def load_contrast(n=13,
k=.01,
filter="box",
contrast="mean",
want_dense=False):
X = TorontoFace.load(want_mean=False, want_dense=True)
X = X[:100000]
X_mean = X.mean(axis=1)
X_std = (X.var(1) + 10)**.5
X = (X - X_mean[:, np.newaxis]) / X_std[:, np.newaxis]
X = X.reshape(-1, 1, 48, 48)
if filter == "box":
filters = nn.ones((n, n))
elif filter == "gaussian":
filters = nn.gaussian2D(n, k)
else:
raise Exception("unhandled case")
X_cn = nn.zeros(X.shape)
for i in xrange(10):
if contrast == "mean":
X_cn[10000 * i:10000 *
(i + 1), :, :, :] = nn.SpatialContrastMean(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
elif contrast == "mean-var":
X_cn[10000 * i:10000 * (i + 1), :, :, :] = nn.SpatialContrast(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
else:
raise Exception("unhandled case")
if want_dense: X_cn = X_cn.reshape(-1, 48 * 48)
return X_cn
def load_contrast(size=32, n=19,k=.01,filter="gaussian",contrast="mean-var"):
X,T,X_test,T_test,T_train_labels,T_labels = NORB.load(size=size,want_mean=False,want_dense=True)
X = X[:20000]
X_mean=X.mean(axis=1)
X_std = (X.var(1)+10)**.5
X = (X-X_mean[:, np.newaxis])/X_std[:, np.newaxis]
X = X.reshape(-1,1,32,32)
if filter=="box":
filters = nn.ones((n,n))
elif filter=="gaussian":
filters = nn.gaussian2D(n,k)
else:
raise Exception("unhandled case")
X_cn = nn.zeros(X.shape)
for i in xrange(2):
if contrast=="mean":
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrastMean(X[10000*i:10000*(i+1),:,:,:], filters)
elif contrast=="mean-var":
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X[10000*i:10000*(i+1),:,:,:], filters)
else:
raise Exception("unhandled case")
X_cn = X_cn.reshape(-1,1,size,size)
return X_cn,T,X_test,T_test,T_train_labels,T_labels
def load_contrast(n=13,k=.01,filter="box",contrast="mean",want_dense = False):
X = TorontoFace.load(want_mean=False,want_dense=True)
X = X[:100000]
X_mean=X.mean(axis=1)
X_std = (X.var(1)+10)**.5
X = (X-X_mean[:, np.newaxis])/X_std[:, np.newaxis]
X = X.reshape(-1,1,48,48)
if filter=="box":
filters = nn.ones((n,n))
elif filter=="gaussian":
filters = nn.gaussian2D(n,k)
else:
raise Exception("unhandled case")
X_cn = nn.zeros(X.shape)
for i in xrange(10):
if contrast=="mean":
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrastMean(X[10000*i:10000*(i+1),:,:,:], filters)
elif contrast=="mean-var":
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X[10000*i:10000*(i+1),:,:,:], filters)
else:
raise Exception("unhandled case")
if want_dense: X_cn = X_cn.reshape(-1,48*48)
return X_cn
def load_contrast(size=32,
n=19,
k=.01,
filter="gaussian",
contrast="mean-var"):
X, T, X_test, T_test, T_train_labels, T_labels = NORB.load(
size=size, want_mean=False, want_dense=True)
X = X[:20000]
X_mean = X.mean(axis=1)
X_std = (X.var(1) + 10)**.5
X = (X - X_mean[:, np.newaxis]) / X_std[:, np.newaxis]
X = X.reshape(-1, 1, 32, 32)
if filter == "box":
filters = nn.ones((n, n))
elif filter == "gaussian":
filters = nn.gaussian2D(n, k)
else:
raise Exception("unhandled case")
X_cn = nn.zeros(X.shape)
for i in xrange(2):
if contrast == "mean":
X_cn[10000 * i:10000 *
(i + 1), :, :, :] = nn.SpatialContrastMean(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
elif contrast == "mean-var":
X_cn[10000 * i:10000 * (i + 1), :, :, :] = nn.SpatialContrast(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
else:
raise Exception("unhandled case")
X_cn = X_cn.reshape(-1, 1, size, size)
return X_cn, T, X_test, T_test, T_train_labels, T_labels
def load_contrast(n=13,k=.01,want_dense=False,filter="gaussian",contrast="mean"):
X,T,X_test,T_test,T_train_labels,T_labels = CIFAR10.load(want_mean=False,want_dense=True)
# for i in xrange(3):
# mean = X[:,i,:,:].mean()
# std = X[:,i,:,:].std()
# X[:,i,:,:] = (X[:,i,:,:] - mean)/std
# X_test[:,i,:,:] = (X_test[:,i,:,:] - mean)/std
X_mean=X.mean(axis=1)
X_test_mean=X_test.mean(axis=1)
X_std = X.std(1)
X_test_std = X_test.std(1)
X = (X-X_mean[:, np.newaxis])/X_std[:, np.newaxis]
X_test = (X_test-X_test_mean[:, np.newaxis])/X_test_std[:, np.newaxis]
X = X.reshape(50000,3,32,32)
X_test = X_test.reshape(10000,3,32,32)
if filter=="box":
filters = nn.ones((n,n))
elif filter=="gaussian":
filters = nn.gaussian2D(n,k)
else:
raise Exception("unhandled case")
X_cn = nn.zeros(X.shape)
X_test_cn = nn.zeros(X_test.shape)
for i in xrange(5):
if contrast=="mean":
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrastMean(X[10000*i:10000*(i+1),:,:,:], filters)
elif contrast=="mean-var":
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X[10000*i:10000*(i+1),:,:,:], filters)
else:
raise Exception("unhandled case")
for i in xrange(1):
if contrast=="mean":
X_test_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrastMean(X_test[10000*i:10000*(i+1),:,:,:], filters)
elif contrast=="mean-var":
X_test_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X_test[10000*i:10000*(i+1),:,:,:], filters)
else:
raise Exception("unhandled case")
if want_dense:
X_cn = X_cn.reshape(50000,3072)
X_test_cn = X_test_cn.reshape(10000,3072)
return X_cn,T,X_test_cn,T_test,T_train_labels,T_labels
def load_contrast_extra(n=21, k=.01):
X, T, X_test, T_test, T_train_labels, T_labels = SVHN.load_extra(
want_mean=False, want_dense=True)
print "Contrast Normalization."
