Zeroing gradients in PyTorch - Model Pipeline Trace

Model Pipeline - Zeroing gradients

This pipeline shows how zeroing gradients helps prepare a model for a new training step by clearing old gradient values. This prevents mixing old and new gradient information, which keeps training stable and accurate.

Data Flow - 6 Stages

1Data loading

1000 rows x 10 features→Load dataset with 1000 samples and 10 features each→1000 rows x 10 features

[[0.5, 1.2, ..., 0.3], [0.1, 0.4, ..., 0.9], ...]

↓

2Model input

1000 rows x 10 features→Feed input data to model→1000 rows x 1 output

[[0.7], [0.2], ...]

↓

3Loss calculation

1000 rows x 1 output→Calculate loss between predictions and true labels→Scalar loss value

0.45

↓

4Zeroing gradients

Model parameters with old gradients→Clear gradients from previous training step→Model parameters with zero gradients

All gradients set to 0 before backward pass

↓

5Backward pass

Scalar loss→Compute gradients of loss w.r.t. model parameters→Gradients stored in model parameters

Gradients like tensor([0.01, -0.02, ...])

↓

6Optimizer step

Model parameters with gradients→Update model parameters using gradients→Updated model parameters

Weights updated to new values

Training Trace - Epoch by Epoch

Loss
0.7 |*       
0.6 | *      
0.5 |  *     
0.4 |   *    
0.3 |    *   
0.2 |     *  
0.1 |       
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Initial loss is high; accuracy is low as model starts learning
2	0.48	0.75	Loss decreases and accuracy improves after zeroing gradients each step
3	0.35	0.82	Continued improvement shows stable training with proper gradient management
4	0.28	0.87	Loss keeps decreasing; zeroing gradients prevents gradient accumulation
5	0.22	0.90	Model converges well with clear gradients each step

Prediction Trace - 6 Layers

Layer 1: Input layer

Layer 2: Forward pass

Layer 3: Loss calculation

Layer 4: Zeroing gradients

Layer 5: Backward pass

Layer 6: Optimizer step

Model Quiz - 3 Questions

Why do we zero gradients before the backward pass?

A To save memory by deleting gradients permanently

B To increase the gradient values for faster learning

C To clear old gradients so they don't add up with new ones

D To reset model weights to initial values

Key Insight

Zeroing gradients is a crucial step in training neural networks. It clears old gradient values before computing new ones, preventing unwanted accumulation. This keeps training stable and helps the model learn correctly over time.