jerinphilip / dirichlet-prior-networks Goto Github PK

Hi,

I'm trying to replicate the synthetic data experiment in the paper Predictive Uncertainty Estimation via Prior Networks found here- https://papers.nips.cc/paper/7936-predictive-uncertainty-estimation-via-prior-networks.pdf using your code with a few modifications. However, the precision (\alpha_0) of the output is not able to differentiate between in-distribution and out of distribution data as the values are always low i.e. <2. Could you please give me a list of parameters and initial conditions you used? Here is the list of parameters I used. Is there something wrong I'm doing? Also I am attaching the code I am using right now.
DPNmodifiedFiles.zip

parser.add_argument('--alpha', type=float, default=1e2)
parser.add_argument('--epochs', type=int, default=30)
parser.add_argument('--log_interval', type=int, default=20)
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--lr', type=float, default=1e-2)
parser.add_argument('--weight_decay', type=float, default=1)
parser.add_argument('--work_dir', type=str, default="C:\Users\dirichlet-prior-networks\dpn")
parser.add_argument('--model', type=str, default='mlp')
parser.add_argument('--dataset', type=str, default='synthetic')
parser.add_argument('--radius', type=float, default=4.0)
parser.add_argument('--sigma', type=float, default=1.0)
parser.add_argument('--shuffle', action='store_false')
parser.add_argument('--num_train_samples', type=int, default=int(500))
parser.add_argument('--num_test_samples', type=int, default=int(1))
parser.add_argument('--log', action='store_false')
parser.add_argument('--ind-loss', type=str, default= "{'dirichlet_kldiv': 1.0}")
parser.add_argument('--ood-loss', type=str, default='{"dirichlet_kldiv":1}')
parser.add_argument('--ind-fraction', type=float, default=0.5)
parser.add_argument('--rejection-threshold', type=float, default=1e-4)

Hi,
I'm very keen on using your work in my thesis. can you explain to me how can calculate prior distribution by Dirichlet prior