The binary cross entropy loss is defined as: Binary cross-entropy loss In binary classification, there are two output probabilities p_i and (1-p_i) and ground truth values y_i and (1-y_i). The multi-class classification problem uses the generalization of BCE loss for ...
5.3 示例1:单变量回归 - Example 1: univariate regression 5.4 示例2:二分类 - Example 2: binary classification 5.5 示例3:多类分类 - Example 3: multiclass classification 5.6 多输出 - Multiple outputs 5.7 交叉熵损失 - Cross-entropy loss 5.8 总结 - Summary 6模型拟合- Fitting models 6.1 梯度下降...
其中H_{3d} 和H_{2d} 分别是对于3D和2D的 cross entropy losses 。 \lambda 是一个用来平衡2D和3D loss的权重,实验上经验上被设置为0.1。 算法的实现基于Pytorch和 MinkowskiEngine sparse convolution库,在ScanNetV2数据集上训练了100个epochs。2D UNet由ImageNet上预训练得到,3D部分随机初始化,使用基础学习率为...
计算mask 的 hidden_states(最后一层),mask 选择不含 padding 的 input tokens(同上面第二种,也是输入的 attention mask),size 为(sum(lenghts), hidden_dim)。计算 Lcos(余弦嵌入),并计算累计 loss:loss += alpha_cos × Lcos 这样,我们就和前面的理论对应起来了,感性地理解,第一个和第三个 loss 是和教...
Then, Ti will be used to predict the original token with cross entropy loss. We compare variations of this procedure in Appendix C.2 虽然这使我们能够获得双向预训练模型,但缺点是我们在预训练和微调之间造成了不匹配,因为 [MASK] Token在微调期间不会出现。 为了缓解这种情况,我们并不总是用实际的 [...
然而,术语“交叉熵损失 (cross-entropy loss)”也广为流传。在本节中,我们将解释交叉熵损失,并证明它与使用负对数似然是等价的。 交叉熵损失的核心思想是寻找参数 θθ,以最小化观测数据 yy 的经验分布 q(y)q(y) 与模型分布 Pr(y|θ)Pr(y|θ)(见图 5.12)之间的差距。可以用 Kullback-Leibler (KL) ...
接下来重点解析一下作者的预训练方法。为了训练一个深度双向表示模型,作者提出了一种屏蔽一句话中的部分词的训练方法,然后让模型来预测屏蔽的那个词(同比于CBOW,就是根据这个词的上下文,去预测每一个词,损失函数由所有词的loss组成(cross-entropy),而本文的loss只来源于那些被屏蔽的词)。
def cross_entropy_loss(output, targets): log_probs = output.log_softmax(dim=-1) predictions = log_probs[:, 0] batch, out_dim = predictions.shape true_output = predictions[range(batch), targets] return -true_output.sum() / batch def test(model, data, loss_...
Mathematically, for a target distribution y and model output ŷ, the cross-entropy loss can be expressed as:During training, the network tweaks its weights to minimize this loss. Now, the central aspect governing how much a weight should change is the learning rate. In the stochastic ...
2.2 Probabilistic Interpretation of Neural Network Output If the final layer of a neural network is a soft-max layer and the network is trained using cross entropy loss, then the output may be interpreted as a probability distribution over the categorical variables. Thus, at a given θ, the ...