def f_info(self, w):
train_loss = None
val_loss = None
s = self.net.get_status_info()
if len(s) > 0: s += ', '
w_0 = self.net.get_param_vec()
self.net.set_noiseless_param_from_vec(w)
self.setup_batch_normalization_mean_std()
self.net.load_target(self.t_train)
y = self.net.forward_prop(self.x_train,
add_noise=False,
compute_loss=True,
is_test=True)
train_loss = self.net.get_loss() / self.x_train.shape[0]
train_acc = self._compute_accuracy(self.t_train, y.argmax(axis=1))
if self.use_validation:
self.net.load_target(self.t_val)
y = self.net.forward_prop(self.x_val,
add_noise=False,
compute_loss=True,
is_test=True)
val_loss = self.net.get_loss() / self.x_val.shape[0]
val_acc = self._compute_accuracy(self.t_val, y.argmax(axis=1))
self.net.load_target(self.t_train)
s += 'train loss %.4f, acc %.4f, val loss %.4f, acc ' % (
train_loss, train_acc, val_loss)
if self.best_obj is None or val_acc > self.best_obj:
self.best_obj = val_acc
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % val_acc)
else:
s += '%.4f' % val_acc
else:
s += 'train loss %.4f, acc ' % train_loss
if self.best_obj is None or train_acc < self.best_obj:
self.best_obj = train_acc
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % train_acc)
else:
s += '%.4f' % train_acc
self.net.set_param_from_vec(w_0)
return s
def f_info(self, w):
"""
This is a reference implementatoin of this function, but can be
customized for other learners as well.
"""
train_loss = None
val_loss = None
w_0 = self.net.get_param_vec()
self.net.set_noiseless_param_from_vec(w)
#self.net.load_target(self.t_train)
#self.net.forward_prop(self.x_train, add_noise=False, compute_loss=True)
#train_loss = self.net.get_loss() / self.x_train.shape[0]
train_loss = self.evaluate_loss_large_set(self.x_train, self.t_train)
if self.use_validation:
#self.net.load_target(self.t_val)
#self.net.forward_prop(self.x_val, add_noise=False, compute_loss=True)
#val_loss = self.net.get_loss() / self.x_val.shape[0]
val_loss = self.evaluate_loss_large_set(self.x_val, self.t_val)
s = 'train loss %.4f, val loss ' % train_loss
if self.best_obj is None or val_loss < self.best_obj:
self.best_obj = val_loss
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % val_loss)
else:
s += '%.4f' % val_loss
else:
s = 'train loss '
if self.best_obj is None or train_loss < self.best_obj:
self.best_obj = train_loss
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % train_loss)
else:
s += '%.4f' % train_loss
self.net.load_target(self.t_train)
self.net.set_param_from_vec(w_0)
net_status = self.net.get_status_info()
if len(net_status) > 0:
s += ', ' + net_status
return s
def f_info(self, w):
"""
This is a reference implementatoin of this function, but can be
customized for other learners as well.
"""
train_loss = None
val_loss = None
w_0 = self.net.get_param_vec()
self.net.set_noiseless_param_from_vec(w)
self.setup_batch_normalization_mean_std()
train_loss = self.evaluate_loss_large_set(self.x_train, self.t_train)
s = self.net.get_status_info()
if len(s) > 0:
s += ', '
if self.use_validation:
val_loss = self.evaluate_loss_large_set(self.x_val, self.t_val)
s += 'train loss %.4f, val loss ' % train_loss
if self.best_obj is None or val_loss < self.best_obj:
self.best_obj = val_loss
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % val_loss)
else:
s += '%.4f' % val_loss
else:
s += 'train loss '
if self.best_obj is None or train_loss < self.best_obj:
self.best_obj = train_loss
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % train_loss)
else:
s += '%.4f' % train_loss
# self.net.load_target(self.t_train)
self.net.set_param_from_vec(w_0)
return s
def f_info(self, w):
"""
This is a reference implementatoin of this function, but can be
customized for other learners as well.
"""
train_loss = None
val_loss = None
w_0 = self.net.get_param_vec()
self.net.set_noiseless_param_from_vec(w)
self.setup_batch_normalization_mean_std()
train_loss = self.evaluate_loss_large_set(self.x_train, self.t_train)
s = self.net.get_status_info()
if len(s) > 0:
s += ', '
if self.use_validation:
val_loss = self.evaluate_loss_large_set(self.x_val, self.t_val)
s += 'train loss %.4f, val loss ' % train_loss
if self.best_obj is None or val_loss < self.best_obj:
self.best_obj = val_loss
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % val_loss)
else:
s += '%.4f' % val_loss
else:
s += 'train loss '
if self.best_obj is None or train_loss < self.best_obj:
self.best_obj = train_loss
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % train_loss)
else:
s += '%.4f' % train_loss
# self.net.load_target(self.t_train)
self.net.set_param_from_vec(w_0)
return s
def f_info(self, w):
train_loss = None
val_loss = None
s = self.net.get_status_info()
if len(s) > 0: s += ', '
w_0 = self.net.get_param_vec()
self.net.set_noiseless_param_from_vec(w)
self.setup_batch_normalization_mean_std()
self.net.load_target(self.t_train)
y = self.net.forward_prop(self.x_train, add_noise=False, compute_loss=True, is_test=True)
train_loss = self.net.get_loss() / self.x_train.shape[0]
train_acc = self._compute_accuracy(self.t_train, y.argmax(axis=1))
if self.use_validation:
self.net.load_target(self.t_val)
y = self.net.forward_prop(self.x_val, add_noise=False, compute_loss=True, is_test=True)
val_loss = self.net.get_loss() / self.x_val.shape[0]
val_acc = self._compute_accuracy(self.t_val, y.argmax(axis=1))
self.net.load_target(self.t_train)
s += 'train loss %.4f, acc %.4f, val loss %.4f, acc ' % (train_loss, train_acc, val_loss)
if self.best_obj is None or val_acc > self.best_obj:
self.best_obj = val_acc
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % val_acc)
else:
s += '%.4f' % val_acc
else:
s += 'train loss %.4f, acc ' % train_loss
if self.best_obj is None or train_acc < self.best_obj:
self.best_obj = train_acc
self.best_w = w.copy()
s += co.good_colored_str('%.4f' % train_acc)
else:
s += '%.4f' % train_acc
self.net.set_param_from_vec(w_0)
return s
