def test_FoveatedNORB():
"""
This function tests the FoveatedNORB class. In addition to the shape and
datatype of X and y member of the returned object, it also checks the
scale of data while passing different parameters to the constructor.
"""
skip_if_no_data()
data = FoveatedNORB('train')
datamin = data.X.min()
datamax = data.X.max()
assert data.X.shape == (24300, 8976)
assert data.X.dtype == 'float32'
assert data.y.shape == (24300, )
assert data.y_labels == 5
assert data.get_topological_view().shape == (24300, 96, 96, 2)
data = FoveatedNORB('train', center=True)
assert data.X.min() == datamin - 127.5
assert data.X.max() == datamax - 127.5
data = FoveatedNORB('train', center=True, scale=True)
assert numpy.all(data.X <= 1.)
assert numpy.all(data.X >= -1.)
data = FoveatedNORB('train', scale=True)
assert numpy.all(data.X <= 1.)
assert numpy.all(data.X >= 0.)
data = FoveatedNORB('test')
assert data.X.shape == (24300, 8976)
assert data.X.dtype == 'float32'
assert data.y.shape == (24300, )
assert data.y_labels == 5
assert data.get_topological_view().shape == (24300, 96, 96, 2)
A script for sequentially stepping through FoveatedNORB, viewing each image
and its label.
"""
import numpy as np
from pylearn2.datasets.norb_small import FoveatedNORB
from pylearn2.gui.patch_viewer import PatchViewer
from pylearn2.utils import get_choice
print('Use test set?')
choices = {'y': 'test', 'n': 'train'}
which_set = choices[get_choice(choices)]
dataset = FoveatedNORB(which_set=which_set, center=True)
topo = dataset.get_topological_view()
b, r, c, ch = topo.shape
assert ch == 2
pv = PatchViewer((1, 2), (r, c), is_color=False)
i = 0
while True:
patch = topo[i, :, :, :]
patch = patch / np.abs(patch).max()
pv.add_patch(patch[:,:,1], rescale=False)
pv.add_patch(patch[:,:,0], rescale=False)
"""
import numpy as np
from six.moves import input
from pylearn2.datasets.norb_small import FoveatedNORB
from pylearn2.gui.patch_viewer import PatchViewer
from pylearn2.utils import get_choice
print('Use test set?')
choices = {'y': 'test', 'n': 'train'}
which_set = choices[get_choice(choices)]
dataset = FoveatedNORB(which_set=which_set, center=True)
topo = dataset.get_topological_view()
b, r, c, ch = topo.shape
assert ch == 2
pv = PatchViewer((1, 2), (r, c), is_color=False)
i = 0
while True:
patch = topo[i, :, :, :]
patch = patch / np.abs(patch).max()
pv.add_patch(patch[:, :, 1], rescale=False)
pv.add_patch(patch[:, :, 0], rescale=False)
