Optimizers (SGD, Adam, RMSprop) in TensorFlow - Model Pipeline Trace

Model Pipeline - Optimizers (SGD, Adam, RMSprop)

This pipeline shows how different optimizers help a model learn better by adjusting weights during training. We compare three popular optimizers: SGD, Adam, and RMSprop, to see how they improve the model's accuracy and reduce loss.

Data Flow - 5 Stages

1Data Input

1000 rows x 10 columns→Load dataset with 10 features per example→1000 rows x 10 columns

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

↓

2Preprocessing

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

[[0.05, 0.12, ..., 0.03], [0.01, 0.04, ..., 0.09], ...]

↓

3Feature Engineering

1000 rows x 10 columns→No additional features added→1000 rows x 10 columns

[[0.05, 0.12, ..., 0.03], [0.01, 0.04, ..., 0.09], ...]

↓

4Model Training

1000 rows x 10 columns→Train model using optimizer (SGD, Adam, RMSprop)→Model weights updated after each epoch

Weights updated to reduce loss

↓

5Evaluation

200 rows x 10 columns (test set)→Calculate loss and accuracy on test data→Loss and accuracy values

Loss=0.15, Accuracy=0.92

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |**  
0.3 |*   
0.2 |*   
0.1 |    
    +-----
    1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Initial training with high loss and moderate accuracy
2	0.45	0.75	Loss decreased, accuracy improved
3	0.30	0.85	Model learning well, loss dropping
4	0.22	0.90	Good convergence, accuracy rising
5	0.15	0.93	Loss low, accuracy high, training stable

Prediction Trace - 3 Layers

Layer 1: Input Layer

Layer 2: Dense Layer with ReLU

Layer 3: Output Layer with Softmax

Model Quiz - 3 Questions

Which optimizer adapts the learning rate during training to improve convergence?

A SGD

B Adam

C Simple Gradient Descent

D None of the above

Key Insight

Optimizers like Adam and RMSprop adjust learning rates during training, helping the model reduce loss faster and improve accuracy compared to simple SGD. Activation functions like ReLU and softmax shape the model's outputs to be meaningful for classification.