pytorch scheduler step
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Certainly! Below is an informative tutorial about PyTorch scheduler StepLR with a code example.
PyTorch provides various learning rate schedulers that dynamically adjust the learning rate during training to improve model convergence and performance. The StepLR scheduler is one such scheduler that decreases the learning rate by a factor gamma every step_size epochs.
The StepLR scheduler in PyTorch allows us to decrease the learning rate by a specified factor at predefined epochs during the training process.
Let's demonstrate how to use the StepLR scheduler with PyTorch by training a simple neural network on a synthetic dataset.
Model Definition: A simple neural network (SimpleNN) with one linear layer is created.
Dataset Preparation: A synthetic dataset using FakeData from torchvision is generated.
Model Training: Training loop runs for a specified number of epochs. Inside the loop, the model parameters are updated using the optimizer (SGD in this case) and the loss is calculated.
Scheduler Setup: StepLR scheduler is initialized with a step size of 5 epochs and a gamma value of 0.1. The scheduler is updated at the end of each epoch using scheduler.step().
Training Loop with Scheduler: The learning rate is adjusted by the scheduler based on the specified step size and gamma value, effectively decreasing the learning rate at predefined epochs during training.
Output: The training loop prints the epoch number and the average loss for each epoch.
This tutorial covers the basic implementation of the StepLR scheduler in PyTorch. Adjusting the step size and gamma allows for experimentation to find the optimal learning rate schedule for your specific model and dataset.
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