Padding and stride in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output shape with SAME padding and stride 2

Consider a 2D convolution layer in TensorFlow with input shape (1, 28, 28, 3), kernel size 3x3, stride 2, and padding='SAME'. What is the output shape after applying this convolution?

TensorFlow

import tensorflow as tf
input_tensor = tf.random.uniform((1, 28, 28, 3))
conv_layer = tf.keras.layers.Conv2D(filters=5, kernel_size=3, strides=2, padding='SAME')
output = conv_layer(input_tensor)
print(output.shape)

A (1, 14, 14, 5)

B (1, 12, 12, 5)

C (1, 15, 15, 5)

D (1, 13, 13, 5)

💡 Hint

Padding='SAME' keeps output size roughly input size divided by stride.

With padding='SAME' and stride=2, output height and width are ceil(input_dim / stride). For 28 input, output is ceil(28/2)=14.

🧠 Conceptual

intermediate

Effect of stride on output size

In a convolutional neural network, if you increase the stride from 1 to 3 while keeping padding='VALID' and kernel size fixed, what happens to the output feature map size?

A Output size stays the same because kernel size is fixed.

B Output size increases because stride adds padding.

C Output size becomes zero because stride is too large.

D Output size decreases because stride skips more input positions.

💡 Hint

Think about how stride controls the step size when sliding the kernel.

Increasing stride makes the kernel jump more pixels, reducing the number of positions it fits, so output size decreases.

❓ Metrics

advanced

Calculate output size with padding='VALID' and stride 2

Given an input image of size 32x32, a convolutional layer with kernel size 5x5, stride 2, and padding='VALID', what is the output width and height?

A 16 x 16

B 15 x 15

C 14 x 14

D 13 x 13

💡 Hint

Use formula: output = floor((input - kernel + 1) / stride)

Output size = floor((32 - 5 + 1) / 2) = floor(28 / 2) = 14.

🔧 Debug

advanced

Identify the error in convolution output shape calculation

A user writes this code to compute output shape after a convolution: input_shape = (1, 64, 64, 3) kernel_size = 7 stride = 2 padding = 'VALID' output_height = (input_shape[1] - kernel_size) // stride output_width = (input_shape[2] - kernel_size) // stride print(f'Output shape: (1, {output_height}, {output_width}, 10)') What is wrong with this calculation?

A Padding='VALID' requires adding padding size to input

B The kernel size should be subtracted with +1: (input - kernel + 1)

C Output channels should be input channels minus kernel size

D The stride should be added, not divided

💡 Hint

Recall the formula for output size with VALID padding.

The formula for output size with VALID padding is floor((input - kernel + 1) / stride), missing the +1 causes wrong output size.

❓ Model Choice

expert

Choosing padding and stride for preserving spatial dimensions

You want to design a convolutional layer that keeps the input image size exactly the same after convolution. Which combination of padding and stride should you choose?

A padding='SAME' and stride=1

B padding='VALID' and stride=1

C padding='SAME' and stride=2

D padding='VALID' and stride=2

💡 Hint

Think about how padding and stride affect output size.

Padding='SAME' with stride=1 keeps output size equal to input size by adding zero padding as needed.

Practice

1. What does padding do in a convolutional layer in TensorFlow?

easy

A. Changes the filter size

B. Adjusts the learning rate

C. Controls the number of filters

D. Adds extra space around the input edges

Solution

1. Step 1: Understand padding role

   Padding adds pixels around the input image edges to control output size and preserve edge information.

2. Step 2: Compare options

   Only Adds extra space around the input edges correctly describes padding as adding space around input edges.

3. Final Answer:

   Adds extra space around the input edges -> Option D

4. Quick Check:

   Padding = Adds space around input edges [OK]

Hint: Padding adds border space around input images [OK]

Common Mistakes:

• Confusing padding with stride
• Thinking padding changes filter size
• Assuming padding controls learning rate

2. Which of the following is the correct way to specify stride of 2 in TensorFlow's Conv2D layer?

easy

A. strides=(2)

B. stride=(2)

C. strides=(2, 2)

D. stride=2

Solution

1. Step 1: Recall Conv2D stride syntax

   TensorFlow Conv2D expects strides as a tuple for height and width, e.g., (2, 2).

2. Step 2: Check options

   Only strides=(2, 2) uses the correct parameter name and tuple format.

3. Final Answer:

   strides=(2, 2) -> Option C

4. Quick Check:

   Stride syntax = strides=(2, 2) [OK]

Hint: Use tuple for strides in Conv2D: strides=(height, width) [OK]

Common Mistakes:

• Using 'stride' instead of 'strides'
• Passing a single integer instead of a tuple
• Omitting parentheses around stride values

3. Given an input of size 28x28, a filter size of 3x3, stride of 2, and 'valid' padding, what is the output size after Conv2D?

medium

A. 13x13

B. 14x14

C. 15x15

D. 12x12

Solution

1. Step 1: Calculate output size formula for 'valid' padding

   Output size = floor((Input - Filter) / Stride) + 1 = floor((28 - 3) / 2) + 1 = floor(25 / 2) + 1 = 12 + 1 = 13.

2. Step 2: Confirm output dimensions

   Output height and width are both 13, so output size is 13x13.

3. Final Answer:

   13x13 -> Option A

4. Quick Check:

   Output size = 13x13 with valid padding [OK]

Hint: Use formula: floor((input-filter)/stride)+1 for 'valid' padding [OK]

Common Mistakes:

• Using 'same' padding formula instead
• Forgetting to floor the division
• Adding padding when 'valid' means no padding

4. You wrote this code but get an error:

tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=2, padding='samee')

What is the error?

medium

A. Strides must be a tuple, not integer

B. Invalid padding value 'samee'

C. Kernel size must be a tuple

D. Filters must be a list

Solution

1. Step 1: Check padding parameter

   Padding accepts only 'valid' or 'same'. 'samee' is a typo and invalid.

2. Step 2: Verify other parameters

   Strides can be integer or tuple, kernel_size can be int or tuple, filters is integer. So no error there.

3. Final Answer:

   Invalid padding value 'samee' -> Option B

4. Quick Check:

   Padding typo causes error [OK]

Hint: Check spelling of padding values: 'same' or 'valid' only [OK]

Common Mistakes:

• Misspelling padding values
• Assuming strides must be tuple always
• Confusing kernel_size type requirements

5. You want to keep the output size same as input (28x28) after Conv2D with filter size 5x5. Which padding and stride should you use?

hard

A. padding='same', strides=(1, 1)

B. padding='valid', strides=(1, 1)

C. padding='same', strides=(2, 2)

D. padding='valid', strides=(2, 2)

Solution

1. Step 1: Understand output size preservation

   To keep output size same as input, use 'same' padding and stride 1 so filter slides over every pixel with padding added.

2. Step 2: Check options

   Only padding='same', strides=(1, 1) uses 'same' padding and stride (1,1), which preserves size.

3. Final Answer:

   padding='same', strides=(1, 1) -> Option A

4. Quick Check:

   Same padding + stride 1 keeps size [OK]

Hint: Use 'same' padding and stride 1 to keep output size [OK]

Common Mistakes:

• Using 'valid' padding which shrinks output
• Using stride > 1 which reduces output size
• Confusing filter size effect on output