def binarized2_bn_label():
image = bn_image()
image = binarize(image, (0, 1), included_in='lower')
mean = np.mean(image, axis=(0, 1))
assert mean.shape == (10,)
label = np.argmax(mean)
return label
def binarized_bn_label(bn_image):
image = bn_image
image = binarize(image, (0, 1))
mean = np.mean(image, axis=(0, 1))
assert mean.shape == (10,)
label = np.argmax(mean)
return label
def preprocessing(x):
x = binarize(x, (0, 1), included_in='lower')
def backward(x):
return x
return x, backward
def preprocessing(x):
x = binarize(x, (0, 1))
def backward(x):
return x
return x, backward
def binarized2_bn_labels(bn_images):
images = bn_images
images = binarize(images, (0, 1), included_in="lower")
means = np.mean(images, axis=(1, 2))
assert means.shape == (len(images), 10)
labels = np.argmax(means, -1)
return labels
def binarized_bn_labels(bn_images):
images = bn_images
images = binarize(images, (1, 2))
means = np.mean(images, axis=(1, 2))
assert means.shape == (len(bn_images), 10)
label = np.argmax(means, axis=-1)
return label
def test_binarize():
x = np.array([0.1, 0.5, 0.7, 0.4])
x1 = binarize(x, (-2, 2), 0.5)
assert np.all(abs(x1) == 2)
with pytest.raises(ValueError):
binarize(x, (-2, 2), 0.5, included_in='blabla')
