Model architectures ๐

Metrics ๐

Solvers / Optimizers ๐งฎ

Scheduler โฐ

Training parameters

Augmentations

DEPLOYMENT

ASGD

Stochastic gradient descent with averaged parameters

Average Stochastic Gradient Descent, abbreviated as ASGD, averages the weights that are calculated in every iteration.

$w_{t+1}=w_t-\eta \nabla Q(w_t)$

where

$w_t$

being the weight tensor , $\eta$

being the base learning rate and $\nabla Q(w_t)$

being the gradient of the objective function evaluated at $w_t$

. With the given update rule SGD assigns calculated weight to the model. But with ASGD assigns the following averaged weight

$\overline{w}$

,$\overline{w}=\frac{1}{N} \sum_{t=1}^Nw_t$

where

$w_t$

is the weight tensor calculated in iteration 't'.Such averaging is used when the data is noisy.

Major Parameters

- Lambda
- Alpha
- TO

Lambda

It is the decay term for the past weights used in the average.

Alpha

It is the power value that is used to update the learning rate.

TO

It is the optimization step at which the averaging is started. If the required number of iteration is lower than the TO value, then the averaging will not happen.

Code Implementation

1

# importing the library

2

import torch

3

import torch.nn as nn

4

โ

5

x = torch.randn(10, 3)

6

y = torch.randn(10, 2)

7

โ

8

# Build a fully connected layer.

9

linear = nn.Linear(3, 2)

10

โ

11

# Build MSE loss function and optimizer.

12

criterion = nn.MSELoss()

13

โ

14

# Optimization method using ASGD

15

optimizer = torch.optim.ASGD(linear.parameters(), lr=0.01, lambd=0.0001,

16

alpha=0.75, t0=1000000.0, weight_decay=0)

17

โ

18

# Forward pass.

19

pred = linear(x)

20

โ

21

# Compute loss.

22

loss = criterion(pred, y)

23

print('loss:', loss.item())

24

โ

25

optimizer.step()

Copied!

โ

Last modified 3mo ago

Copy link