weight_b=(1-alpha)*tf.pow(y_pred,gamma)*(1-y_true)loss=tf.log1p(tf.exp(-logits))*(weight_a+weight_b)+logits*weight_breturntf.reduce_mean(loss)
self.alpha=alpha defforward(self,inputs,targets):ce_loss=nn.CrossEntropyLoss(reduction='none')(inputs,targets)pt=torch.exp(-ce_loss)focal_loss=self.alpha*(1-pt)**self.gamma*ce_lossreturnfocal_loss.mean()# 设置训练参数 num_epochs=10batch_size=16learning_rate=0.001# 数据预处理 transform=...
y_pred = tf.clip_by_value(softmax_prob, epsilon,1. - epsilon) # 得到交叉熵,其中的“-”符号可以放在好几个地方,都是等效的,最后取mean是为了兼容batch训练的情况。 cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_true*tf.log(y_pred))) return cross_entropy 所以需要做的就是往上边这段代...
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(1,target)logpt=logpt.view(-1,1)pt=torch.exp(logpt)alpha=self.alpha.gather(0,target.view(-1))gamma=self.gammaifnot self.gamma.device==input.device:gamma=torch.tensor(self.gamma,device=input.device)loss=-1*alpha*torch.pow((1-pt),gamma)*logptifself.size_average:loss=loss.mean()...
reduction:传入的是一个字符串,有三种形式可以选择,分别是mean/sum/none,默认是mean。mean和sum如字面意思所示,代表损失值取平均,损失值求和的形式。none是计算每个位置对应的损失值,返回和label对应的形状。 更多参数解释如下图所示: 使用方法 CrossEntropyLoss传入的值为两个,分别是input和target。输出只有一个Output...
(yc2 - yc1) + self.eps rho2_w = (w - wg) * (w - wg) rho2_h = (h - hg) * (h - hg) eiou_term = (rho2_w / c2_w) + (rho2_h / c2_h) #Focal-EIOU eiou = paddle.mean((1 - iouk + diou_term + eiou_term) * iou_weight) focal_eiou = iouk**0.5 * eiou ...
reduction='mean', avg_factor=None): pred_sigmoid = pred.sigmoid() target = target.type_as(pred) pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target) focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * pt.pow(gamma) ...
Mean—The mean (average value) of the cells in the neighborhood will be calculated. Majority—The majority (value that occurs most often) of the cells in the neighborhood will be identified. Maximum—The maximum (largest value) of the cells in the neighborhood will be identified. Median—The...
if self.sampling =="mean": return loss.mean() elif self.sampling =="sum": return loss.sum() elif self.sampling == None: return loss 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27