0 = F.exp(log_var0)
h0 = F.pow_scalar(h0, 0.5)
h1 = F.exp(log_var1)
h1 = F.pow_scalar(h1, 0.5)
r = F.mean(F.squared_error(h0, h1))
return r示例5: mnist_lenet_siamese
def mnist_lenet_siamese(x0, x1, test=False):
""""""
h0 = mnist_lenet_feature(x0, test)
h1 = mnist_lenet_feature(x1, test) # share weights
# h = (h0 - h1) ** 2 # equivalent
h = F.squared_error(h0, h1)
p = F.sum(h, axis=1)
return p示例6: sr_loss_with_uncertainty
def sr_loss_with_uncertainty(ctx, pred0, pred1, log_var0, log_var1):
var0 = F.exp(log_var0)
var1 = F.exp(log_var1)
s0 = F.pow_scalar(var0, 0.5)
s1 = F.pow_scalar(var0, 0.5)
squared_error = F.squared_error(pred0, pred1)
with nn.context_scope(ctx):
loss = F.log(s1/s0) + (var0/var1 + squared_error/var1) * 0.5
loss_sr = F.mean(loss)
return loss_sr示例7: sr_loss_with_uncertainty
def sr_loss_with_uncertainty(ctx, pred0, pred1, log_v0, log_v1,
log_s0, log_s1):
v0 = F.exp(log_v0)
v1 = F.exp(log_v1)
squared_error = F.squared_error(pred0, pred1)
s0 = F.exp(log_s0)
s1 = F.exp(log_s1)
with nn.context_scope(ctx):
error = squared_error * (1 / v0 + 1 / v1) + (v0 / v1 + v1 / v0) + (s0 / s1 + s1 / s0)
loss_sr = F.mean(error) * 0.5
return loss_sr示例8: sr_loss_with_uncertainty_and_coef
def sr_loss_with_uncertainty_and_coef(ctx, pred0, pred1, log_var0, log_var1):
c0 = srwu_learned_coef(ctx, log_var0)
c1 = srwu_learned_coef(ctx, log_var1)
sc0 = sigmas_learned_coef(ctx, log_var0, log_var1)
sc1 = sigmas_learned_coef(ctx, log_var1, log_var0)
c0.need_grad = False
c1.need_grad = False
sc0.need_grad = False
sc1.need_grad = False
#TODO: squared error/absolute error
s0 = F.exp(log_var0)
s1 = F.exp(log_var1)
squared_error = F.squared_error(pred0, pred1)
with nn.context_scope(ctx):
loss_sr = F.mean(
squared_error * (c0 / s0 + c1 / s1) + (sc0 * s0 / s1 + sc1 * s1 / s0)) * 0.5
return loss_sr示例9: sr_loss
def sr_loss(ctx, pred0, pred1):
with nn.context_scope(ctx):
loss_sr = F.mean(F.squared_error(pred0, pred1))
return loss_sr示例10: sr_loss
def sr_loss(ctx, pred0, pred1):
with nn.context_scope(ctx):
pred_x_u0 = F.softmax(pred0)
pred_x_u1 = F.softmax(pred1)
loss_sr = F.mean(F.squared_error(pred_x_u0, pred_x_u1))
return loss_sr示例11: recon_loss
def recon_loss(ctx, pred, x_l):
with nn.context_scope(ctx):
loss_recon = F.mean(F.squared_error(pred, x_l))
return loss_recon