Backward pass (loss.backward) in PyTorch - Model Pipeline Trace

Model Pipeline - Backward pass (loss.backward)

This pipeline shows how a simple neural network learns by calculating errors and then updating itself using the backward pass with loss.backward(). This step helps the model improve by adjusting weights based on the error.

Data Flow - 5 Stages

1Data input

1000 rows x 3 columns→Raw input features representing 3 measurements per sample→1000 rows x 3 columns

[[0.5, 1.2, 3.3], [1.1, 0.7, 2.8], ...]

↓

2Forward pass

1000 rows x 3 columns→Input passed through linear layer and activation to produce predictions→1000 rows x 1 column

[[0.7], [0.3], ...]

↓

3Loss calculation

1000 rows x 1 column (predictions) and 1000 rows x 1 column (targets)→Calculate mean squared error between predictions and true values→Scalar loss value

loss = 0.25

↓

4Backward pass

Scalar loss value→Compute gradients of loss with respect to model parameters using loss.backward()→Gradients stored in model parameters

model.weight.grad = tensor([[0.1, -0.05, 0.02]])

↓

5Parameter update

Model parameters and their gradients→Adjust model weights using gradients (e.g., optimizer.step())→Updated model parameters

model.weight updated from tensor([[0.5, 0.3, 0.2]]) to tensor([[0.49, 0.305, 0.198]])

Training Trace - Epoch by Epoch

Loss
0.5 |*****
0.4 |**** 
0.3 |***  
0.2 |**   
0.1 |*    
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.50	0.60	Initial loss is high; accuracy is low as model starts learning
2	0.35	0.72	Loss decreased; accuracy improved after backward pass updates
3	0.25	0.80	Loss continues to decrease; model is learning well
4	0.18	0.85	Loss decreasing steadily; accuracy improving
5	0.12	0.90	Model converging with low loss and high accuracy

Prediction Trace - 5 Layers

Layer 1: Input layer

Layer 2: Linear layer

Layer 3: Activation (ReLU)

Layer 4: Loss calculation

Layer 5: Backward pass (loss.backward())

Model Quiz - 3 Questions

What does the backward pass (loss.backward()) compute?

A Input data normalization

B Final predictions of the model

C Gradients of loss with respect to model parameters

D Loss value calculation

Key Insight

The backward pass is essential for learning. It calculates gradients that tell the model how to adjust its weights to reduce errors. This process repeated over epochs helps the model improve its predictions.