nn.Conv2d layers in PyTorch - Interactive Code Practice

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 = nn.Conv2d([1], 16, kernel_size=3)

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

A1

B3

C16

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Using the number of output channels as the first argument.

Confusing kernel size with input channels.

The first argument to nn.Conv2d is the number of input channels. Here, it should be 3.

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'

Ainput_tensor

Bconv_layer

Coutput

Dnn.Conv2d

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the layer itself instead of the input tensor.

Using the output variable before it is defined.

You apply the convolutional layer to the input tensor by calling it like a function with the input tensor as argument.

3fill in blank

hard

Fix the error in the code by completing the missing argument for padding.

PyTorch

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

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A1

B0

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using padding 0 causes output size to shrink.

Using padding larger than needed causes unexpected output size.

Padding of 1 keeps the output size the same when using a 3x3 kernel.

4fill in blank

hard

Fill both blanks to create a convolutional layer with stride 2 and kernel size 5.

PyTorch

conv_layer = nn.Conv2d(3, 32, kernel_size=[1], stride=[2])

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A5

B2

C3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing stride with padding.

Using kernel size 3 instead of 5.

The kernel size is 5 and stride is 2 to downsample the input.

5fill in blank

hard

Fill all three blanks to create a Conv2d layer with 1 input channel, 10 output channels, kernel size 3, padding 1.

PyTorch

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

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

A1

B10

C3

D16

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up input and output channels.

Forgetting to set padding to 1 for same output size.

The layer has 1 input channel, 10 output channels, and kernel size 3 with padding 1.

Practice

1. What does the nn.Conv2d layer in PyTorch primarily do?

easy

A. It increases the image size by adding pixels.

B. It slides filters over images to find patterns.

C. It converts images to grayscale.

D. It sorts images by color intensity.

Solution

1. Step 1: Understand the role of convolution layers

   Convolution layers slide small filters over input images to detect features like edges or textures.

2. Step 2: Match the function to the options

   Only It slides filters over images to find patterns. correctly describes this sliding filter action, while others describe unrelated image operations.

3. Final Answer:

   It slides filters over images to find patterns. -> Option B

4. Quick Check:

   Convolution = sliding filters [OK]

Hint: Conv2d = sliding filters over images to find features [OK]

Common Mistakes:

• Thinking Conv2d changes image size by adding pixels
• Confusing Conv2d with image color adjustments
• Assuming Conv2d sorts or rearranges pixels

2. Which of the following is the correct way to create a Conv2d layer with 3 input channels, 16 output channels, and a 3x3 kernel in PyTorch?

easy

A. nn.Conv2d(3, 16, kernel_size=3)

B. nn.Conv2d(16, 3, kernel_size=3)

C. nn.Conv2d(3, 16, kernel=3)

D. nn.Conv2d(input=3, output=16, size=3)

Solution

1. Step 1: Recall Conv2d constructor parameters

   The correct order is nn.Conv2d(in_channels, out_channels, kernel_size).

2. Step 2: Check each option

   nn.Conv2d(3, 16, kernel_size=3) matches the correct parameter order and uses the correct keyword for kernel size. The other options have wrong parameter order or incorrect keywords.

3. Final Answer:

   nn.Conv2d(3, 16, kernel_size=3) -> Option A

4. Quick Check:

   Conv2d(in, out, kernel_size) = A [OK]

Hint: Remember Conv2d(in_channels, out_channels, kernel_size) [OK]

Common Mistakes:

• Swapping input and output channels
• Using wrong parameter names like 'kernel' instead of 'kernel_size'
• Passing parameters as keywords not supported by Conv2d

3. What will be the output shape of the following PyTorch Conv2d layer when applied to an input tensor of shape (1, 3, 32, 32)?

conv = nn.Conv2d(3, 6, kernel_size=5)
output = conv(torch.randn(1, 3, 32, 32))
print(output.shape)

medium

A. torch.Size([1, 3, 28, 28])

B. torch.Size([1, 6, 32, 32])

C. torch.Size([6, 3, 28, 28])

D. torch.Size([1, 6, 28, 28])

Solution

1. Step 1: Calculate output spatial size

   Output size = (Input size - Kernel size + 1) = (32 - 5 + 1) = 28 for both height and width.

2. Step 2: Determine output channels and batch size

   Output channels = 6, batch size = 1, so output shape is (1, 6, 28, 28).

3. Final Answer:

   torch.Size([1, 6, 28, 28]) -> Option D

4. Quick Check:

   Output shape = (batch, out_channels, 28, 28) [OK]

Hint: Output size = input - kernel + 1 if stride=1, padding=0 [OK]

Common Mistakes:

• Assuming output size equals input size without padding
• Mixing up input and output channels in shape
• Forgetting batch size dimension

4. Identify the error in this Conv2d layer definition:

conv = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=3)
output = conv(torch.randn(1, 3, 28, 28))
print(output.shape)

medium

A. Stride cannot be 2 in Conv2d.

B. Input tensor shape is incorrect for 3 input channels.

C. Padding is too large causing output size to increase unexpectedly.

D. Kernel size must be an odd number.

Solution

1. Step 1: Calculate output size with given parameters

   Output size formula: floor((Input + 2*padding - kernel_size)/stride) + 1 = floor((28 + 6 - 3)/2) + 1 = floor(31/2) + 1 = 15 + 1 = 16.

2. Step 2: Understand padding effect

   Padding=3 is large for kernel=3, causing output spatial size to increase unexpectedly, which is unusual and may cause unexpected behavior.

3. Final Answer:

   Padding is too large causing output size to increase unexpectedly. -> Option C

4. Quick Check:

   Large padding inflates output size [OK]

Hint: Check padding size relative to kernel size for output shape [OK]

Common Mistakes:

• Thinking stride=2 is invalid
• Assuming input shape is wrong for 3 channels
• Believing kernel size must be odd always

5. You want to design a Conv2d layer that keeps the input image size (28x28) unchanged after convolution with a 5x5 kernel and stride 1. Which padding value should you use?

hard

A. Padding = 2

B. Padding = 1

C. Padding = 0

D. Padding = 3

Solution

1. Step 1: Use output size formula for Conv2d

   Output size = floor((Input + 2*padding - kernel_size)/stride) + 1. We want output = input = 28, stride=1, kernel=5.

2. Step 2: Solve for padding

   28 = (28 + 2*padding - 5) + 1 -> 28 = 24 + 2*padding -> 2*padding = 4 -> padding = 2.

3. Final Answer:

   Padding = 2 -> Option A

4. Quick Check:

   Padding 2 keeps size with 5x5 kernel [OK]

Hint: Padding = (kernel_size - 1) / 2 for same size [OK]

Common Mistakes:

• Using zero padding and expecting same size
• Choosing padding less than 2 for 5x5 kernel
• Confusing stride effect with padding