CosineAnnealingLR in PyTorch - Model Pipeline Trace

Model Pipeline - CosineAnnealingLR

This pipeline shows how the CosineAnnealingLR scheduler adjusts the learning rate during model training to help the model learn better over time.

Data Flow - 5 Stages

1Data Loading

1000 rows x 20 features→Load dataset with 20 features per sample→1000 rows x 20 features

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

↓

2Preprocessing

1000 rows x 20 features→Normalize features to zero mean and unit variance→1000 rows x 20 features

[[-0.1, 0.3, ..., -0.2], [0.0, -0.5, ..., 1.1], ...]

↓

3Model Training

Batch of 32 rows x 20 features→Train neural network with optimizer and CosineAnnealingLR scheduler→Model parameters updated

Batch input tensor shape: (32, 20)

↓

4Learning Rate Scheduling

Initial learning rate = 0.1→Adjust learning rate using CosineAnnealingLR each epoch→Learning rate changes per epoch

Epoch 1 LR=0.1, Epoch 10 LR=0.0, Epoch 20 LR=0.1 (cosine cycle)

↓

5Model Evaluation

Validation set 200 rows x 20 features→Evaluate model accuracy→Accuracy metric

Accuracy = 0.85

Training Trace - Epoch by Epoch

Loss
1.0 |*
0.8 | *
0.6 |  *
0.4 |   *
0.2 |    *
0.0 +---------
     1 5 10 15 20 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Starting training with initial learning rate 0.1
5	0.45	0.75	Loss decreasing, accuracy improving, learning rate reducing
10	0.30	0.82	Learning rate near minimum, model converging
15	0.28	0.84	Learning rate increasing again due to cosine cycle
20	0.25	0.86	End of cosine cycle, learning rate back to initial

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

What happens to the learning rate at the middle of the cosine annealing cycle?

A It reaches its minimum value

B It reaches its maximum value

C It stays constant

D It becomes negative

Key Insight

CosineAnnealingLR helps the model by smoothly lowering and then raising the learning rate in cycles, which can improve training stability and final accuracy.