def train_gradient_descent(self, **kwargs):
"""
f_info will be overwritten here.
"""
self._prepare_for_training()
self.load_train_target()
if 'f_info' not in kwargs:
kwargs['f_info'] = self._f_info_decorated
if 'f_exe' not in kwargs:
kwargs['f_exe'] = self._f_exe_decorated
self._general_option_processing(kwargs)
self._process_options(kwargs)
self.print_options(kwargs)
opt.fmin_gradient_descent(self.f_and_fprime, self.init_w, **kwargs)
return self._f_post_training_decorated()
def train_gradient_descent(self, **kwargs):
"""
f_info will be overwritten here.
"""
self._prepare_for_training()
self.load_train_target()
if 'f_info' not in kwargs:
kwargs['f_info'] = self._f_info_decorated
if 'f_exe' not in kwargs:
kwargs['f_exe'] = self._f_exe_decorated
self._general_option_processing(kwargs)
self._process_options(kwargs)
self.print_options(kwargs)
opt.fmin_gradient_descent(self.f_and_fprime, self.init_w, **kwargs)
return self._f_post_training_decorated()
def test_gradient_descent(weight_decay=0):
print 'Testing gradient descent weight_decay=%g' % weight_decay
def f(x):
return ((x - 1)**2).sum() / 2.0, x - 1
def f_info(x):
return 'x=%s' % str(x)
x0 = np.array([2, 0], dtype=np.float)
x_opt = opt.fmin_gradient_descent(f,
x0,
learn_rate=0.6,
momentum=0.0,
weight_decay=weight_decay,
learn_rate_drop_iters=5,
adagrad_start_iter=1,
max_iters=20,
iprint=1,
f_info=f_info,
verbose=True)
x_star = np.ones(2, dtype=np.float) / (1 + weight_decay)
success = test_vec_pair(x_star, 'x_star', x_opt, 'x_opt ')
print ''
return success
def train_sgd(self, **kwargs):
"""
f_info will be overwritten here.
"""
self.print_options(kwargs)
self._prepare_for_training()
if 'minibatch_size' in kwargs:
minibatch_size = kwargs['minibatch_size']
del kwargs['minibatch_size']
else:
minibatch_size = 100
self.create_minibatch_generator(minibatch_size)
if 'f_info' not in kwargs:
kwargs['f_info'] = self._f_info_decorated
if 'f_exe' not in kwargs:
kwargs['f_exe'] = self._f_exe_decorated
self._general_option_processing(kwargs)
self._process_options(kwargs)
opt.fmin_gradient_descent(self.f_and_fprime_minibatch, self.init_w, **kwargs)
return self._f_post_training_decorated()
def train_sgd(self, **kwargs):
"""
f_info will be overwritten here.
"""
self.print_options(kwargs)
self._prepare_for_training()
if 'minibatch_size' in kwargs:
minibatch_size = kwargs['minibatch_size']
del kwargs['minibatch_size']
else:
minibatch_size = 100
self.create_minibatch_generator(minibatch_size)
if 'f_info' not in kwargs:
kwargs['f_info'] = self._f_info_decorated
if 'f_exe' not in kwargs:
kwargs['f_exe'] = self._f_exe_decorated
self._general_option_processing(kwargs)
self._process_options(kwargs)
opt.fmin_gradient_descent(self.f_and_fprime_minibatch, self.init_w,
**kwargs)
return self._f_post_training_decorated()
