def load_usps(invert=False, zero_centre=False, val=False, scale28=False):
#
#
# Load USPS
#
#
print('Loading USPS...')
if val:
d_usps = usps.USPS()
else:
d_usps = usps.USPS(n_val=None)
d_usps.train_X = d_usps.train_X[:]
d_usps.val_X = d_usps.val_X[:]
d_usps.test_X = d_usps.test_X[:]
d_usps.train_y = d_usps.train_y[:]
d_usps.val_y = d_usps.val_y[:]
d_usps.test_y = d_usps.test_y[:]
if scale28:
def _resize_tensor(X):
X_prime = np.zeros((X.shape[0], 1, 28, 28), dtype=np.float32)
for i in range(X.shape[0]):
X_prime[i, 0, :, :] = resize(X[i, 0, :, :], (28, 28),
mode='constant')
return X_prime
# Scale 16x16 to 28x28
d_usps.train_X = _resize_tensor(d_usps.train_X)
d_usps.val_X = _resize_tensor(d_usps.val_X)
d_usps.test_X = _resize_tensor(d_usps.test_X)
if invert:
# Invert
d_usps.train_X = 1.0 - d_usps.train_X
d_usps.val_X = 1.0 - d_usps.val_X
d_usps.test_X = 1.0 - d_usps.test_X
if zero_centre:
d_usps.train_X = d_usps.train_X * 2.0 - 1.0
d_usps.test_X = d_usps.test_X * 2.0 - 1.0
print(
'USPS: train: X.shape={}, y.shape={}, val: X.shape={}, y.shape={}, test: X.shape={}, y.shape={}'
.format(d_usps.train_X.shape, d_usps.train_y.shape, d_usps.val_X.shape,
d_usps.val_y.shape, d_usps.test_X.shape, d_usps.test_y.shape))
print('USPS: train: X.min={}, X.max={}'.format(d_usps.train_X.min(),
d_usps.train_X.max()))
d_usps.n_classes = 10
return d_usps
def test_val_0():
from batchup.datasets import usps
ds = usps.USPS(n_val=0)
assert ds.train_X.shape == (7291, 1, 16, 16)
assert ds.train_X.dtype == np.float32
assert ds.train_y.shape == (7291, )
assert ds.train_y.dtype == np.int32
assert ds.val_X.shape == (0, 1, 16, 16)
assert ds.val_X.dtype == np.float32
assert ds.val_y.shape == (0, )
assert ds.val_y.dtype == np.int32
assert ds.test_X.shape == (2007, 1, 16, 16)
assert ds.test_X.dtype == np.float32
assert ds.test_y.shape == (2007, )
assert ds.test_y.dtype == np.int32