def __call__(self, net, input, target):
if not self.adapt:
while True:
g, output = self.calc(net, input, target)
# regularization grad
if self.rr > 0:
g = tool.reg_grad(g, net, self.rr)
e = self.error(net, input, target, output)
# regularization error
if self.rr:
e = tool.reg_error(e, net, self.rr)
self.epochf(e, net, input, target)
self.learn(net, g)
else:
while True:
for i in range(input.shape[0]):
g = self.calc(net, [input[i]], [target[i]])[0]
# regularization grad
if self.rr > 0:
g = tool.reg_grad(g, net, self.rr)
self.learn(net, g)
e = self.error(net, input, target)
# regularization error
if self.rr:
e = tool.reg_error(e, self.rr, net)
self.epochf(e, net, input, target)
return None
def fcn(self, x):
self.x[:] = x
err = self.error(self.net, self.input, self.target)
if self.rr:
eee = tool.reg_error(err, self.net, self.rr)
self.lerr = err
return err
def fcn(self, x):
self.x[:] = x
err = self.error(self.net, self.input, self.target)
if self.rr:
eee = tool.reg_error(err, self.net, self.rr)
self.lerr = err
return err