Best model saving pattern in PyTorch - Model Pipeline Trace

Model Pipeline - Best model saving pattern

This pipeline trains a PyTorch model and saves the best version based on validation accuracy. It ensures the model with the highest validation accuracy is saved for later use.

Data Flow - 4 Stages

1Data Loading

1000 rows x 20 features→Load dataset and split into training and validation sets→800 rows x 20 features (train), 200 rows x 20 features (val)

Train data sample: [0.5, 1.2, ..., 0.3], Label: 1

↓

2Model Initialization

20 features→Define a simple neural network with input, hidden, and output layers→Model with parameters for 20 input features

Layer sizes: Input(20) -> Hidden(10) -> Output(2)

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3Training Loop

800 rows x 20 features→Train model for multiple epochs, evaluate on validation set each epoch→Trained model and validation accuracy per epoch

Epoch 1: val_accuracy=0.75, Epoch 2: val_accuracy=0.80

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4Best Model Saving

Validation accuracy per epoch→Save model state dict only if validation accuracy improves→Best model saved to disk

Saved model at epoch 2 with val_accuracy=0.80

Training Trace - Epoch by Epoch

Loss: 0.65 |\
       0.50 | \
       0.45 |  \
       0.40 |   \
       0.38 |    \
          +----------------
          1  2  3  4  5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.70	Initial training with moderate accuracy
2	0.50	0.78	Accuracy improved, model saved
3	0.45	0.75	Accuracy dropped, model not saved
4	0.40	0.82	New best accuracy, model saved
5	0.38	0.80	Accuracy slightly lower, model not saved

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer with ReLU

Layer 3: Output Layer with Softmax

Layer 4: Prediction

Model Quiz - 3 Questions

Why do we save the model only when validation accuracy improves?

A Because training loss always decreases

B To keep the best performing model on unseen data

C To save disk space by not saving every epoch

D To avoid saving the model at the first epoch

Key Insight

Saving the model only when validation accuracy improves ensures that the best version of the model is kept. This prevents overfitting and saves storage by not saving every epoch. Monitoring validation accuracy is key to selecting the best model.