outputs=output)model.compile(optimizer='adam', loss='binary_crossentropy',
从初学开始就一直难以分清二分类和多分类在loss上的区别,虽然明白二分类其实是多分类的一个特殊情况,但在看Focal loss文章中的公式的时候还是不免头晕,之前不愿处理的细节如今不得不仔细从很基础的地方开始解读。 多分类Cross Entropy: H(y,y′)=−∑y′ilogyiH(y,y′)=−∑yi′logyi ...
最近在做一个multilabel classification(多标签分类)的项目,需要一些特定的metrics去评判一个multilabel classifier的优劣。这里对用到的三个metrics做一个总结。 首先明确一下多标签(multilabel)分类和多类别(multiclass)分类的
view(num, -1) #loss_c: torch.Size([32, 8732]) 以下解释代码段“loss_c = log_sum_exp(batch_conf)- batch_conf.gather(1, conf_t.view(-1,1))” 其中, log_sum_exp(x): log\left(\sum_{i=0}^{i=num\_class}{e^{x_i-x_{max}}} \right) +x_{max} : 为何使用log\_sum\_...
Multi loss: 其中\sigma_{label}^2, \sigma_{instance}^2,\sigma_{disp}^2 都是可学习的 src/statistic.py中 def set_sigmas_and_wights(self): if FLAGS.use_multi_loss: for i, sigma_sq_tn in zip(range(len(self.multi_loss_class._sigmas_sq)), self.multi_loss_class._sigmas_sq): sig...
Analysis of loss functions for multi-class tissue segmentation Categorical cross-entropy In the context of image segmentation, the cross-entropy loss, also known as softmax loss, has been largely used both in binary and multi-class problems, referred to as categorical cross-entropy (CC) loss in...
Finally, we applied two loss functions in the prediction tasks. The first one was the bag loss function of standard binary or multiclass cross-entropy with the inverted class weights informed by the number of tiles in each class. The inverted class weights enabled machine learning models to acc...
""" A subclass that uses the Softmax + Cross-entropy loss function """ def loss(self, X_batch, y_batch, reg): return softmax_loss_vectorized(self.W, X_batch, y_batch, reg) 可视化: # A useful debugging strategy is to plot the loss as a function of ...
objective='multiclass', # 'multilogloss num_class=num_class, # 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker. n_estimators=n_estimators, learning_rate=lr, num_leaves=2**max_depth-1, # (0, 2^max_depth - 1] ...
❔Question I see from the code in ComputeLoss that BCE (binary cross entropy) is used to calculate the cls and obj loss. For multi-class application, it seems that loss for multiple classes are broken down into multiple single class BCE. ...