Integrated Gradients这个方法来源于论文Axiomatic Attribution for Deep Networks,于2017年发表于34th International Conference on Machine Learning 上,因为是在会议上的一篇论文,所以整体篇幅比较简洁。作者是来自Google的三位科学家,文章在最下方标注了三个人是均等贡献。文章标题的中文翻译叫做“深度网络的公理归因”,...
首先引入必要的库,并设计一个小模型。 接下来,初始化模型的输入和IntegratedGradients方法所需要的baseline。 之后,使用两行代码完成解释,我们要做的,就是搞清楚这两行代码里面干了什么。 直接点进ig.attribute看看里面干了什么 首先从函数的参数说起 inputs不用说,要放入模型的输入 baselines:ig模型需要的baseline t...
1、梯度归因(Gradients) 2、基于反向传播的归因 积分梯度算法(Integrated Gradients) 完整度(Completeness) 积分路径的选择 保对称性(Symmetry-Preserving) 两大公理的形象化理解 敏感性 实现不变性 积分梯度的应用变式及应用实例 基准的选择 应用变式 应用实例 1、目标检测网络 2、糖尿病性视网膜病变图像的预测 可解释...
ig = IntegratedGradients(model) ig.attribute(inputs) after which I get the error: AssertionError: Baseline can be provided as a tensor for just one input and broadcasted to the batch or input and baseline must have the same shape or the baseline corresponding to each input tensor must be...
(5):fwd_fn=single_output_forward(i)integrated_gradients=IntegratedGradients(fwd_fn)prediction_score,pred_label_idx=torch.topk(fwd_fn(x),1)print('Predicted class',pred_label_idx)print('Prediction score',prediction_score)attributions_ig=integrated_gradients.attribute(x,target=pred_label_idx,n_...
可解释性与积分梯度 Integrated Gradients 积分梯度是一种神经网络可解释性方法 此方法首先在论文《Gradients of Counterfactuals》中提出,后来 《Axiomatic Attribution for Deep Networks》再次介绍了它,这已经是2016～2017年间的工作了...
Remarkably, the prediction error of these machine-learning models has become comparable to the inherent error between the Dst index itself and the actual ring-current strength.To understand the physical process behind the forecasting model, the IG algorithm was applied in our prediction model, in ...
(p\)value###in STREME was calculated by a one-sided binomial test. The motifs within the blue dashed anchor boxes were extracted to do pair comparisons. IG scores were calculated by the average of the contribution scores of each nucleotide obtained by the integrated gradients method. Accession...
Wrap it with an integrated_gradients instance. ig = integrated_gradients(model) Call explain() with a sample to explain. ig.explain(X[0]) ==> array([-0.25757075, -0.24014562, 0.12732635, 0.00960122]) Features supports both Sequential() and Model() instances. supports both TensorFlow and Thean...
def forward_fun(x_d, x_s, x_one_hot): out = lstm(x_d, x_s, x_one_hot)[0][:,-1] #0 is for the cell output and -1 is for the last index in the ouput series return out ig = IntegratedGradients(forward_fun) attrs = [] for i in range(x_d.shape[0]): attr = ig....