Feature map visualization in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the PyTorch library.

PyTorch

import [1]

Drag options to blanks, or click blank then click option'

Atorch

Btensorflow

Cnumpy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing TensorFlow instead of PyTorch.

Importing numpy or matplotlib which are not PyTorch.

PyTorch is imported using import torch.

2fill in blank

medium

Complete the code to create a simple convolutional layer in PyTorch.

PyTorch

conv_layer = torch.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=[1])

Drag options to blanks, or click blank then click option'

A7

B3

C1

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using kernel size 1 which is too small for feature extraction.

Using kernel size 3 or 7 which are valid but not the intended answer.

The kernel size is set to 5 to define the filter size in the convolutional layer.

3fill in blank

hard

Fix the error in the code to get the feature maps from the convolutional layer output.

PyTorch

feature_maps = output.[1]()

Drag options to blanks, or click blank then click option'

Adetach

Bnumpy

Ctolist

Dcpu

Attempts:

3 left

💡 Hint

Common Mistakes

Using numpy() directly without detaching causes errors.

Using tolist() which is not suitable for tensor operations.

Use detach() to get a tensor that is not tracked for gradients, which is needed before converting to numpy or visualizing.

4fill in blank

hard

Fill both blanks to convert the feature maps to a NumPy array and select the first channel for visualization.

PyTorch

feature_maps_np = feature_maps.[1]().[2][0]

Drag options to blanks, or click blank then click option'

Acpu

Bnumpy

Cdetach

Dtolist

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to convert tensor on GPU directly to NumPy causes errors.

Using tolist() instead of numpy().

First move the tensor to CPU with cpu(), then convert to NumPy array with numpy(). Selecting [0] gets the first channel.

5fill in blank

hard

Fill all three blanks to plot the first feature map using matplotlib.

PyTorch

import matplotlib.pyplot as plt
plt.imshow(feature_maps_np[1][2]) 
plt.[3]()

Drag options to blanks, or click blank then click option'

A[0]

B.squeeze()

Cshow

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot() instead of show() to display the image.

Not squeezing the tensor before plotting causes shape errors.

Use squeeze() to remove extra dimensions, then select the first map with [0], and finally call show() to display the image.

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