Training and validation loss tracking in PyTorch - Model Pipeline Trace

Model Pipeline - Training and validation loss tracking

This pipeline trains a simple model and tracks both training and validation loss to monitor learning progress and detect overfitting.

Data Flow - 5 Stages

1Load Dataset

1000 rows x 10 columns→Load raw data with features and labels→1000 rows x 10 columns

Feature row: [0.5, 1.2, ..., 0.3], Label: 1

↓

2Train/Test Split

1000 rows x 10 columns→Split data into training (80%) and validation (20%) sets→Train: 800 rows x 10 columns, Validation: 200 rows x 10 columns

Train sample: [0.4, 1.0, ..., 0.2], Label: 0

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3Data Normalization

Train: 800 rows x 10 columns→Scale features to zero mean and unit variance→Train: 800 rows x 10 columns (normalized)

Normalized feature row: [-0.1, 0.3, ..., 0.0]

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4Model Training

Train: 800 rows x 10 columns→Feed features to model and update weights using training loss→Model parameters updated

Batch input: 32 rows x 10 columns

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5Validation Evaluation

Validation: 200 rows x 10 columns→Evaluate model on validation set to compute validation loss→Validation loss scalar

Validation batch input: 32 rows x 10 columns

Training Trace - Epoch by Epoch

Loss
0.9 |*       
0.8 | *      
0.7 |  *     
0.6 |   *    
0.5 |    *   
0.4 |     *  
0.3 |      * 
    +--------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Training loss starts high, accuracy moderate
2	0.65	0.72	Loss decreases, accuracy improves
3	0.50	0.80	Model learns well, loss drops further
4	0.40	0.85	Training loss continues to decrease
5	0.35	0.88	Training loss low, accuracy high

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU)

Layer 3: Output Layer (Sigmoid)

Layer 4: Loss Calculation

Model Quiz - 3 Questions

Why do we track validation loss during training?

A To check if the model is learning patterns that generalize

B To increase training speed

C To reduce the size of the dataset

D To make the model more complex

Key Insight

Tracking both training and validation loss helps us understand if the model is learning well and generalizing to new data, preventing overfitting.