X_mean = X.mean(axis=1)
X_test_mean = X_test.mean(axis=1)
X_std = X.std(1)
X_test_std = X_test.std(1)
X = (X - X_mean[:, np.newaxis]) / X_std[:, np.newaxis]
X_test = (X_test - X_test_mean[:, np.newaxis]) / X_test_std[:,
np.newaxis]
X = X.reshape(600000, 3, 32, 32)
X_test = X_test.reshape(20000, 3, 32, 32)
filters = nn.gaussian2D(n, k)
X_cn = np.zeros(X.shape)
X_test_cn = np.zeros(X_test.shape)
for i in xrange(60):
if i % 10 == 0: print i,
X_cn[i * 10000:(i + 1) * 10000, :, :, :] = nn.SpatialContrast(
X[i * 10000:(i + 1) * 10000, :, :, :], filters)
for i in xrange(2):
X_test_cn[10000 * i:10000 * (i + 1), :, :, :] = nn.SpatialContrast(
X_test[10000 * i:10000 * (i + 1), :, :, :], filters)
h5f = h5py.File(
'/media/nas/PSI-Share-no-backup/Ali/Dataset/SVHN/extra_contrast21.01_new.h5',
'w')
print "file created."
h5f.create_dataset('X', data=X_cn)
h5f.create_dataset('T', data=T)
h5f.create_dataset('T_train_labels', data=T_train_labels)
print "train done."
h5f.create_dataset('X_test', data=X_test_cn)
h5f.create_dataset('T_test', data=T_test)
h5f.create_dataset('T_labels', data=T_labels)
return X_cn, T, X_test_cn, T_test, T_train_labels, T_labels
def load_contrast(n=21, k=.01, want_dense=False):
X, T, X_test, T_test, T_train_labels, T_labels = SVHN.load(
want_mean=False, want_dense=True)
# for i in xrange(3):
# mean = X[:,i,:,:].mean()
# std = X[:,i,:,:].std()
# X[:,i,:,:] = (X[:,i,:,:] - mean)/std
# X_test[:,i,:,:] = (X_test[:,i,:,:] - mean)/std
X_mean = X.mean(axis=1)
X_test_mean = X_test.mean(axis=1)
X_std = X.std(1)
X_test_std = X_test.std(1)
X = (X - X_mean[:, np.newaxis]) / X_std[:, np.newaxis]
X_test = (X_test - X_test_mean[:, np.newaxis]) / X_test_std[:,
np.newaxis]
X = X.reshape(70000, 3, 32, 32)
X_test = X_test.reshape(20000, 3, 32, 32)
filters = nn.gaussian2D(n, k)
X_cn = nn.zeros(X.shape)
X_test_cn = nn.zeros(X_test.shape)
for i in xrange(7):
X_cn[10000 * i:10000 * (i + 1), :, :, :] = nn.SpatialContrast(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
for i in xrange(2):
X_test_cn[10000 * i:10000 * (i + 1), :, :, :] = nn.SpatialContrast(
X_test[10000 * i:10000 * (i + 1), :, :, :], filters)
if want_dense:
X_cn = X_cn.reshape(70000, 3072)
X_test_cn = X_test_cn.reshape(20000, 3072)
return X_cn, T, X_test_cn, T_test, T_train_labels, T_labels
def load_contrast_extra(n=21,k=.01):
X,T,X_test,T_test,T_train_labels,T_labels = SVHN.load_extra(want_mean=False,want_dense=True)
print "Contrast Normalization."
