sample_rate = 16000 loss_func = NegSTOILoss(sample_rate=sample_rate) est_speec = torch.randn(2, sample_rate) clean_speech = torch.randn(2, sample_rate) # Compute loss and backward (then step etc...) loss_batch = loss_func(est_speech, clen_speech) loss_batch.mean().backward() 代...
选择’sum’时即为L1 loss; loss_func = torch.nn.L1Loss() input = torch.autograd.Variable(torch.randn(3,4)) target = torch.autograd.Variable(torch.randn(3,4)) loss = loss_func(input, target) print(input); print(target); print(loss) print(input.size(), target.size(), loss.size()...
Debug程序到: loss = loss_functoin(outputs, labels) 1. 因为loss_functoin()是一个Module,所以输入outputs和labels,其实就是执行一个forward(),一个模型模块必须有一个forward()函数,通过步进调试进入查看具体是怎么实现的: def __call__(self, *input, **kwargs): for hook in self._forward_pre_hook...
选择’sum’时即为L1 loss; loss_func = torch.nn.L1Loss() input = torch.autograd.Variable(torch.randn(3,4)) target = torch.autograd.Variable(torch.randn(3,4)) loss = loss_func(input, target) print(input); print(target); print(loss) print(input.size(), target.size(), loss.size()...
总结:cost function是各个样本的loss funcion的平均 === 那么caffe下的loss又是怎么一回事?=== caffe通常是视觉任务用的深度学习框架,处理的原始数据是图片。每次处理一张图片,这本身可以run,算是OK,但不够好。 每次处理多张图片,称为一个batch(批次),比如训练图片一共有4000张,每个batch处理50张。 按照batch...
Stochastic Depth(Resnet- shortcut) 三、自定义Loss Funcion + L2 除了Regulation方法,那就自定义带有L1,L2的loss function,下面是个参考。 Goto:Implementing L2-constrained Softmax Loss Function on a Convolutional Neural Network using TensorFlow End....
然后,要除以温度系数,再进行 loss 的计算,loss_func 采用 CE loss,注意 CE loss 中是自带 softmax 计算的。 # 计算score和loss norm_emb = F.normalize(batch_emb, dim=1, p=2) sim_score = torch.matmul(norm_emb, norm_emb.transpose(0,1)) ...
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之后即可构造自定义的triplet loss func: def semi_hard(labels, embeddings): labels = K.squeeze(labels, axis=1) return tf.contrib.losses.metric_learning.triplet_semihard_loss(labels, embeddings, margin=1.0) 最后在compile中调用即可: model.compile(optimizer=optim, loss=[classify_loss, semi_hard],...
y = torch.tensor([-1]) #创建损失函数 loss_func = nn.CosineEmbeddingLoss(margin=0.0) #计算损失 loss = loss_func(x1, x2, y) #打印结果 print(loss) ```©2022 Baidu |由 百度智能云 提供计算服务 | 使用百度前必读 | 文库协议 | 网站地图 | 百度营销 ...