for file_name in pretrained_files : file_path = os. makedirs ( CHECKPOINT_PATH, exist_ok = True ) # For each file, check whether it already exists. # Github URL where saved models are stored for this tutorial base_url = "" # Files to download pretrained_files = # Create checkpoint path if it doesn't exist yet os. device ( "cuda:0" ) print ( "Using device", device ) benchmark = False # Fetching the device that will be used throughout this notebook device = torch. manual_seed_all ( seed ) set_seed ( 42 ) # Additionally, some operations on a GPU are implemented stochastic for efficiency # We want to ensure that all operations are deterministic on GPU (if used) for reproducibility torch. is_available (): # GPU operation have separate seed torch. get ( "PATH_CHECKPOINT", "saved_models/Activation_Functions/" ) # Function for setting the seed def set_seed ( seed ): np. get ( "PATH_DATASETS", "data/" ) # Path to the folder where the pretrained models are saved CHECKPOINT_PATH = os. # Path to the folder where the datasets are/should be downloaded (e.g. In case you are on Google Colab, it is recommended toĬhange the directories to start from the current directory (i.e. remove. The needed files will be automatically downloaded. The checkpoint path is the directory where we will store trained model weights and additional files. It is recommended to store all datasets from PyTorch in one joined directory to prevent duplicate downloads. The dataset path is the directory where we will download datasets used in the notebooks. All models here have been trained on an NVIDIA GTX1080Ti.Īdditionally, the following cell defines two paths: DATASET_PATH and CHECKPOINT_PATH. However, note that in contrast to the CPU, the same seed on different GPU architectures can give different results. This allows us to make our training reproducible. We will define a function to set a seed on all libraries we might interact with in this tutorial (here numpy and torch). Set_matplotlib_formats("svg", "pdf") # For export tmp/ipykernel_749/3776275675.py:24: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_t_matplotlib_formats()` Multi-agent Reinforcement Learning With WarpDrive.Finetune Transformers Models with PyTorch Lightning.PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.GPU and batched data augmentation with Kornia and PyTorch-Lightning.Tutorial 13: Self-Supervised Contrastive Learning with SimCLR.Tutorial 12: Meta-Learning - Learning to Learn.Tutorial 10: Autoregressive Image Modeling.Tutorial 9: Normalizing Flows for Image Modeling.Tutorial 7: Deep Energy-Based Generative Models.Tutorial 6: Basics of Graph Neural Networks.Tutorial 5: Transformers and Multi-Head Attention.Tutorial 4: Inception, ResNet and DenseNet.Tutorial 3: Initialization and Optimization.LightningLite (Stepping Stone to Lightning).Organize existing PyTorch into Lightning.The derivative is provided w.r.t f(□) if possible, but in instances this may not be the case then it would be w.r.t □. I’ve not read ‘ The Hitchhiker’s Guide to the Galaxy’. If we included the Identity Activation function this list would contain 42 activation functions, although you could say with the inclusion of the bipolar sigmoid that it is indeed 42. Typically one would use tanh for an FNN and ReLU for a CNN. I have also provided additional links to appropriate research papers for activation functions where none had been, or in cases where no specific research paper could be located a relevant paper of interest is provided in-place. In this article, I have written an algorithm to mine every unique Activation function out of the history of this Wikipedia page as of the 22nd of April 2022 so that I can list them all in one comprehensive document here. Since then, at the time of writing this article, there have been 391 changes to the Wikipedia page. The first introduction of the ‘table of activation functions’ was in November 2015 by the user Laughinthestocks. You can view a list of historical changes to this particular Wikipedia page here. A great place to find and learn about activation functions is Wikipedia however, over the years, the table of activation functions has fluctuated wildly, functions have been added and removed time and time again.
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