Prediction and evaluation in TensorFlow - Model Pipeline Trace

Model Pipeline - Prediction and evaluation

This pipeline shows how a trained TensorFlow model makes predictions on new data and evaluates its performance using accuracy and loss metrics.

Data Flow - 3 Stages

1Input Data

1000 rows x 20 columns→Raw feature data for prediction→1000 rows x 20 columns

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

↓

2Model Prediction

1000 rows x 20 columns→Model processes input to produce class probabilities→1000 rows x 3 columns

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

↓

3Evaluation

1000 rows x 3 columns (predictions), 1000 rows x 1 column (true labels)→Calculate loss and accuracy comparing predictions to true labels→Scalar loss value, scalar accuracy value

Loss: 0.35, Accuracy: 0.87

Training Trace - Epoch by Epoch

Loss
1.2 |************
1.0 |********
0.8 |******
0.6 |****
0.4 |**
0.2 |
    +----------------
     1  2  3  4  5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts with high loss and low accuracy.
2	0.85	0.65	Loss decreases and accuracy improves as model learns.
3	0.6	0.78	Model continues to improve with more training.
4	0.45	0.85	Loss lowers further and accuracy approaches good performance.
5	0.35	0.9	Training converges with low loss and high accuracy.

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layers (Dense + ReLU)

Layer 3: Output Layer (Dense + Softmax)

Layer 4: Prediction

Model Quiz - 3 Questions

What does the softmax layer output represent in the prediction process?

A Binary true/false labels

B Probabilities of each class summing to 1

C Raw scores without normalization

D Loss values for each class

Key Insight

Prediction and evaluation show how the model uses learned patterns to assign probabilities to classes and how metrics like loss and accuracy measure its performance on new data.