When I use binary cross-entropy I get ~80% accuracy, with categorical cross-entropy I get ~50% accuracy. I don't understand why this is. It's a multiclass problem, doesn't that mean that I have to use categorical cross-entropy and that the results with binary c...
On the same subject, I was reading a paper that said: To deal with the unbalanced negative and positive data, we dilate each keypoint by 10 pixels and use weighted cross-entropy loss. The weight for each keypoint is set to 100 while for non-keypoint pixels it is set ...
# 需要导入模块: from torch.nn import functional [as 别名]# 或者: from torch.nn.functional importbinary_cross_entropy[as 别名]def_add_losses(self, sigma_rpn=3.0):# classification lossimage_prob = self._predictions["image_prob"]# assert ((image_prob.data>=0).sum()+(image_prob.data<=1...
I'm reading the YOLOv3 paper, and it says that YOLOv3 uses the binary crossentropy loss; however, when I looked in the code in this repository, I noticed you use squared error. I'm curious as to how the two losses compare.Owner qqwweee commented May 21, 2018 You are right. But...
test_loss =binary_crossentropy(test_prediction, target_var).mean()returntest_prediction, prediction, loss, params 开发者ID:tfjgeorge,项目名称:kaggle-heart,代码行数:59,代码来源:full_model.py 示例3: test_binary_crossentropy ▲点赞 4▼
This analysis refers to the use of crossover Artificial Neural Network (ANN) and Binary Particle Swarm Optimization (BPSO) with Binary Cross-Entropy (BCE) loss for the fitness function. The conclusion of proposed paper is to provide the significance and opportunity of using Binary Cross-Entropy ...
In this paper, we propose regularized lightweight deep convolutional neural network models, capable of effectively operating in real-time on-drone for high-resolution video input. Furthermore, we study the impact of hinge loss against the cross entropy loss on the classification performance, mainly ...
In the remainder of this paper I shall describe the Target Offset, Sigmoid Prime Offset, and Cross Entropy approaches in more detail, and present the results from a series of evolutionary simulations that optimize each case for a representative pair of binary mappings. We end with a clear ...
different source codes to be dissimilar. Since the final output of our model has only two cases, similar and dissimilar, we choose cross-entropy as our loss function. For each pair of inputs, we predict similarity with probabilitypand dissimilarity with probability1−p, so the loss function ...
The best feature combination is the one that yields the cross-validated loss of the classifier performance and minimum number of selected features. 4.3. Fitness function The major purpose of the proposed algorithm is to increase the effectiveness of feature selection methods by the optimal reduct. ...