本文简要介绍python语言中 torch.nn.functional.binary_cross_entropy_with_logits 的用法。 用法: torch.nn.functional.binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None) 参数: input-任意形状的张量作为非标准化分数(通常称为 ...
F.binary_cross_entropy_with_logits函数和 F.binary_cross_entropy函数的reduction 参数都默认是‘mean’模式,直接使用默认值的话,结果是320个样本点的二元交叉熵的平均值, 若要计算8个图像样本的二元交叉熵的平均值,可以设置reduction=‘sum’ ,这样能得到320个样本点的二元交叉熵的和,然后除以batch_size 就能得到...
>>> loss = F.binary_cross_entropy_with_logits(input, target) >>> loss.backward() """ if has_torch_function_variadic(input, target, weight, pos_weight): return handle_torch_function( binary_cross_entropy_with_logits, (input, target, weight, pos_weight), input, target, weight=weight, ...
# 需要導入模塊: from torch.nn import functional [as 別名]# 或者: from torch.nn.functional importbinary_cross_entropy_with_logits[as 別名]def_weighted_cross_entropy_loss(preds, edges):""" Calculate sum of weighted cross entropy loss. """# Reference:# hed/src/caffe/layers/sigmoid_cro...
posweight = torch.ones([69]) critrion = torch.nn.BCEWithLogitsLoss(pos_weight=posweight) critrion(out_var, tar_var) Output: In the following output, we can see that the PyTorch cross-entropy pos_weight value in which all the weights are equal to 1 is printed on the screen. ...
F.binary_cross_entropy_with_logits(x, y) out: tensor(0.7739) __EOF__ 本文来自博客园,作者:SXQ-BLOG,转载请注明原文链接:https://www.cnblogs.com/sxq-blog/p/17068865.html 分类:DeepLearning SXQ-BLOG 粉丝-9关注 -2 +加关注 0 0 «关于目标检测中bounding box编码和解码时weight参数的理解 ...
🐛 Bug I'm moving to pytorch 1.0.1 recently. But I got the error below when I use 'binary_cross_entropy_with_logits' RuntimeError: the derivative for 'weight' is not implemented my code is work well with pytorch 0.4.1 I'm used CUDA 9.0.17...
问题已解决:我认为这确实是paddlepaddle的F.binary_cross_entropy_with_logits函数实现的一个潜在bug——函数本身可以使用,只不过它本应该支持的一个功能,实际上却不支持。 解决这个问题的方法很简单:对于两类分类问题,网络最后全连接层的输出如果是2个数,则可以用F.cross_entropy函数来计算损失。但是其实这时候可以让...
weight = torch.zeros_like(mask) weight[edges > 0.5] = num_neg / (num_pos + num_neg) weight[edges <= 0.5] = num_pos / (num_pos + num_neg) # Calculate loss. losses = F.binary_cross_entropy_with_logits( preds.float(), edges.float(), weight=weight, reduction='none') loss =...
的确binary_cross_entropy_with_logits不需要sigmoid函数了。 事实上,官方是推荐使用函数带有with_logits的,解释是 This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the ope...