Model parameters inspection in PyTorch - Model Pipeline Trace

Model Pipeline - Model parameters inspection

This pipeline shows how a simple neural network model is built and how its parameters (weights and biases) are inspected to understand the model's structure and values.

Data Flow - 2 Stages

1Input data

100 samples x 10 features→Raw input features for the model→100 samples x 10 features

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

↓

2Model initialization

N/A→Create a neural network with 1 hidden layer (10 inputs -> 5 hidden units -> 1 output)→Model with parameters: weights and biases

Layer1 weights shape: (5, 10), Layer1 biases shape: (5), Layer2 weights shape: (1, 5), Layer2 biases shape: (1)

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.693	0.50	Initial loss is high, accuracy is at chance level
2	0.580	0.70	Loss decreased, accuracy improved
3	0.450	0.80	Model is learning, loss continues to drop
4	0.350	0.85	Good progress, accuracy increasing
5	0.280	0.90	Loss low, accuracy high - model converging

Prediction Trace - 3 Layers

Layer 1: Input layer

Layer 2: Hidden layer (Linear + ReLU)

Layer 3: Output layer (Linear + Sigmoid)

Model Quiz - 3 Questions

What shape do the weights of the first layer have?

A (1, 5)

B (5, 10)

C (10, 5)

D (5, 1)

Key Insight

Inspecting model parameters helps us understand the size and structure of the model. Observing weights and biases shapes confirms the model architecture. Tracking training loss and accuracy shows if the model is learning well. Activation functions like ReLU shape the data flow by removing negative values, and sigmoid outputs probabilities for predictions.