importpytorch_influence_functionsasptif# Supplied by the user:model=get_my_model()trainloader,testloader=get_my_dataloaders()ptif.init_logging()config=ptif.get_default_config()influences,harmful,helpful=ptif.calc_img_wise(config,model,trainloader,testloader)# do someting with influences/harmful/...
8 changes: 8 additions & 0 deletions 8 pytorch_influence_functions/calc_influence_function.py Original file line numberDiff line numberDiff line change @@ -365,6 +365,11 @@ def get_dataset_sample_ids_per_class(class_id, num_samples, test_loader,...
In deep learning, several factors influence performance levels. Key considerations include the training speed, effectiveutilization of GPUs, and proficiency in handling extensive models and datasets. PyTorch and TensorFlow use GPU acceleration, utilizingNVIDIA CUDAor AMD ROCm, to boost the efficiency of ...
Soumith, I think that you are probably one of the people who is having the greatest impact on AI today given your influence on the PyTorch ecosystem, and the question I kind of want to start with, as I always do, is the origin story. How did you get into AI in the first place?
GAN(生成对抗网络)是用于教授DL模型以捕获训练数据分布的框架,因此可以从同一分布中生成新数据。它们由两个不同的模型组成,生成器和判别器。生成器的工作是生成看起来像训练图像的假图像,判别器的工作是查看图像并从生成器输出它是真实地训练图像还是伪图像。在训练过程中,生成器不断尝试通过生成越来越好地伪造品而使...
s artificial identity for the purpose of knowingly deceiving a person about the content of the communication. It applies where the person is trying to incentivize a purchase or sale of goods or services in a commercial transaction or to influence voting. No liability attaches, however, if the ...
# # .. figure:: /_static/img/dcgan_generator.png # :alt: dcgan_generator # # Notice, the how the inputs we set in the input section (*nz*, *ngf*, and # *nc*) influence the generator architecture in code. *nz* is the length # of the z input vector, *ngf* relates to the...
per input example using a distribution of baselines. DeepLIFT’s non-linearity rules help to linearize the network’s non-linear functions, and the method’s approximation of SHAP values also applies to the linearized network. Input features are likewise presumed to be independent in this method....
Hyperparameter tuning is the process of selecting the best values for parameters that govern the training of a machine learning model but are not learned from the data itself. These parameters directly influence how the model optimizes and converges. ...
The problem with this architecture for interpretability is that the inputs get all completely mixed together because of the fully connected layers. Each single input node influences all hidden layer nodes, and this influence gets more complicated the deeper we go into the network. ...