def loss(self, args, X):
if type(args) is not list:
args = args_from_vector(args, self.params)
# Reshape theta -> args
args += [X]
# Compute gradient from args
loss = self._loss(*args)
return loss
def f_df(self, args, X):
if type(args) is not list:
args = args_from_vector(args, self.params)
args += [X]
# Compute gradient from args
#grad = vector_from_args(self.grads(*args))
grad = self.grads(*args)
loss = self._loss(*args)
return loss,grad
def get_weights(self, theta):
args = args_from_vector(theta, self.params)
for arg in args:
if arg.ndim == 2:
return arg
def get_args(self, theta):
args = args_from_vector(theta, self.params)
return args
def set_params(self, theta):
params = args_from_vector(theta, [self.W, self.hidbias, self.visbias])
self.W = params[0]
self.hidbias = params[1]
self.visbias = params[2]