Python torch.nn.HingeEmbeddingLoss() Examples
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code examples of torch.nn.HingeEmbeddingLoss().
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Example #1
Source File: __init__.py From Deep-Expander-Networks with GNU General Public License v3.0 | 6 votes |
def setup(model, opt): if opt.criterion == "l1": criterion = nn.L1Loss().cuda() elif opt.criterion == "mse": criterion = nn.MSELoss().cuda() elif opt.criterion == "crossentropy": criterion = nn.CrossEntropyLoss().cuda() elif opt.criterion == "hingeEmbedding": criterion = nn.HingeEmbeddingLoss().cuda() elif opt.criterion == "tripletmargin": criterion = nn.TripletMarginLoss(margin = opt.margin, swap = opt.anchorswap).cuda() parameters = filter(lambda p: p.requires_grad, model.parameters()) if opt.optimType == 'sgd': optimizer = optim.SGD(parameters, lr = opt.lr, momentum = opt.momentum, nesterov = opt.nesterov, weight_decay = opt.weightDecay) elif opt.optimType == 'adam': optimizer = optim.Adam(parameters, lr = opt.maxlr, weight_decay = opt.weightDecay) if opt.weight_init: utils.weights_init(model, opt) return model, criterion, optimizer
Example #2
Source File: criterion.py From L2C with MIT License | 5 votes |
def __init__(self, margin=2.0): super(KCL,self).__init__() self.kld = KLDiv() self.hingeloss = nn.HingeEmbeddingLoss(margin)