Why CNNs detect spatial patterns in PyTorch - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 2D convolutional layer with 3 input channels and 16 output channels.

PyTorch

conv_layer = torch.nn.Conv2d([1], 16, kernel_size=3, stride=1, padding=1)

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

A1

B16

C32

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using output channels instead of input channels

Confusing kernel size with channels

The first argument to Conv2d is the number of input channels. For example, an RGB image has 3 channels.

2fill in blank

medium

Complete the code to apply the convolutional layer to the input tensor.

PyTorch

output = conv_layer([1])

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

Aconv_layer

Binput_tensor

Coutput

Dtorch.nn

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the layer itself instead of the input

Passing the output variable before assignment

The convolutional layer is applied to the input tensor to produce the output feature map.

3fill in blank

hard

Fix the error in the code to correctly initialize the convolutional layer with a 5x5 kernel.

PyTorch

conv_layer = torch.nn.Conv2d(3, 16, kernel_size=[1], stride=1, padding=2)

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

A5

B7

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using kernel size 3 instead of 5

Incorrect padding for kernel size

The kernel size should be 5 to create a 5x5 filter. Padding of 2 keeps the output size same as input.

4fill in blank

hard

Fill both blanks to create a feature map by applying a convolution and then a ReLU activation.

PyTorch

conv_layer = torch.nn.Conv2d(3, 8, kernel_size=3, padding=1)
output = conv_layer([1])
activated_output = torch.nn.functional.[2](output)

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

Ainput_tensor

Bsigmoid

Crelu

Doutput_tensor

Attempts:

3 left

💡 Hint

Common Mistakes

Using output_tensor instead of input_tensor

Using sigmoid instead of relu

The input tensor is passed to the convolutional layer, then ReLU activation is applied to introduce non-linearity.

5fill in blank

hard

Fill all three blanks to create a simple CNN block: convolution, activation, and max pooling.

PyTorch

conv = torch.nn.Conv2d(1, [1], kernel_size=3, padding=1)
x = conv([2])
x = torch.nn.functional.[3](x)
x = torch.nn.functional.max_pool2d(x, 2)

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

A10

Binput_image

Crelu

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using too many output channels

Passing wrong variable as input

Using wrong activation function

The convolution outputs 5 channels, input_image is the input tensor, and ReLU activation is applied before max pooling.

Practice

1. Why do CNNs use small filters that slide over an image?

easy

A. To detect local spatial patterns like edges and textures

B. To reduce the image size drastically in one step

C. To convert images into text data

D. To randomly change pixel colors

Solution

1. Step 1: Understand the role of filters in CNNs

   Filters slide over small parts of the image to focus on local details like edges or shapes.

2. Step 2: Connect filter behavior to spatial pattern detection

   By scanning the image locally, filters learn to recognize important spatial features that help in tasks like image recognition.

3. Final Answer:

   To detect local spatial patterns like edges and textures -> Option A

4. Quick Check:

   Filters detect local patterns = A [OK]

Hint: Filters scan small areas to find edges and shapes [OK]

Common Mistakes:

• Thinking filters change image size drastically in one step
• Believing CNNs convert images to text directly
• Assuming filters randomly alter pixel colors

2. Which PyTorch code correctly creates a 2D convolutional layer with a 3x3 filter?

easy

A. torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3)

B. torch.nn.Conv1d(in_channels=1, out_channels=10, kernel_size=3)

C. torch.nn.Linear(in_features=3, out_features=10)

D. torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5)

Solution

1. Step 1: Identify the correct convolution layer type

   For images, 2D convolution (Conv2d) is used, not Conv1d or Linear layers.

2. Step 2: Check the kernel size matches 3x3

   kernel_size=3 means a 3x3 filter, so torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3) is correct; torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5) uses 5x5.

3. Final Answer:

   torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3) -> Option A

4. Quick Check:

   Conv2d with kernel_size=3 = D [OK]

Hint: Use Conv2d and kernel_size=3 for 3x3 filters [OK]

Common Mistakes:

• Using Conv1d instead of Conv2d for images
• Confusing Linear layers with convolution layers
• Setting wrong kernel size for the filter

3. Given this PyTorch code snippet, what is the output shape after the convolution?

import torch
conv = torch.nn.Conv2d(1, 1, kernel_size=3)
input = torch.randn(1, 1, 5, 5)
output = conv(input)
print(output.shape)

medium

A. torch.Size([1, 1, 5, 5])

B. torch.Size([1, 3, 3, 3])

C. torch.Size([1, 1, 7, 7])

D. torch.Size([1, 1, 3, 3])

Solution

1. Step 1: Understand convolution output size formula

   Output size = Input size - Kernel size + 1 (assuming stride=1, padding=0). Here, 5 - 3 + 1 = 3.

2. Step 2: Apply formula to each spatial dimension

   Both height and width become 3, so output shape is (1 batch, 1 channel, 3 height, 3 width).

3. Final Answer:

   torch.Size([1, 1, 3, 3]) -> Option D

4. Quick Check:

   Output size = 5-3+1 = 3 [OK]

Hint: Output size = input - kernel + 1 if no padding [OK]

Common Mistakes:

• Assuming output size equals input size without padding
• Confusing batch and channel dimensions
• Misapplying kernel size in output calculation

4. What is wrong with this PyTorch code for a convolutional layer?

conv = torch.nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3)
input = torch.randn(1, 1, 28, 28)
output = conv(input)
print(output.shape)

medium

A. Output channels must be less than input channels

B. Kernel size is too large for the input

C. Input channels do not match the layer's in_channels

D. Batch size must be greater than 1

Solution

1. Step 1: Check input and layer channel compatibility

   The layer expects 3 input channels, but input has only 1 channel, causing a mismatch error.

2. Step 2: Confirm other parameters are valid

   Kernel size 3 is valid for 28x28 input, output channels can be any positive number, batch size 1 is allowed.

3. Final Answer:

   Input channels do not match the layer's in_channels -> Option C

4. Quick Check:

   Input channels mismatch = A [OK]

Hint: Input channels must match Conv2d in_channels [OK]

Common Mistakes:

• Ignoring channel mismatch errors
• Thinking kernel size is invalid for input
• Believing batch size must be >1

5. How does using multiple convolutional layers help CNNs detect complex spatial patterns?

hard

A. Layers randomly shuffle pixels to create new patterns

B. Each layer learns higher-level features by combining simpler patterns from previous layers

C. Multiple layers reduce the image size to zero quickly

D. Each layer independently detects the same simple edges

Solution

1. Step 1: Understand feature hierarchy in CNNs

   Early layers detect simple features like edges; later layers combine these to form complex shapes and objects.

2. Step 2: Explain how multiple layers build complexity

   Stacking layers lets the network learn spatial patterns at increasing levels of abstraction, improving recognition.

3. Final Answer:

   Each layer learns higher-level features by combining simpler patterns from previous layers -> Option B

4. Quick Check:

   Layer stacking builds complex features = C [OK]

Hint: Layers build complexity by combining simpler features [OK]

Common Mistakes:

• Thinking layers just reduce image size quickly
• Believing layers shuffle pixels randomly
• Assuming all layers detect the same simple edges