Loseis always a verb.Lossis always a noun. Both words can be used in multiple ways and for both tangible and intangible things. You canloseyour wallet, your password, weight, a game, a job, a loved one, track of time.Losscan be used in many of the same situations, but it refers ...
理清了softmax loss,就可以来看看cross entropy了。 corss entropy是交叉熵的意思,它的公式如下: 是不是觉得和softmax loss的公式很像。当cross entropy的输入P是softmax的输出时,cross entropy等于softmax loss。Pj是输入的概率向量P的第j个值,所以如果你的概率是通过softmax公式得到的,那么cross entropy就是softm...
然后需要做两个计算:首先是自己层内的参数的 gradient,比如如果是一个普通的全连通内积层,则会有参数 和 bias 参数 ,根据刚才的 Chain Rule 式子直接计算就可以了,如果 层没有参数(例如 Softmax 或者 Softmax-Loss 层就是没有参数的。),这一步可以省略;其次就是“向后传递”的步骤,同样地,根据...
As nouns the difference between loss and losses is that loss is an instance of losing, such as a defeat while losses is...
lostvs.lossed The termlossedmay result from someone hearing the wordlostspoken out loud and mistakenly transcribing it aslossed. Alternatively, someone may not know how toconjugatethe verbloseand mistakenly uselossed,losed,or another incorrect past tense or past participle form. ...
http://freemind.pluskid.org/machine-learning/softmax-vs-softmax-loss-numerical-stability/ softmax 在 Logistic Regression 里起到的作用是将线性预测值转化为类别概率 1.最大似然估计通常使用log-likelihood,并且是negative log-likelihood,将最大化转换为最小化 ...
L1、L2正则VS L1、L2 loss 查看原文 正则化和范数 L1范数: ||X||1=(|x1|+|x2|+...+|xn|)L2范数: ||X||2=(|x1|2+|x2|2+...+|xn|2)12 特别的,L0范数:指向量中非零元素的个数。无穷范数:指向量中所有元素的最大绝对值。L1正则化和L2正则化可以看做是损失函数的惩罚项。所谓『惩罚』...
Softmax vs. Softmax-Loss: Numerical StabilityNeural, Deep ConvolutionalLearning, DeepRegression, LogisticRegression, Logistic
Softmax vs. Softmax-Loss: Numerical Stability,softmax在LogisticRegression里起到的作用是将线性预测值转化为类别概率1.最大似然估计通常使用log-likelihood,并且是negativelog-likelihood,将最大化转换为最小化2.softmaxloss是将softmax和最大似然估计结合起来 sof
Loss, on the other hand, is a noun. It stands for the result of losing or the feeling experienced when something or someone is gone. Experiencing a "loss" can be emotional or materialistic. 14 Lose can also mean failing to maintain a position or status. For instance, one might "lose"...