eltomali

weiliu620

def dice_loss(pred, target):
    """This definition generalize to real valued pred and target vector.
This should be differentiable.

    pred: tensor with first dimension as batch
    target: tensor with first dimension as batch

    """

    smooth = 1.

harveyslash

class ContrastiveLoss(torch.nn.Module):
    """
    Contrastive loss function.
    Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    """

    def __init__(self, margin=2.0):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin

xmfbit

import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
## load mnist dataset
use_cuda = torch.cuda.is_available()

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state