def load_cifar10(path=".",
normalize=True,
contrast_normalize=False,
whiten=False):
cfiar10_dataset = CIFAR10(path=path,
normalize=normalize,
contrast_normalize=contrast_normalize,
whiten=whiten)
return cfiar10_dataset.load_data()
def global_contrast_normalize(X, scale=1., min_divisor=1e-8):
return CIFAR10.global_contrast_normalize(X, scale=scale, min_divisor=min_divisor)
def zca_whiten(train, test, cache=None):
return CIFAR10.zca_whiten(train, test, cache=cache)
def _compute_zca_transform(imgs, filter_bias=0.1):
return CIFAR10._compute_zca_transform(imgs, filter_bias=filter_bias)
def global_contrast_normalize(X, scale=1., min_divisor=1e-8):
return CIFAR10.global_contrast_normalize(X, scale=scale, min_divisor=min_divisor)
def zca_whiten(train, test, cache=None):
return CIFAR10.zca_whiten(train, test, cache=cache)
def _compute_zca_transform(imgs, filter_bias=0.1):
return CIFAR10._compute_zca_transform(imgs, filter_bias=filter_bias)