本文整理匯總了Python中tflib.inception_score方法的典型用法代碼示例。如果您正苦於以下問題：Python tflib.inception_score方法的具體用法？Python tflib.inception_score怎麽用？Python tflib.inception_score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tflib的用法示例。
在下文中一共展示了tflib.inception_score方法的4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: get_inception_score
# 需要導入模塊: import tflib [as 別名]
# 或者: from tflib import inception_score [as 別名]
def get_inception_score():
all_samples = []
for i in xrange(10):
all_samples.append(session.run(samples_100))
all_samples = np.concatenate(all_samples, axis=0)
all_samples = ((all_samples+1.)*(255./2)).astype('int32')
all_samples = all_samples.reshape((-1, 3, 32, 32)).transpose(0,2,3,1)
return lib.inception_score.get_inception_score(list(all_samples))
# Dataset iterators 示例2: get_inception_score
# 需要導入模塊: import tflib [as 別名]
# 或者: from tflib import inception_score [as 別名]
def get_inception_score(n):
all_samples = []
for i in xrange(n/100):
all_samples.append(session.run(samples_100))
all_samples = np.concatenate(all_samples, axis=0)
all_samples = ((all_samples+1.)*(255.99/2)).astype('int32')
all_samples = all_samples.reshape((-1, 3, 32, 32)).transpose(0,2,3,1)
return lib.inception_score.get_inception_score(list(all_samples)) 示例3: get_inception_score
# 需要導入模塊: import tflib [as 別名]
# 或者: from tflib import inception_score [as 別名]
def get_inception_score(G, ):
all_samples = []
for i in xrange(10):
samples_100 = torch.randn(100, 128)
if use_cuda:
samples_100 = samples_100.cuda(gpu)
samples_100 = autograd.Variable(samples_100, volatile=True)
all_samples.append(G(samples_100).cpu().data.numpy())
all_samples = np.concatenate(all_samples, axis=0)
all_samples = np.multiply(np.add(np.multiply(all_samples, 0.5), 0.5), 255).astype('int32')
all_samples = all_samples.reshape((-1, 3, 32, 32)).transpose(0, 2, 3, 1)
return lib.inception_score.get_inception_score(list(all_samples))
# Dataset iterator 示例4: get_inception_score
# 需要導入模塊: import tflib [as 別名]
# 或者: from tflib import inception_score [as 別名]
def get_inception_score():
all_samples = []
for i in range(10):
all_samples.append(session.run(samples_100))
all_samples = np.concatenate(all_samples, axis=0)
all_samples = ((all_samples+1.)*(255./2)).astype('int32')
all_samples = all_samples.reshape((-1, 3, 32, 32)).transpose(0,2,3,1)
return lib.inception_score.get_inception_score(list(all_samples))
# Dataset iterators