X_mean=X.mean(axis=1)
X_test_mean=X_test.mean(axis=1)
X_std = X.std(1)
X_test_std = X_test.std(1)
X = (X-X_mean[:, np.newaxis])/X_std[:, np.newaxis]
X_test = (X_test-X_test_mean[:, np.newaxis])/X_test_std[:, np.newaxis]
X = X.reshape(600000,3,32,32)
X_test = X_test.reshape(20000,3,32,32)
filters = nn.gaussian2D(n,k)
X_cn = np.zeros(X.shape)
X_test_cn = np.zeros(X_test.shape)
for i in xrange(60):
if i%10==0: print i,
X_cn[i*10000:(i+1)*10000,:,:,:] = nn.SpatialContrast(X[i*10000:(i+1)*10000,:,:,:], filters)
for i in xrange(2):
X_test_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X_test[10000*i:10000*(i+1),:,:,:], filters)
h5f = h5py.File('/media/nas/PSI-Share-no-backup/Ali/Dataset/SVHN/extra_contrast21.01_new.h5', 'w')
print "file created."
h5f.create_dataset('X', data=X_cn)
h5f.create_dataset('T', data=T)
h5f.create_dataset('T_train_labels', data=T_train_labels)
print "train done."
h5f.create_dataset('X_test', data=X_test_cn)
h5f.create_dataset('T_test', data=T_test)
h5f.create_dataset('T_labels', data=T_labels)
return X_cn,T,X_test_cn,T_test,T_train_labels,T_labels
def load_contrast(n=21,k=.01,want_dense=False):
X,T,X_test,T_test,T_train_labels,T_labels = SVHN.load(want_mean=False,want_dense=True)
# for i in xrange(3):
# mean = X[:,i,:,:].mean()
# std = X[:,i,:,:].std()
# X[:,i,:,:] = (X[:,i,:,:] - mean)/std
# X_test[:,i,:,:] = (X_test[:,i,:,:] - mean)/std
X_mean=X.mean(axis=1)
X_test_mean=X_test.mean(axis=1)
X_std = X.std(1)
X_test_std = X_test.std(1)
X = (X-X_mean[:, np.newaxis])/X_std[:, np.newaxis]
X_test = (X_test-X_test_mean[:, np.newaxis])/X_test_std[:, np.newaxis]
X = X.reshape(70000,3,32,32)
X_test = X_test.reshape(20000,3,32,32)
filters = nn.gaussian2D(n,k)
X_cn = nn.zeros(X.shape)
X_test_cn = nn.zeros(X_test.shape)
for i in xrange(7):
X_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X[10000*i:10000*(i+1),:,:,:], filters)
for i in xrange(2):
X_test_cn[10000*i:10000*(i+1),:,:,:] = nn.SpatialContrast(X_test[10000*i:10000*(i+1),:,:,:], filters)
if want_dense:
X_cn = X_cn.reshape(70000,3072)
X_test_cn = X_test_cn.reshape(20000,3072)
return X_cn,T,X_test_cn,T_test,T_train_labels,T_labels
def load_contrast(n=13,
k=.01,
want_dense=False,
filter="gaussian",
contrast="mean"):
X, T, X_test, T_test, T_train_labels, T_labels = CIFAR10.load(
want_mean=False, want_dense=True)
# for i in xrange(3):
# mean = X[:,i,:,:].mean()
# std = X[:,i,:,:].std()
# X[:,i,:,:] = (X[:,i,:,:] - mean)/std
# X_test[:,i,:,:] = (X_test[:,i,:,:] - mean)/std
X_mean = X.mean(axis=1)
X_test_mean = X_test.mean(axis=1)
X_std = X.std(1)
X_test_std = X_test.std(1)
X = (X - X_mean[:, np.newaxis]) / X_std[:, np.newaxis]
X_test = (X_test - X_test_mean[:, np.newaxis]) / X_test_std[:,
np.newaxis]
X = X.reshape(50000, 3, 32, 32)
X_test = X_test.reshape(10000, 3, 32, 32)
if filter == "box":
filters = nn.ones((n, n))
elif filter == "gaussian":
filters = nn.gaussian2D(n, k)
else:
raise Exception("unhandled case")
X_cn = nn.zeros(X.shape)
X_test_cn = nn.zeros(X_test.shape)
for i in xrange(5):
if contrast == "mean":
X_cn[10000 * i:10000 *
(i + 1), :, :, :] = nn.SpatialContrastMean(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
elif contrast == "mean-var":
X_cn[10000 * i:10000 * (i + 1), :, :, :] = nn.SpatialContrast(
X[10000 * i:10000 * (i + 1), :, :, :], filters)
else:
raise Exception("unhandled case")
for i in xrange(1):
if contrast == "mean":
X_test_cn[10000 * i:10000 *
(i + 1), :, :, :] = nn.SpatialContrastMean(
X_test[10000 * i:10000 * (i + 1), :, :, :],
filters)
elif contrast == "mean-var":
X_test_cn[10000 * i:10000 *
(i + 1), :, :, :] = nn.SpatialContrast(
X_test[10000 * i:10000 * (i + 1), :, :, :],
filters)
else:
raise Exception("unhandled case")
if want_dense:
X_cn = X_cn.reshape(50000, 3072)
X_test_cn = X_test_cn.reshape(10000, 3072)
return X_cn, T, X_test_cn, T_test, T_train_labels, T_labels
