def protocol(self, algo):
rng = np.random.RandomState(self.seed)
n_prototypes = self.n_prototypes
coords = [prototype_coords(rng) for i in range(n_prototypes)]
dset = functools.partial(
self.distortion_set,
coords=coords,
rng=rng,
)
train_images, train_labels = dset(N=self.n_train_per_prototype,
level=self.train_level)
test_5_images, test_5_labels = dset(N=self.n_test_5_per_prototype,
level='5')
test_7_images, test_7_labels = dset(N=self.n_test_7_per_prototype,
level='7.7')
test_proto_images, test_proto_labels = dset(N=1, level='0')
test_proto_labels = range(self.n_prototypes)
# XXX: Careful not to actually expect the model to get these right.
test_random_images = [
render_coords_uint8_channels(prototype_coords(rng))
for c in range(n_prototypes)
]
test_random_labels = range(self.n_prototypes)
model = algo.best_model(
train=self.task('train', train_images, train_labels))
loss_5 = algo.loss(model,
self.task('test_5', test_5_images, test_5_labels))
loss_7 = algo.loss(model,
self.task('test_7', test_7_images, test_7_labels))
loss_train = algo.loss(
model, self.task('test_train', train_images, train_labels))
loss_proto = algo.loss(
model, self.task('test_proto', test_proto_images,
test_proto_labels))
loss_random = algo.loss(
model,
self.task('test_random', test_random_images, test_random_labels))
return algo
def protocol(self, algo):
rng = np.random.RandomState(self.seed)
n_prototypes = self.n_prototypes
coords = [prototype_coords(rng) for i in range(n_prototypes)]
dset = functools.partial(self.distortion_set,
coords=coords,
rng=rng,
)
train_images, train_labels = dset(
N=self.n_train_per_prototype,
level=self.train_level)
test_5_images, test_5_labels = dset(
N=self.n_test_5_per_prototype,
level='5')
test_7_images, test_7_labels = dset(
N=self.n_test_7_per_prototype,
level='7.7')
test_proto_images, test_proto_labels = dset(N=1, level='0')
test_proto_labels = range(self.n_prototypes)
# XXX: Careful not to actually expect the model to get these right.
test_random_images = [
render_coords_uint8_channels(prototype_coords(rng))
for c in range(n_prototypes)]
test_random_labels = range(self.n_prototypes)
model = algo.best_model(
train=self.task('train', train_images, train_labels))
loss_5 = algo.loss(model,
self.task('test_5', test_5_images, test_5_labels))
loss_7 = algo.loss(model,
self.task('test_7', test_7_images, test_7_labels))
loss_train = algo.loss(model,
self.task('test_train', train_images, train_labels))
loss_proto = algo.loss(model,
self.task('test_proto', test_proto_images, test_proto_labels))
loss_random = algo.loss(model,
self.task('test_random', test_random_images, test_random_labels))
return algo
def main_show():
import matplotlib.pyplot as plt
rng = np.random.RandomState(1)
coords = prototype_coords(rng)
img = render_coords(coords)
img3 = render_coords(distort(coords, '3', rng))
img6 = render_coords(distort(coords, '6', rng))
plt.imshow(np.asarray([img, img3, img6]).T,
cmap='gray',
interpolation='nearest')
plt.show()
def main_show():
import matplotlib.pyplot as plt
rng = np.random.RandomState(1)
coords = prototype_coords(rng)
img = render_coords(coords)
img3 = render_coords(distort(coords, '3', rng))
img6 = render_coords(distort(coords, '6', rng))
plt.imshow(np.asarray([img, img3, img6]).T,
cmap='gray',
interpolation='nearest')
plt.show()
