11 'batchnorm_3' Batch Normalization Batch normalization with 32 channels 12 'relu_3' ReLU ReLU 13 'conv_4' 2-D Convolution 32 3x3x32 convolutions with stride [1 1] and padding 'same' 14 'batchnorm_4' Batch Normalization Batch normalization with 32 channels 15 'relu_4' ReLU ReLU 16...
()# < construct the modelmodel.fit()# < train the modelattributions=de.explain(...)# < compute attributions# Option 2. First create and train your model, then apply DeepExplain.# IMPORTANT: in order to work correctly, the graph to analyze# must always be (re)constructed within the ...
[2] BN_cause_of_Adv_Vul; Galloway el al."Batch Normalization is a Cause of Adversarial Vulnerability" ICML Workshop, 2019. [3] Fixup; Zhang et al. "Fixup initialization: Residual learning without normalization", ICML, 2019. [4] RetinaNet; Lin et al. "Focal Loss for Dense Object Detec...
batch_sizeint, optionalBy default, DeepExplain will try to evaluate the model using all data inxsat the same time. Ifxscontains many samples, it might be necessary to split the processing in batches. In this case, providing abatch_sizegreater than zero will automatically split the evaluation ...
3x3 Convolution Layer + activation function (with batch normalization) 2x2 Max Pooling Note that the number of feature maps doubles at each pooling, starting with 64 feature maps for the first block, 128 for the second, and so on. The purpose of this contracting path is to capture the cont...
在继续往下讲之前,我们还需再提一下编码器层中的一个细节:每个编码器层中的每个子层(自注意力层、前馈神经网络)都有一个残差连接(图中的Add),之后是做了一个层归一化(layer-normalization)(图中的Normalize)。 将过程中的向量相加和layer-norm可视化如下所示: ...