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src/p/y/pylearn2-HEAD/pylearn2/scripts/icml_2013_wrepl/black_box/learn_zca.py   pylearn2(Download)
from pylearn2.datasets.preprocessing import ZCA
from pylearn2.utils import serial
 
from black_box_dataset import BlackBoxDataset
 
extra = BlackBoxDataset('extra')
 
zca = ZCA(filter_bias=.1)

src/p/y/pylearn2-HEAD/pylearn2/scripts/tutorials/grbm_smd/make_dataset.py   pylearn2(Download)
    # ZCA are set to the same values as those used in the previously mentioned
    # paper.
    pipeline.items.append(preprocessing.ZCA())
 
    # Here we apply the preprocessing pipeline to the dataset. The can_fit

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_stl10_patches_8x8.py   pylearn2(Download)
pipeline.items.append(preprocessing.ExtractPatches(patch_shape=(8,8),num_patches=2*1000*1000))
pipeline.items.append(preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
pipeline.items.append(preprocessing.ZCA())
data.apply_preprocessor(preprocessor = pipeline, can_fit = True)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_stl10_patches.py   pylearn2(Download)
pipeline.items.append(preprocessing.ExtractPatches(patch_shape=(6,6),num_patches=2*1000*1000))
pipeline.items.append(preprocessing.GlobalContrastNormalization(use_std=True, sqrt_bias=10.))
pipeline.items.append(preprocessing.ZCA())
data.apply_preprocessor(preprocessor = pipeline, can_fit = True)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_cifar100_patches_8x8.py   pylearn2(Download)
pipeline.items.append(preprocessing.ExtractPatches(patch_shape=(8,8),num_patches=2*1000*1000))
pipeline.items.append(preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
pipeline.items.append(preprocessing.ZCA())
data.apply_preprocessor(preprocessor = pipeline, can_fit = True)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_cifar100_patches.py   pylearn2(Download)
pipeline.items.append(preprocessing.ExtractPatches(patch_shape=(6,6),num_patches=2*1000*1000))
pipeline.items.append(preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
pipeline.items.append(preprocessing.ZCA())
data.apply_preprocessor(preprocessor = pipeline, can_fit = True)
 

src/k/a/kaggle-cifar10-HEAD/kaggle_train.py   kaggle-cifar10(Download)
from pylearn2.datasets import cifar10
from kaggle_dataset import kaggle_cifar10
from pylearn2.datasets.preprocessing import Pipeline, ZCA
from pylearn2.datasets.preprocessing import GlobalContrastNormalization
from pylearn2.space import Conv2DSpace
                                      'train': trn})
 
preprocessor = Pipeline([GlobalContrastNormalization(scale=55.), ZCA()])
trn.apply_preprocessor(preprocessor=preprocessor, can_fit=True)
tst.apply_preprocessor(preprocessor=preprocessor, can_fit=False)

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_stl10_whitened.py   pylearn2(Download)
 
print "Learning the preprocessor and preprocessing the unsupervised train data..."
preprocessor = preprocessing.ZCA()
data.apply_preprocessor(preprocessor = preprocessor, can_fit = True)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_cifar10_whitened.py   pylearn2(Download)
 
print "Learning the preprocessor and preprocessing the unsupervised train data..."
preprocessor = preprocessing.ZCA()
train.apply_preprocessor(preprocessor = preprocessor, can_fit = True)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_cifar10_gcn_whitened.py   pylearn2(Download)
 
print "Learning the preprocessor and preprocessing the unsupervised train data..."
preprocessor = preprocessing.ZCA()
train.apply_preprocessor(preprocessor = preprocessor, can_fit = True)
 

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