Multi-class classification model in TensorFlow - Model Pipeline Trace

Model Pipeline - Multi-class classification model

This pipeline trains a model to recognize and classify data into one of several categories. It learns from labeled examples and improves its guesses over time.

Data Flow - 5 Stages

1Raw Data Input

1000 rows x 20 columns→Collect raw features and labels for 3 classes→1000 rows x 20 columns

[[5.1, 3.5, ..., 0.2], label=2]

↓

2Data Preprocessing

1000 rows x 20 columns→Normalize features to range 0-1→1000 rows x 20 columns

[[0.51, 0.35, ..., 0.02], label=2]

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3Train/Test Split

1000 rows x 20 columns→Split data into 800 training and 200 testing rows→Train: 800 rows x 20 columns, Test: 200 rows x 20 columns

Train sample: [[0.51, 0.35, ..., 0.02], label=2]

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4Model Training

800 rows x 20 columns→Train neural network with 3 output classes→Trained model

Model learns weights to classify inputs

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5Model Evaluation

200 rows x 20 columns→Predict classes and compare with true labels→Accuracy and loss metrics

Accuracy: 0.85, Loss: 0.35

Training Trace - Epoch by Epoch

Loss
1.1 |****
0.9 |***
0.7 |**
0.5 |*
0.3 |
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.10	0.45	Model starts learning, accuracy low
2	0.85	0.60	Loss decreases, accuracy improves
3	0.65	0.72	Model learns important patterns
4	0.50	0.80	Good progress, accuracy rising
5	0.40	0.85	Model converging well

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU)

Layer 3: Output Layer (Softmax)

Layer 4: Prediction

Model Quiz - 3 Questions

What does the softmax layer output represent?

A Binary yes/no for each class

B Raw scores that can be negative

C Probabilities for each class that sum to 1

D Normalized input features

Key Insight

This visualization shows how a multi-class model learns to assign probabilities to different classes and improves accuracy by reducing loss over training epochs.