Padding and stride in TensorFlow

Introduction

Padding and stride help control how a convolutional layer processes images. Padding adds space around the image edges, and stride controls how the filter moves over the image.

When you want to keep the output image size the same as the input size.

When you want to reduce the output size to make the model faster.

When you want to control how much the filter moves over the image to capture features.

When you want to avoid losing important edge information in images.

When designing convolutional neural networks for image tasks like recognition or detection.

Syntax

TensorFlow

tf.keras.layers.Conv2D(filters, kernel_size, strides=(stride_height, stride_width), padding='valid' or 'same')

padding='valid' means no padding; the filter only moves inside the image.

padding='same' means padding is added to keep output size same as input size.

Examples

Filter moves one pixel at a time, no padding added.

TensorFlow

Conv2D(32, (3, 3), strides=(1, 1), padding='valid')

Filter moves two pixels at a time, padding added to keep output size.

TensorFlow

Conv2D(32, (3, 3), strides=(2, 2), padding='same')

Filter size 5x5, stride 1, padding added to keep output size.

TensorFlow

Conv2D(64, (5, 5), strides=(1, 1), padding='same')

Sample Model

This code shows how padding changes the output size and values. The kernel weights are all ones to make it easy to see the sum of values under the filter.

TensorFlow

import tensorflow as tf
import numpy as np

# Create a simple 5x5 grayscale image with one channel
input_image = np.array([[[[1], [2], [3], [4], [5]],
                         [[6], [7], [8], [9], [10]],
                         [[11], [12], [13], [14], [15]],
                         [[16], [17], [18], [19], [20]],
                         [[21], [22], [23], [24], [25]]]], dtype=np.float32)

# Define a Conv2D layer with kernel 3x3, stride 1, padding 'valid'
conv_valid = tf.keras.layers.Conv2D(filters=1, kernel_size=(3,3), strides=(1,1), padding='valid', use_bias=False,
                                   kernel_initializer=tf.constant_initializer(1.0))

# Define a Conv2D layer with kernel 3x3, stride 1, padding 'same'
conv_same = tf.keras.layers.Conv2D(filters=1, kernel_size=(3,3), strides=(1,1), padding='same', use_bias=False,
                                  kernel_initializer=tf.constant_initializer(1.0))

# Apply convolution with 'valid' padding
output_valid = conv_valid(input_image)

# Apply convolution with 'same' padding
output_same = conv_same(input_image)

print('Output shape with valid padding:', output_valid.shape)
print('Output values with valid padding:\n', output_valid.numpy().squeeze())

print('Output shape with same padding:', output_same.shape)
print('Output values with same padding:\n', output_same.numpy().squeeze())

Important Notes

Stride larger than 1 reduces output size by skipping positions.

Padding='same' adds zeros around the input to keep output size equal to input size.

Using all ones kernel helps understand how convolution sums input values under the filter.

Summary

Padding controls if and how much space is added around input edges.

Stride controls how far the filter moves each step over the input.

Together, they affect output size and what features the model can learn.

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