本文整理匯總了Python中db.DB.finishLoadingTrainingset方法的典型用法代碼示例。如果您正苦於以下問題：Python DB.finishLoadingTrainingset方法的具體用法？Python DB.finishLoadingTrainingset怎麽用？Python DB.finishLoadingTrainingset使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類db.DB的用法示例。
在下文中一共展示了DB.finishLoadingTrainingset方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: load
# 需要導入模塊: from db import DB [as 別名]
# 或者: from db.DB import finishLoadingTrainingset [as 別名]
def load(self, project):
print 'data load...'
if self.offline:
d = self.gen_samples_offline(
nsamples=self.n_train_samples,
purpose='train',
patchSize=self.project.patchSize,
mean=self.project.mean,
std=self.project.std)
self.x = d[0]
self.y = d[1]
d = self.gen_samples_offline(
nsamples=self.n_valid_samples,
purpose='validate',
patchSize=self.project.patchSize,
mean=d[2],
std=d[3])
self.x_valid = d[0]
self.y_valid = d[1]
print 'x:', np.shape(self.x)
print 'y:', np.shape(self.y)
print 'xvalid:', np.shape(self.x_valid)
print 'yvalid:', np.shape(self.y_valid)
else:
self.load_validation()
self.load_training()
DB.finishLoadingTrainingset( project.id )示例2: aload
# 需要導入模塊: from db import DB [as 別名]
# 或者: from db.DB import finishLoadingTrainingset [as 別名]
def aload(self, project):
self.projecft = project
# LOAD TRAINING DATA
train_new = len(self.entries) > 0
images = DB.getImages( project.id, purpose=0, new=train_new)
out = self.load_data(
Paths.TrainGrayscale,
images,
project,
self.x,
self.y,
self.p,
self.entries)
self.x = out[0]
self.y = out[1]
self.p = out[2]
self.entries = out[3]
n_samples = len(self.y)
self.i = np.arange( n_samples )
self.n_superbatch = int(n_samples/(Data.TrainSuperBatchSize + Data.ValidSuperBatchSize))
self.i_randomize = 0
self.data_changed = True
self.i_train = []
self.avg_losses = []
self.last_avg_loss = 0
if n_samples > 0:
Utility.report_status('---------training---------', '')
Utility.report_status('#samples','(%d)'%len(self.y))
Utility.report_status('x shape','(%d,%d)'%(self.x.shape[0], self.x.shape[1]))
Utility.report_status('y shape','(%d)'%(self.x.shape[0]))
Utility.report_status('x memory', '(%d bytes)'%(self.x.nbytes))
Utility.report_status('y memory', '(%d bytes)'%(self.y.nbytes))
print 'min:', np.min( self.x )
print 'max:', np.max( self.x )
print 'uy:', np.unique( self.y )
print 'x:', self.x[:5]
print 'y:', self.y[:5]
# LOAD VALIDATION IMAGES
valid_new = len(self.entries_valid) > 0
images = DB.getImages( project.id, purpose=1, new=valid_new )
out = self.load_data(
Paths.ValidGrayscale,
images,
project,
self.x_valid,
self.y_valid,
self.p_valid,
self.entries_valid)
self.x_valid = out[0]
self.y_valid = out[1]
self.p_valid = out[2]
self.entries_valid = out[3]
n_samples = len(self.y_valid)
self.i_valid = np.arange( n_samples )
if n_samples > 0:
Utility.report_status('---------validation---------', '')
Utility.report_status('#samples','(%d)'%len(self.y_valid))
Utility.report_status('x shape','(%d,%d)'%(self.x_valid.shape[0], self.x_valid.shape[1]))
Utility.report_status('y shape','(%d)'%(self.x_valid.shape[0]))
Utility.report_status('x memory', '(%d bytes)'%(self.x_valid.nbytes))
Utility.report_status('y memory', '(%d bytes)'%(self.y_valid.nbytes))
print 'min:', np.min( self.x_valid )
print 'max:', np.max( self.x_valid )
print 'uy:', np.unique( self.y_valid )
print 'x:', self.x_valid[:5]
print 'y:', self.y_valid[:5]
Utility.report_memused()
DB.finishLoadingTrainingset( project.id )