sbarratt

def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data

dsleo

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from conformal.conformal_predictor import InductiveConformalPredictor

data = load_digits()
X, y = data.data, data.target
alpha = 0.05
seed = 41

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,