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PyTorch Tutorial
PyTorch Tutorial PyTorch Introduction PyTorch Installation PyTorch Basics PyTorch Tensor PyTorch Neural Network Basics PyTorch First Neural Network PyTorch Data Processing and Loading PyTorch Linear Regression PyTorch Convolutional Neural Network PyTorch Recurrent Neural Network PyTorch Datasets PyTorch Data Transformation Pytorch torch Reference Manual PyTorch torch.nn Reference Manual Transformer Model PyTorch Building Transformer Model PyTorch torch.optim Optimizer Module PyTorch torchvision Computer Vision Module PyTorch Model Deployment PyTorch Model Save and Load PyTorch Example - Image Classification Project PyTorch Example - Text Sentiment Analysis Project PyTorch Autograd Automatic Differentiation PyTorch GPU / CUDA Acceleration PyTorch Loss Function PyTorch Learning Rate Scheduler PyTorch Transfer Learning PyTorch Batch Normalization and Dropout PyTorch LSTM / GRU PyTorch Word Embedding (Embedding) PyTorch Generative Adversarial Network (GAN) PyTorch Autoencoder (Autoencoder) PyTorch Model Evaluation and Debugging PyTorch torchtext PyTorch Mixed Precision Training (AMP) PyTorch TorchScript/ONNX Export PyTorch Distributed Training PyTorch Attention Mechanism
torch.set_rng_state
The torch.set_rng_state function in PyTorch is used to set the random number generator (RNG) state. This is particularly useful when you want to ensure reproducibility in your experiments by setting a specific seed for the RNG.
import torch
# Set the RNG state
state = torch.get_rng_state()
torch.set_rng_state(state)
In the example above, we first get the current RNG state using torch.get_rng_state(), and then set it back using torch.set_rng_state(). This ensures that any subsequent random operations will use the same sequence of random numbers.
This function is especially important in scenarios where you need to reproduce results exactly, such as in scientific research or when debugging code.
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