def test():
X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_norb(size=32,mode="serial",want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_whiten(size=32)
print X.dtype,X.max()
dp = nn.dp_ram(X=X,T=T,X_test=X_test,T_test=T_test,T_train_labels=None,T_labels=T_labels,data_batch=25)
while 1:
x,t,id = dp.train()
print x.shape
nn.show_images(x,(5,5))
print t
nn.show()
# np.savez("./dataset/norb_binocular_small_normalized", X=X,T=T,X_test=X_test,T_test=T_test,T_train_labels=T_train_labels,T_labels=T_labels)
# f=np.load(work_address+'./dataset/norb_single_small.npz')
# X=f['X'];T=f['T'];X_test=f['X_test'];T_test=f['T_test'];T_train_labels=f['T_train_labels'];T_labels=f['T_labels']
# f=np.load(work_address+'./dataset/norb_binocular_small.npz')
# X=f['X'];T=f['T'];X_test=f['X_test'];T_test=f['T_test'];T_train_labels=f['T_train_labels'];T_labels=f['T_labels']
# X_std=X.std(axis=0)
# X_mean=X.mean(axis=0)
# X_test_std=X_test.std(axis=0)
# X_test_mean=X_test.mean(axis=0)
# X=(X-X_mean)/X_std
# X_test=(X_test-X_test_mean)/X_test_std
# np.savez("./dataset/norb_binocular_small_normalized", X=X,T=T,X_test=X_test,T_test=T_test,T_train_labels=T_train_labels,T_labels=T_labels)
# f=np.load(work_address+'./dataset/norb_single_small.npz')
# X=f['X'];T=f['T'];X_test=f['X_test'];T_test=f['T_test'];T_train_labels=f['T_train_labels'];T_labels=f['T_labels']
# f=np.load(work_address+'./dataset/norb_binocular_small.npz')
# X=f['X'];T=f['T'];X_test=f['X_test'];T_test=f['T_test'];T_train_labels=f['T_train_labels'];T_labels=f['T_labels']
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!numpy object dtype correct it
def test():
# X = TorontoFace.load(want_mean = True)
X = TorontoFace.load_whiten(bias=.1)
# X = TorontoFace.load_contrast(n=21,k=.01)
# X = TorontoFace.load_contrast(n=3,k=.01,filter="box",contrast="mean")
dp = nn.dp_ram(X=X, T=X, data_batch=9)
while 1:
X, T, _ = dp.train()
nn.show_images(X, (3, 3))
nn.show()
def test():
# X = TorontoFace.load(want_mean = True)
X = TorontoFace.load_whiten(bias=.1)
# X = TorontoFace.load_contrast(n=21,k=.01)
# X = TorontoFace.load_contrast(n=3,k=.01,filter="box",contrast="mean")
dp = nn.dp_ram(X=X,T=X,data_batch=9)
while 1:
X,T,_ = dp.train()
nn.show_images(X,(3,3))
nn.show()
def test():
# X = TorontoFace.load(want_mean = True)
# X = TorontoFace.load_whiten(p=1)
# X = TorontoFace.load_contrast(n=21,k=.01)
X,T,X_test,T_test,T_train_labels,T_labels = MNIST.semi(N=100,want_dense = False)
dp = nn.dp_ram(X,T,data_batch=100)
while 1:
x,t,id = dp.train()
nn.show_images(x,(10,10))
print t
nn.show()
def test():
# X = TorontoFace.load(want_mean = True)
# X = TorontoFace.load_whiten(p=1)
# X = TorontoFace.load_contrast(n=21,k=.01)
X, T, X_test, T_test, T_train_labels, T_labels = MNIST.semi(
N=100, want_dense=False)
dp = nn.dp_ram(X, T, data_batch=100)
while 1:
x, t, id = dp.train()
nn.show_images(x, (10, 10))
print t
nn.show()
def test():
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load(size=32,want_mean=True)
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_whiten(size=32,bias=.1)
X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_contrast()
print X.dtype,X.max()
dp = nn.dp_ram(X=X,T=T,X_test=X_test,T_test=T_test,T_train_labels=None,T_labels=T_labels,data_batch=25)
while 1:
x,t,id = dp.train()
print x.shape
nn.show_images(x,(5,5))
print t
nn.show()
def test(want_dense=False):
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_contrast_extra(n=21,k=.01,want_dense=False)
# dataset.load_svhn_contrast_extra_make()
# X,T,X_test,T_test,T_train_labels,T_labels = SVHN.load_contrast(n=13,k=.01,want_dense=False)
X,T,X_test,T_test,_,_,lst = SVHN.semi(N=20)
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_contrast(n=13,want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_pylearn2(want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_torch(want_dense=False)
# n = 0
# print X.shape
dp = nn.dp_ram(X,T,data_batch=20)
x,t,_ = dp.train()
nn.show_images(x,(2,10))
nn.show()
def test(want_dense=False):
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_contrast_extra(n=21,k=.01,want_dense=False)
# dataset.load_svhn_contrast_extra_make()
# X,T,X_test,T_test,T_train_labels,T_labels = SVHN.load_contrast(n=13,k=.01,want_dense=False)
X, T, X_test, T_test, _, _, lst = SVHN.semi(N=20)
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_contrast(n=13,want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_pylearn2(want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = dataset.load_svhn_torch(want_dense=False)
# n = 0
# print X.shape
dp = nn.dp_ram(X, T, data_batch=20)
x, t, _ = dp.train()
nn.show_images(x, (2, 10))
nn.show()
def test():
# X,T,X_test,T_test,T_train_labels,T_labels = CIFAR10.load(want_mean=True,want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = CIFAR10.load_whiten(bias=.1)
X,T,X_test,T_test,T_train_labels,T_labels = CIFAR10.load_contrast(n=13,k=.01)
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_whiten(size=32,bias=.1)
# X = CIFAR10.load_cifar10_adam_patch(num_patch=100000,size=11,backend="numpy")
# print X.dtype,X.max()
dp = nn.dp_ram(X=X,X_test=X,data_batch=25)
while 1:
x,t,id = dp.train()
print x.shape
nn.show_images(x,(5,5))
print t
nn.show()
def test():
# X,T,X_test,T_test,T_train_labels,T_labels = CIFAR10.load(want_mean=True,want_dense=False)
# X,T,X_test,T_test,T_train_labels,T_labels = CIFAR10.load_whiten(bias=.1)
X, T, X_test, T_test, T_train_labels, T_labels = CIFAR10.load_contrast(
n=13, k=.01)
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_whiten(size=32,bias=.1)
# X = CIFAR10.load_cifar10_adam_patch(num_patch=100000,size=11,backend="numpy")
# print X.dtype,X.max()
dp = nn.dp_ram(X=X, X_test=X, data_batch=25)
while 1:
x, t, id = dp.train()
print x.shape
nn.show_images(x, (5, 5))
print t
nn.show()
def test():
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load(size=32,want_mean=True)
# X,T,X_test,T_test,T_train_labels,T_labels = NORB.load_whiten(size=32,bias=.1)
X, T, X_test, T_test, T_train_labels, T_labels = NORB.load_contrast()
print X.dtype, X.max()
dp = nn.dp_ram(X=X,
T=T,
X_test=X_test,
T_test=T_test,
T_train_labels=None,
T_labels=T_labels,
data_batch=25)
while 1:
x, t, id = dp.train()
print x.shape
nn.show_images(x, (5, 5))
print t
nn.show()