Model Pipeline - CNN architecture for image classification

This pipeline uses a Convolutional Neural Network (CNN) to classify images into categories. It starts with raw images, processes them through layers that detect patterns, trains the model to improve accuracy, and finally predicts the class of new images.

Data Flow - 13 Stages

1Input Images

1000 images x 3 channels x 32 height x 32 width→Raw image data loaded from dataset→1000 images x 3 channels x 32 height x 32 width

An image of a cat represented as a 3D array of pixel colors

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2Normalization

1000 images x 3 channels x 32 height x 32 width→Scale pixel values from 0-255 to 0-1→1000 images x 3 channels x 32 height x 32 width

Pixel value 128 becomes 0.502

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3Convolutional Layer 1

1000 images x 3 channels x 32 height x 32 width→Apply 16 filters of size 3x3 with stride 1 and padding 1→1000 images x 16 channels x 32 height x 32 width

Detect edges and simple shapes in images

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4ReLU Activation

1000 images x 16 channels x 32 height x 32 width→Apply ReLU to add non-linearity→1000 images x 16 channels x 32 height x 32 width

Negative values become 0, positive stay same

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5Max Pooling

1000 images x 16 channels x 32 height x 32 width→Downsample by taking max over 2x2 regions with stride 2→1000 images x 16 channels x 16 height x 16 width

Reduce image size while keeping important features

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6Convolutional Layer 2

1000 images x 16 channels x 16 height x 16 width→Apply 32 filters of size 3x3 with stride 1 and padding 1→1000 images x 32 channels x 16 height x 16 width

Detect more complex patterns

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7ReLU Activation

1000 images x 32 channels x 16 height x 16 width→Apply ReLU→1000 images x 32 channels x 16 height x 16 width

Keep positive activations

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8Max Pooling

1000 images x 32 channels x 16 height x 16 width→Downsample by 2x2 max pooling→1000 images x 32 channels x 8 height x 8 width

Further reduce spatial size

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9Flatten

1000 images x 32 channels x 8 height x 8 width→Convert 3D feature maps to 1D vector→1000 images x 2048 features

32*8*8 = 2048 features per image

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10Fully Connected Layer

1000 images x 2048 features→Linear layer to 64 neurons→1000 images x 64 features

Combine features to learn complex relations

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11ReLU Activation

1000 images x 64 features→Apply ReLU→1000 images x 64 features

Add non-linearity

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12Output Layer

1000 images x 64 features→Linear layer to 10 classes→1000 images x 10 classes

Predict scores for 10 categories

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13Softmax

1000 images x 10 classes→Convert scores to probabilities→1000 images x 10 classes

Probabilities sum to 1 for each image

Training Trace - Epoch by Epoch

Loss
1.9 |*        
1.6 | **      
1.3 |  ***    
1.0 |    **** 
0.7 |      ***
0.4 |       **
    +---------
     1 2 3 4 5 6 7 8 9 10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.85	0.35	Model starts learning basic patterns
2	1.25	0.55	Loss decreases, accuracy improves
3	0.95	0.68	Model captures more features
4	0.75	0.75	Good convergence, accuracy rising
5	0.60	0.81	Model stabilizes with better accuracy
6	0.52	0.85	Further improvement, loss lowers
7	0.47	0.87	Training converging well
8	0.43	0.89	High accuracy, low loss
9	0.40	0.90	Model near optimal performance
10	0.38	0.91	Training complete with good results

Prediction Trace - 8 Layers

Layer 1: Input Image

Layer 2: Conv Layer 1 + ReLU

Layer 3: Max Pooling

Layer 4: Conv Layer 2 + ReLU

Layer 5: Max Pooling

Layer 6: Flatten

Layer 7: Fully Connected + ReLU

Layer 8: Output Layer + Softmax

Model Quiz - 3 Questions

Test your understanding

What is the purpose of the max pooling layers in this CNN?

ATo reduce the spatial size and keep important features

BTo increase the number of channels

CTo normalize pixel values

DTo convert images to grayscale

Key Insight

This CNN model learns to recognize image patterns by gradually extracting features through convolution and pooling layers, then classifies images by combining these features in fully connected layers. Training shows steady improvement in accuracy as loss decreases, demonstrating effective learning.