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StepLR Scheduler

EasyLearning Rate Schedulers

Problem Statement

Use StepLR to decay the learning rate by a factor every N epochs.

Background

StepLR(optimizer, step_size, gamma) multiplies LR by gamma every step_size epochs. It's the simplest scheduling strategy.

Your Task

The starter code creates a model and SGD optimizer. Create a StepLR scheduler that halves the learning rate every 3 epochs.

The epoch loop that records LR history is pre-filled.

Output Format

Returns a dictionary with "lr_history" (10 values), "initial_lr", and "final_lr".

Example:

Input:
None
Output:
{'lr_history': [0.1, 0.1, 0.1, 0.05, 0.05, 0.05, 0.025, 0.025, 0.025, 0.0125], 'initial_lr': 0.1, 'final_lr': 0.0125}
Reasoning:
  • We initialize the learning rate to 0.1 and create a StepLR scheduler with step_size=3 and gamma=0.5, meaning the learning rate will be multiplied by 0.50.5 every 3 epochs.
  • For the first 3 epochs, the learning rate remains at 0.1, as the scheduler hasn't reached its first step.
  • At epoch 3, the scheduler steps and multiplies the learning rate by 0.50.5, resulting in a new learning rate of 0.1⋅0.5=0.050.1 \cdot 0.5 = 0.05, which remains for the next 2 epochs.
  • This process repeats, with the learning rate being multiplied by 0.50.5 every 3 epochs, resulting in the sequence: 0.1, 0.1, 0.1, 0.05, 0.05, 0.05, 0.025, 0.025, 0.025, 0.0125.

Constraints:

  • step_size=3, gamma=0.5
  • Record LR before each scheduler.step()
  • 10 epochs total
Editor

Test Results

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Run code to see test results.