Forward pass, loss, backward, step in PyTorch - Model Pipeline Trace

Model Pipeline - Forward pass, loss, backward, step

This pipeline shows how a simple neural network learns by passing data forward, calculating loss, going backward to find errors, and updating weights step-by-step.

Data Flow - 5 Stages

1Input Data

64 rows x 10 columns→Batch of 64 samples, each with 10 features→64 rows x 10 columns

[[0.5, 0.1, ..., 0.3], ..., [0.2, 0.4, ..., 0.7]]

↓

2Forward Pass

64 rows x 10 columns→Data passed through linear layer and activation→64 rows x 3 columns

[[1.2, 0.5, -0.3], ..., [0.7, 1.1, 0.0]]

↓

3Loss Calculation

64 rows x 3 columns (predictions), 64 rows (labels)→Calculate cross-entropy loss between predictions and true labels→Scalar loss value

Loss = 1.23

↓

4Backward Pass

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

Gradients like weight.grad = [[0.01, -0.02, ...], ...]

↓

5Optimizer Step

Model parameters with gradients→Update model weights using gradients and learning rate→Updated model parameters

Weights changed from 0.5 to 0.48

Training Trace - Epoch by Epoch

Loss
1.3 |*       
1.1 | *      
0.9 |  *     
0.7 |   *    
0.5 |    *   
    +---------
     1 2 3 4 5
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.23	0.45	Loss starts high, accuracy low as model begins learning
2	0.95	0.60	Loss decreases, accuracy improves
3	0.75	0.70	Model continues to learn, better predictions
4	0.60	0.78	Loss steadily decreases, accuracy rises
5	0.50	0.82	Training converging, model improving

Prediction Trace - 7 Layers

Layer 1: Input Layer

Layer 2: Linear Layer

Layer 3: Activation (ReLU)

Layer 4: Softmax

Layer 5: Loss Calculation

Layer 6: Backward Pass

Layer 7: Optimizer Step

Model Quiz - 3 Questions

What happens during the backward pass?

A Calculate gradients of loss with respect to weights

B Update weights using gradients

C Pass input data through the model

D Calculate the loss value

Key Insight

This visualization shows how a model learns step-by-step: it passes data forward to predict, measures error with loss, goes backward to find how to improve, and updates weights to get better over time.