FilesExpand file tree

 master

/

06_1_loss_and_optimizer.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

57 lines (40 loc) · 1.28 KB

 master

/

06_1_loss_and_optimizer.py

Copy path

Top

File metadata and controls

• Code
• Blame

57 lines (40 loc) · 1.28 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

# 1) Design model (input, output, forward pass with different layers)

# 2) Construct loss and optimizer

# 3) Training loop

# - Forward = compute prediction and loss

# - Backward = compute gradients

# - Update weights

import torch

import torch.nn as nn

# Linear regression

# f = w * x

# here : f = 2 * x

# 0) Training samples

X = torch.tensor([1, 2, 3, 4], dtype=torch.float32)

Y = torch.tensor([2, 4, 6, 8], dtype=torch.float32)

# 1) Design Model: Weights to optimize and forward function

w = torch.tensor(0.0, dtype=torch.float32, requires_grad=True)

def forward(x):

return w * x

print(f'Prediction before training: f(5) = {forward(5).item():.3f}')

# 2) Define loss and optimizer

learning_rate = 0.01

n_iters = 100

# callable function

loss = nn.MSELoss()

optimizer = torch.optim.SGD([w], lr=learning_rate)

# 3) Training loop

for epoch in range(n_iters):

# predict = forward pass

y_predicted = forward(X)

# loss

l = loss(Y, y_predicted)

# calculate gradients = backward pass

l.backward()

# update weights

optimizer.step()

# zero the gradients after updating

optimizer.zero_grad()

if epoch % 10 == 0:

print('epoch ', epoch+1, ': w = ', w, ' loss = ', l)

print(f'Prediction after training: f(5) = {forward(5).item():.3f}')