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?
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. ...
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....
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 ...
(Bonus: Here’s someone coding in TensorFlow with a PyTorch influence:Building a Multi-label Text Classifier using BERT and TensorFlow) Conclusion If you have GPUs available, you’re typically not going to see any major differences between either framework. However, please keep in mind the abo...
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. ...
# initialise model tft = TemporalFusionTransformer.from_dataset( training, learning_rate=0.03, hidden_size=16, # biggest influence network size attention_head_size=1, dropout=0.1, hidden_continuous_size=8, output_size=7, # QuantileLoss has 7 quantiles by default loss=QuantileLoss(), log_...