Loading and inference in TensorFlow - Model Pipeline Trace

Model Pipeline - Loading and inference

This pipeline shows how a saved TensorFlow model is loaded and used to make predictions on new data. It starts with input data, loads the trained model, and outputs predictions.

Data Flow - 3 Stages

1Input Data

10 rows x 5 columns→Raw new data samples for prediction→10 rows x 5 columns

[[0.5, 1.2, 3.3, 0.0, 2.1], [1.1, 0.4, 2.2, 1.0, 0.9], ...]

↓

2Load Model

N/A→Load saved TensorFlow model from disk→Model object ready for inference

Loaded model with input shape (None, 5) and output shape (None, 3)

↓

3Model Prediction

10 rows x 5 columns→Model processes input data to predict output classes→10 rows x 3 columns

[[0.1, 0.7, 0.2], [0.8, 0.1, 0.1], ...]

Training Trace - Epoch by Epoch

Loss
1.0 |****
0.8 |****
0.6 |****
0.4 |****
0.2 |****
0.0 +----
      1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning with moderate accuracy
2	0.60	0.75	Loss decreases and accuracy improves
3	0.45	0.82	Model continues to improve
4	0.35	0.88	Good convergence observed
5	0.30	0.90	Training stabilizes with high accuracy

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Dense Layer with ReLU

Layer 3: Output Layer with Softmax

Layer 4: Prediction

Model Quiz - 3 Questions

What shape does the input data have when fed into the model?

A 10 rows x 3 columns

B 5 rows x 10 columns

C 10 rows x 5 columns

D 3 rows x 5 columns

Key Insight

Loading a saved model allows us to reuse trained knowledge to make predictions on new data quickly. The softmax output helps interpret model results as probabilities, making it easier to choose the best prediction.