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Gradient Descent Calculator

Calculate gradient descent parameters, convergence rate, effective learning rate, and training time estimates.

Inputs

Results

Steps per Epoch

313

Total Steps

313,000

Effective Learning Rate

0.1

Convergence Rate0
Training Time1.67 minutes
Final Learning Rate0
How to Use This Calculator
  1. Enter the learning rate (alpha), the number of training iterations, and the momentum coefficient.
  2. Set the batch size and dataset size to determine steps per epoch and total steps.
  3. Review the steps per epoch and total steps calculated for your training run.
  4. Check the effective learning rate (adjusted for momentum), convergence rate, and final learning rate after decay.
  5. Use the training time estimate and simulated loss decay chart to gauge how long training will take and how quickly loss decreases.

How the result changes with Batch Size

Batch SizeSteps per EpochTotal StepsEffective Learning Rate
1,0011010,0000.1
3,50133,0000.1
6,50022,0000.1
9,00022,0000.1

What each input means

Learning Rate
Learning rate (step size)
Number of Iterations
Total training iterations
Momentum
Momentum coefficient
Batch Size
Samples per batch
Dataset Size
Total training samples

How this is calculated

Formula

θ_new = θ_old - α × ∇J(θ)

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    Learning Rate = 0.01, Number of Iterations = 1000, Momentum = 0.9, Batch Size = 32 = 5 input(s) provided
  2. Calculate Steps per Epoch
    Steps per Epoch
    313 = 313
  3. Calculate Total Steps
    Total Steps
    313000 = 313000
  4. Calculate Effective Learning Rate
    Effective Learning Rate
    0.1 = 0.1
  5. Calculate Convergence Rate
    Convergence Rate
    0 = 0
  6. Calculate Training Time
    Training Time
    1.67 = 1.67

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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