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src/p/y/pylearn2-HEAD/pylearn2/scripts/tutorials/grbm_smd/make_dataset.py   pylearn2(Download)
    # to train an RBM on these
    pipeline.items.append(
        preprocessing.ExtractPatches(patch_shape=(8, 8), num_patches=150000)
    )
 
    # Next we contrast normalize the patches. The default arguments use the
    # same "regularization" parameters as those used in Adam Coates, Honglak
    # Lee, and Andrew Ng's paper "An Analysis of Single-Layer Networks in
    # Unsupervised Feature Learning"
    pipeline.items.append(preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
    # 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)
print "Preprocessing the data..."
pipeline = preprocessing.Pipeline()
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())

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_stl10_patches.py   pylearn2(Download)
print "Preprocessing the data..."
pipeline = preprocessing.Pipeline()
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())

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_cifar100_patches_8x8.py   pylearn2(Download)
print "Preprocessing the data..."
pipeline = preprocessing.Pipeline()
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())

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/make_cifar100_patches.py   pylearn2(Download)
print "Preprocessing the data..."
pipeline = preprocessing.Pipeline()
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())

src/p/y/pylearn2-HEAD/pylearn2/scripts/papers/maxout/svhn_preprocessing.py   pylearn2(Download)
# without batch_size there is a high chance that you might encounter memory error
# or pytables crashes
pipeline.items.append(preprocessing.GlobalContrastNormalization(batch_size=5000))
pipeline.items.append(preprocessing.LeCunLCN((32,32)))
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/lcc_tangents/make_dataset.py   pylearn2(Download)
 
pipeline = preprocessing.Pipeline()
pipeline.items.append(preprocessing.GlobalContrastNormalization(subtract_mean=False,sqrt_bias=0.0, use_std=True))
pipeline.items.append(preprocessing.PCA(num_components=512))