Feature map visualization in TensorFlow - Model Pipeline Trace

Model Pipeline - Feature map visualization

This pipeline shows how an image passes through a convolutional neural network (CNN) and how feature maps are created and visualized. Feature maps help us see what the model learns at each layer.

Data Flow - 5 Stages

1Input Image

1 image x 64 height x 64 width x 3 channels→Load and normalize image pixels to range 0-1→1 image x 64 height x 64 width x 3 channels

Image of a cat with pixel values scaled between 0 and 1

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2Convolution Layer 1

1 image x 64 x 64 x 3→Apply 32 filters of size 3x3 with ReLU activation→1 image x 62 height x 62 width x 32 channels

Feature maps highlighting edges and simple shapes

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3Max Pooling Layer 1

1 image x 62 x 62 x 32→Downsample by 2x2 max pooling→1 image x 31 height x 31 width x 32 channels

Reduced size feature maps focusing on strongest features

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4Convolution Layer 2

1 image x 31 x 31 x 32→Apply 64 filters of size 3x3 with ReLU activation→1 image x 29 height x 29 width x 64 channels

Feature maps capturing more complex patterns

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5Feature Map Extraction

1 image x 29 x 29 x 64→Select feature maps from convolution layer 2 for visualization→64 feature maps of size 29 x 29

Visual grids showing activations for each filter

Training Trace - Epoch by Epoch

Loss
1.2 |*****
0.9 |****
0.7 |***
0.5 |**
0.4 |*

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning basic features
2	0.9	0.60	Loss decreases, accuracy improves as features get clearer
3	0.7	0.72	Model captures more complex patterns
4	0.5	0.82	Good convergence, feature maps become more distinct
5	0.4	0.88	Model well trained, feature maps highlight important image parts

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Convolution Layer 1

Layer 3: Max Pooling Layer 1

Layer 4: Convolution Layer 2

Layer 5: Feature Map Visualization

Model Quiz - 3 Questions

What does a feature map from a convolution layer represent?

A A filtered image showing detected patterns

B The original input image

C The final prediction label

D Random noise added to data

Key Insight

Feature maps show how CNN layers detect simple to complex patterns step-by-step. Visualizing them helps understand what the model learns and why accuracy improves as training progresses.