Feature map visualization in PyTorch - Model Pipeline Trace

Model Pipeline - Feature map visualization

This pipeline shows how an image passes through a simple convolutional neural network. It highlights how feature maps are created and transformed at each convolution layer, helping us understand what the model 'sees' inside the image.

Data Flow - 5 Stages

1Input Image

1 image x 3 channels x 32 height x 32 width→Raw image loaded and normalized→1 image x 3 channels x 32 height x 32 width

A 32x32 color image with 3 color channels (RGB)

↓

2First Convolution Layer

1 x 3 x 32 x 32→Apply 6 filters of size 5x5 with stride 1, padding 0→1 x 6 x 28 x 28

6 feature maps showing edges and simple patterns

↓

3ReLU Activation

1 x 6 x 28 x 28→Apply ReLU to keep positive values, zero out negatives→1 x 6 x 28 x 28

Same 6 feature maps with negative values replaced by zero

↓

4Second Convolution Layer

1 x 6 x 28 x 28→Apply 16 filters of size 5x5 with stride 1, padding 0→1 x 16 x 24 x 24

16 feature maps capturing more complex patterns

↓

5ReLU Activation

1 x 16 x 24 x 24→Apply ReLU activation→1 x 16 x 24 x 24

16 feature maps with negative values zeroed

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, loss is high, accuracy low
2	0.9	0.60	Loss decreases, accuracy improves as model learns features
3	0.7	0.72	Better feature extraction reflected in improved accuracy
4	0.5	0.80	Model converging, loss dropping steadily
5	0.4	0.85	Good feature maps help model classify better

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: First Convolution Layer

Layer 3: ReLU Activation

Layer 4: Second Convolution Layer

Layer 5: ReLU Activation

Model Quiz - 3 Questions

What does the first convolution layer mainly detect in the input image?

A Noise in the image

B Final classification labels

C Edges and simple shapes

D Color histograms

Key Insight

Feature maps show how the model transforms raw images into meaningful patterns step-by-step. Visualizing these maps helps us understand what the model learns and why accuracy improves during training.

Practice

1. What is a feature map in the context of convolutional neural networks (CNNs)?

easy

A. The loss value during training

B. The input image to the CNN

C. The final prediction of the model

D. The output of a convolutional layer showing detected patterns

Solution

1. Step 1: Understand CNN layer outputs

   Convolutional layers process input images and produce outputs called feature maps that highlight detected features.

2. Step 2: Identify feature map role

   Feature maps represent learned patterns like edges or textures, not inputs or final outputs.

3. Final Answer:

   The output of a convolutional layer showing detected patterns -> Option D

4. Quick Check:

   Feature map = convolution output [OK]

Hint: Feature maps are outputs of conv layers, not inputs or predictions [OK]

Common Mistakes:

• Confusing feature maps with input images
• Thinking feature maps are final model outputs
• Mixing feature maps with loss values

2. Which PyTorch code snippet correctly extracts feature maps from a convolutional layer named conv1 given an input tensor x?

easy

A. feature_maps = x.conv1()

B. feature_maps = conv1(x)

C. feature_maps = conv1.forward()

D. feature_maps = conv1.output(x)

Solution

1. Step 1: Understand PyTorch layer call

   In PyTorch, calling a layer like a function with input tensor returns its output (feature maps).

2. Step 2: Check syntax correctness

   Using conv1(x) is correct; x.conv1() or conv1.output(x) are invalid syntax.

3. Final Answer:

   feature_maps = conv1(x) -> Option B

4. Quick Check:

   Call layer as function = correct [OK]

Hint: Call conv layer like a function with input tensor [OK]

Common Mistakes:

• Trying to call layer as method on input tensor
• Using non-existent methods like .output()
• Calling forward() directly instead of layer call

3. Given the following PyTorch code, what is the shape of feature_maps?

import torch
import torch.nn as nn
conv = nn.Conv2d(in_channels=3, out_channels=5, kernel_size=3, padding=1)
x = torch.randn(1, 3, 32, 32)
feature_maps = conv(x)

medium

A. [1, 3, 32, 32]

B. [1, 5, 30, 30]

C. [1, 5, 32, 32]

D. [3, 5, 32, 32]

Solution

1. Step 1: Analyze conv layer parameters

   Input has shape [1, 3, 32, 32]. Conv2d has 5 output channels, kernel size 3, padding 1.

2. Step 2: Calculate output spatial size

   Padding 1 keeps spatial size same: 32x32. Output channels = 5, batch size = 1.

3. Final Answer:

   [1, 5, 32, 32] -> Option C

4. Quick Check:

   Output shape = [batch, out_channels, height, width] [OK]

Hint: Padding keeps size; output channels define depth [OK]

Common Mistakes:

• Ignoring padding effect on output size
• Confusing input channels with output channels
• Mixing batch size with channel dimension

4. You try to visualize feature maps using this code but get an error:

import matplotlib.pyplot as plt
feature_maps = conv(x)
plt.imshow(feature_maps[0])
plt.show()

What is the likely cause of the error?

medium

A. feature_maps[0] has multiple channels, plt.imshow expects 2D or 3D image

B. conv(x) returns a scalar, not a tensor

C. plt.imshow cannot display tensors

D. x is not defined before conv(x)

Solution

1. Step 1: Understand feature_maps shape

   feature_maps[0] is shape [channels, height, width], multiple channels not a single image.

2. Step 2: plt.imshow expects 2D or 3D image

   plt.imshow needs 2D grayscale or 3D RGB image, but feature_maps[0] has multiple channels causing error.

3. Final Answer:

   feature_maps[0] has multiple channels, plt.imshow expects 2D or 3D image -> Option A

4. Quick Check:

   Multi-channel tensor ≠ single image [OK]

Hint: Plot one channel slice, not full multi-channel tensor [OK]

Common Mistakes:

• Trying to plot all channels at once with plt.imshow
• Assuming conv output is scalar
• Not checking input tensor existence

5. You want to visualize all feature maps from a convolutional layer conv for a single input image x. Which code correctly plots each channel as a separate grayscale image using matplotlib?

hard

A. feature_maps = conv(x)
for i in range(feature_maps.shape[1]):
plt.subplot(1, feature_maps.shape[1], i+1)
plt.imshow(feature_maps[0, i].detach().cpu(), cmap='gray')
plt.show()

B. feature_maps = conv(x)
plt.imshow(feature_maps.detach().cpu(), cmap='gray')
plt.show()

C. feature_maps = conv(x)
for i in range(feature_maps.shape[0]):
plt.imshow(feature_maps[i].detach().cpu(), cmap='gray')
plt.show()

D. feature_maps = conv(x)
plt.imshow(feature_maps[0].detach().cpu())
plt.show()

Solution

1. Step 1: Extract feature maps and iterate channels

   feature_maps shape is [batch, channels, height, width]. We select batch 0 and loop over channels.

2. Step 2: Plot each channel as grayscale image

   Use plt.subplot to arrange images, plt.imshow with cmap='gray' to show each channel properly.

3. Final Answer:

   feature_maps = conv(x) for i in range(feature_maps.shape[1]): plt.subplot(1, feature_maps.shape[1], i+1) plt.imshow(feature_maps[0, i].detach().cpu(), cmap='gray') plt.show() -> Option A

4. Quick Check:

   Loop channels, plot each with cmap='gray' [OK]

Hint: Loop channels, plot each with grayscale colormap [OK]

Common Mistakes:

• Plotting entire tensor at once
• Not detaching or moving tensor to CPU
• Ignoring batch dimension