Accuracy and loss monitoring in TensorFlow - Model Pipeline Trace

Model Pipeline - Accuracy and loss monitoring

This pipeline shows how a simple neural network learns to classify handwritten digits. It tracks accuracy and loss during training to see how well the model improves over time.

Data Flow - 5 Stages

1Load Data

70000 rows x 28 x 28 pixels→Load MNIST handwritten digits dataset→70000 rows x 28 x 28 pixels

Image of digit '5' represented as 28x28 grayscale pixels

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2Preprocessing

70000 rows x 28 x 28 pixels→Normalize pixel values from 0-255 to 0-1→70000 rows x 28 x 28 pixels

Pixel value 150 becomes 0.588

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3Train/Test Split

70000 rows x 28 x 28 pixels→Split dataset into training and testing sets→60000 rows x 28 x 28 pixels (train), 10000 rows x 28 x 28 pixels (test)

Training set contains digit images for learning

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4Flatten Images

60000 rows x 28 x 28 pixels→Convert 2D images to 1D vectors→60000 rows x 784 columns

28x28 image becomes a 784-length vector

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5Model Training

60000 rows x 784 columns→Train neural network with 1 hidden layer→Model weights updated

Weights adjust to better classify digits

Training Trace - Epoch by Epoch

Loss: 0.45 |****      |
Loss: 0.30 |*******   |
Loss: 0.22 |********* |
Loss: 0.18 |**********|
Loss: 0.15 |**********|

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.85	Model starts learning, accuracy is moderate
2	0.30	0.91	Loss decreases, accuracy improves
3	0.22	0.94	Model continues to improve
4	0.18	0.95	Loss decreases steadily, accuracy high
5	0.15	0.96	Training converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU activation)

Layer 3: Output Layer (Softmax)

Model Quiz - 3 Questions

What does a decreasing loss during training indicate?

A The model is learning and improving

B The model is forgetting data

C The data is getting corrupted

D The training stopped

Key Insight

Monitoring accuracy and loss during training helps us understand if the model is learning well. Decreasing loss and increasing accuracy show the model improves its predictions over time.