Optimizers (SGD, Adam) in PyTorch - Model Pipeline Trace

Model Pipeline - Optimizers (SGD, Adam)

This pipeline shows how two popular optimizers, SGD and Adam, help a simple neural network learn from data by adjusting its weights to reduce errors.

Data Flow - 5 Stages

1Data Input

1000 rows x 10 columns→Raw dataset with 10 features per sample→1000 rows x 10 columns

[0.5, 1.2, 0.3, ..., 0.7]

↓

2Preprocessing

1000 rows x 10 columns→Normalize features to range 0-1→1000 rows x 10 columns

[0.25, 0.6, 0.15, ..., 0.35]

↓

3Model Input Layer

1000 rows x 10 columns→Feed features into neural network input→1000 rows x 10 neurons

[0.25, 0.6, 0.15, ..., 0.35]

↓

4Hidden Layer

1000 rows x 10 neurons→Apply linear transformation and ReLU activation→1000 rows x 5 neurons

[0.0, 0.8, 0.3, 0.0, 0.5]

↓

5Output Layer

1000 rows x 5 neurons→Apply linear transformation to get predictions→1000 rows x 1 column

[0.7, 0.2, 0.9, ..., 0.4]

Training Trace - Epoch by Epoch

Loss
1.0 | *
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	0.55	Initial training with high loss and low accuracy
2	0.65	0.68	Loss decreased, accuracy improved
3	0.50	0.75	Model learning well, loss dropping steadily
4	0.40	0.82	Good convergence, accuracy increasing
5	0.35	0.85	Training stabilizing with low loss and high accuracy

Prediction Trace - 3 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (Linear + ReLU)

Layer 3: Output Layer (Linear)

Model Quiz - 3 Questions

What is the main role of the optimizer in training?

A Adjust model weights to reduce error

B Increase the size of the dataset

C Change the input data format

D Visualize the training progress

Key Insight

Optimizers like SGD and Adam guide the model to learn by adjusting weights to reduce errors. Adam often converges faster by adapting learning rates, helping the model improve accuracy steadily over training.